IBS TIMES 240th ISSUE

Foundation of Efficient Market Hypothesis By: E Varun Kumar & Bandi Tejaswini

“In an efficient market, prices fully reflect all available information.”

- Eugene F. Fama, Father of the Efficient Market Hypothesis The idea behind market efficiency feels modern, but it actually started long before finance became a serious academic subject.

In the early 1900s, a French mathema cian named Louis Bachelier no ced something unusual: stock prices didn’t move in clean pa erns the way people assumed. They bounced around in ways that looked random because new informa on kept appearing out of nowhere. His work didn’t make a big splash then, mainly because finance wasn’t seen as a scien fic field. For many years, investors s ll believed the best way to make money was through ins nct, experience, or ps. When Eugene Fama took over in the 1960s and 1970s, things changed.

His 1970 paper was a game-changer.

According to Fama, markets work like massive informa on engines, with thousands of investors responding simultaneously to news. Because people react as soon as they learn something new, prices don't fluctuate by accident.

Finance became more data-driven as a result of this change.

Instead of using narra ves or gut feelings, researchers began to depend more on models, sta s cs, and actual market data. One of the cornerstones of contemporary finance theory was once a straigh orward discovery made by a French mathema cian.

When Fama described an efficient market as one where prices “fully reflect available informa on,” he wasn’t trying to complicate things. He was simply saying that markets behave like a giant network of people reac ng to news. Whenever something becomes public earnings, economic updates, global events investors react almost immediately, and their buying or selling pushes prices to a new level. The big idea is that the price you see now already includes the informa on everyone else also knows. That's why predic ng the market or picking “special” stocks becomes so difficult. If the informa on is public, it’s already priced in.

For normal investors, this challenges the belief that research alone can consistently deliver market-bea ng returns. For many people, this challenges the tradi onal belief that picking stocks, following ps, or relying on personal research can guarantee superior results. EMH doesn’t say that markets are perfect. Instead, it suggests that prices usually reflect the best available understanding of what the informa on implies. What makes Fama’s idea even more interes ng is that it shi s the focus from trying to predict prices to understanding why prices move the way they do. In an efficient market, price changes don’t happen slowly or in predictable steps they jump because news itself arrives unexpectedly. When informa on is unpredictable, price movements become unpredictable too. In simple terms, the introduc on to EMH tells us this: markets are fast, compe ve, and constantly adjus ng. Anyone hoping to outperform them must find informa on that others don’t have, or interpret public informa on be er and faster than millions of other investors who are trying to do the exact same thing.

Why Do Markets Absorb Informa on So Quickly?

Markets move fast because so many people are compe ng at the same me. Every investor wants to be early, because being late usually means missing the chance to profit. The moment something important becomes known, investors react to it.

Technology speeds things up even more. News travels instantly through apps, trading pla orms, and financial feeds. Algorithms read headlines faster than humans and make trades in milliseconds. There is no hesita on, they simply follow their programmed logic. The third thing is arbitrage, which basically acts like a price-fixing mechanism. If something's mispriced say a stock's too cheap compared to what it should be professional traders immediately buy the undervalued stuff or sell the overvalued stuff. They keep doing this un l the price corrects itself. It is like a self-healing system that keeps prices lined up with what informa on says they should be and finally, there's just basic human (and now machine) mo va on: everyone wants to make money. This desire generates a very strong incen ve to respond quickly to new informa on. Over me, all this compe ve behaviour creates a system where wrong prices get fixed almost automa cally.

Machine learning's only made this faster big ins tu onal investor can spot pa erns and signals way quicker than humans ever managed. Bo om line? Markets reflect the best available informa on because millions of people and machines are constantly working to make sure, they do. It's not magic; it's just intense compe on and modern technology doing their thing.

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Why fight it if you probably can't win? Also, fintech innova on's kind of democra zed everything. Small retail investors now have access to tools that used to be only for big ins tu ons technical analysis so ware, real- me data feeds, automated trading pla orms. This levels things out and actually reinforces how efficient modern markets are.

Effects

Key Concepts Market efficiency is really about how quickly and fairly informa on spreads. When news becomes public, prices shi so fast that no one can repeatedly profit from the same informa on. Because future informa on is unpredictable, price movements become unpredictable as well. This is why economists describe price changes as following a “random walk.” Yesterday’s movement doesn’t guarantee anything about tomorrow. EMH is usually grouped into three forms. The weak form says past trading data is already reflected in today’s price, which means old charts alone won’t give anyone a reliable advantage. The semi-strong form goes one step further and claims that all informa on reports, announcements, and general news that are accessible to the public are already priced in. Although few people think markets are really that strong, the strong form goes the furthest by believing that even private knowledge affects pricing.

EMH's influence goes much beyond theory. It has influenced market regula on, investment prac ces, and even the crea on of financial products. When markets were already effec vely pricing informa on, many investors started to wonder if ac ve stock selec on was worth the me and expense. Index funds and passive por olios which merely follow benchmarks rather than a emp ng to outperform them grew as a result. EMH became a major topic in academic circles, altering the way that finance was taught and researched. It even inspired behavioural finance, which focuses on the quirks and psychological biases that EMH does not account for. Regulators also responded by placing more importance on transparency, mely repor ng, and equal access to informa on. If markets rely on informa on to stay efficient, then ensuring that informa on is accurate and available becomes crucial. Lower fees, clearer disclosures, and be er market systems all grew from the recogni on that fair informa on flow benefits everyone.

Effect on the Na onal Stock Exchange

Despite their varia ons, all three approaches imply that con nuously outperforming the market is quite difficult, par cularly when trading costs, taxes, and fees are taken into account. The hypothesis is predicated on a number of factors, including a large number of logical investors, rapidly disseminated informa on, and low enough costs to allow arbitrage to fix mispricing. When these prerequisites are sa sfied, markets exhibit efficient behaviour.

How Is Efficient Market Hypothesis Relevant to the Modern Market?

Today's financial world is faster, more digital, and way more connected than when EMH first came out, but the theory is s ll relevant. Maybe even more so now. Algorithmic trading and AI dominate everything markets process news in milliseconds now. Prices adjust quicker than any human trader could possibly react. Informa on moves in real me through Bloomberg, Reuters, social media. News spreads globally almost instantly.

Even crypto markets show EMH principles at work, though they react to public informa on but s ll have tons of specula on happening, so they're only par ally efficient compared to tradi onal stock markets. Here's something interes ng: markets basically operate 24/7 now. Global crypto exchanges, digital trading pla orms they never stop. This constant ac vity backs up EMH's idea that markets never really rest. They're con nuously taking in informa on and adjus ng. Something happens in the U.S., and boom, it affects markets in Europe and Asia right away. Shows just how interconnected everything's become. All this efficiency has actually driven massive growth in passive inves ng. Since bea ng the market is so difficult, more and more people just buy index funds that track overall market performance instead of trying to outperform it.

India’s markets demonstrate many signs of growing efficiency, especially with the rapid shi to digital trading. Pla orms like Zerodha, Groww, and Upstox have given millions of retail investors immediate access to market data and analy cal tools that were once considered advanced. The NSE has built a reputa on for fast and reliable trading technology, reducing the me between order placement and execu on. This kind of speed helps prices reflect informa on more accurately. India’s expanding technology sector also plays a role.

Companies like Infosys, Wipro, and TCS work with blockchain and digital systems that influence both Indian and global financial ecosystems. Because of this, Indian markets are more connected to worldwide sen ment than ever before.

Events in the U.S. or major moves in crypto markets o en influence trading behaviour in India within minutes. The result is an environment where the NSE interacts constantly with global markets, mirroring the interconnectedness that EMH describes.

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Conclusion EMH offers a useful lens for understanding the way markets move. Its main message is straigh orward: prices reflect informa on because investors react quickly and compe vely.

This makes it extremely hard for anyone to consistently outperform the market. But markets are s ll human at their core. Emo ons, rumours, fear, excitement, and overconfidence influence decisions every day.

In Connec on with Digital Trading

Bubbles form, downturns happen, and some mes people behave in ways that make li le logical sense. EMH gives us one side of the story, while behavioural finance provides the other.

Real markets live in between. For regular investors, the key is to stay pa ent, stay informed, and avoid chasing short-term noise.

Efficient markets perform best when informa on is shared openly and when par cipants act with a long-term view. Even though markets aim for efficiency, they s ll reflect the human behaviour behind every decision.

The market's ming has been en rely altered by digital trading.

The majority of deals now take place online in a ma er of seconds rather than over the phone or at physical terminals.

Algorithms and automated systems manage the majority of these trades; humans are no longer even involved.

As a result, markets respond to world events rela vely instantly.

When an unforeseen event occurs in one region of the world, it quickly spreads to other markets. Consider the abrupt increase in the price of Bitcoin. Automated systems begin modifying their posi ons before a human trader has even finished reading the news. The focus quickly shi s because many of these algorithms are linked to industries that follow cryptocurrency developments, such as technology or blockchain-related businesses.

While digital trading increases the efficiency of the market by accelera ng the process of price discovery and increasing the pervasiveness of informa on, it is also likely to simultaneously create new types of market anomalies. High-frequency and algorithm-based trading strategies could amplify short-term vola lity due to overreac ons or flash crashes, which deviate from fundamental values. In such a situa on, temporary devia ons in prices from intrinsic value result in inefficiencies that seem incongruent with the Efficient Market Hypothesis.

Thus, even though digital trading can theore cally enhance semi-strong market efficiency, simultaneously it creates structural and behavioural anomalies due to speed, automa on, and algorithmic feedback mechanisms.

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Random Walk

Theory By: Vijay Venkatesh B & Barnima Paul The Random Walk Theory is based on the idea that stock prices change independently and unpredictably. You cannot predict future prices by looking at past trends or data because each price change comes from new and unexpected information entering the market.

This concept was first introduced by Louis Bachelier, a French mathematician, in his 1900 doctoral thesis titled “The Theory of Speculation.” Bachelier was the first to use mathematical models of probability to analyse changes in stock prices.

According to his research, price fluctuations exhibit a "random walk" pattern, in which each movement is independent of the ones that came before it. This pattern is comparable to the erratic movements of an inebriated person who is unable to predict his next course of action.

Later, in the 1960s and 1970s, the concept was popularized by economists like Paul Samuelson and Burton G. Malkiel. Malkiel’s famous book, “A Random Walk Down Wall Street” (1973), brought this theory into mainstream inves ng. He argued that since prices move randomly, no investor can consistently outperform the market by using past data or stock-picking strategies. In simple words, the Random Walk Theory means that: “Tomorrow’s price is independent of today’s — it’s like the steps of a drunk person, unpredictable and direc onless.”

Connec on Between Random Walk Theory and the Efficient Market Hypothesis (EMH) The Efficient Market Hypothesis and Random Walk Theory basically explain the same thing from different angles. EMH says that prices already factor in everything we know past data, public news, even insider info, depending on which version you're talking about. So, the only thing that can move prices is brand new information nobody's seen yet. But here's the thing: new information is completely unpredictable. You can't know when the next earnings surprise will drop or when a CEO will suddenly resign.

Since surprises are random, price movements end up being random too that's where Random Walk comes in. Random Walk just describes what you actually see on a price chart when markets are efficient: jagged, erratic movements with no clear pattern.

Yesterday's price tells you nothing useful about tomorrow's because today's price already reflects everything known up to this moment. If you graph this, it looks messy and scattered, not smooth or cyclical. That visual chaos is exactly what you'd expect if information hits the market unpredictably and gets absorbed instantly.

So EMH explains why markets should be unpredictable, and Random Walk shows what that unpredictability looks like in actual price data. They're two sides of the same coin if one's true, the other has to be true too.

Evidence from Indian Financial Markets Ni y 50 (India's Stock Market) Take the Ni y 50 during COVID as a perfect example of how markets just don't follow predictable pa erns. When the pandemic hit in March 2020 and India went into lockdown, the index tanked from about 12,000 to under 7,500 in weeks. That's a 40% wipeout, and nobody saw it coming based on charts or price pa erns it was pure shock from lockdown announcements nobody expected.

Then came the weird part. Even though nobody knew how long COVID would last or how bad things would get, markets started climbing back up by April. Within eighteen months, by September 2021, Ni y didn't just recover it shot past 18,000, hi ng record highs. Why? Vaccine news broke, governments threw money at the problem, companies adapted faster than expected, and central banks flooded the system with cash. None of this was visible in the March crash numbers. You couldn't have called this recovery by looking at any graph or trend line. Each piece of news moved the market in ways technical analysis never predicted.

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Bitcoin (Cryptocurrency Market) Bitcoin's wild price swings might be the clearest proof that markets move randomly. Since it trades round the clock with no breaks, any news can slam prices instantly, and that's exactly what keeps happening. Look at December 2017 when Bitcoin hit nearly $20,000. Everyone was hyped, retail investors were piling in, and technical traders were drawing bullish pa erns everywhere. Then it collapsed to $3,500 by December 2018 an 82% wipeout.

Charts didn't warn anyone about that. It crashed because sen ment flipped, regulators started making noise, and the bubble popped. A er that, Bitcoin just dri ed between $6,000 and $10,000 for almost two years with no clear direc on. Then out of nowhere in late 2020, it rocketed to $64,000 by April 2021. What changed?

Dow Jones Industrial Average (U.S. Stock Market) The Dow Jones tells the same story, just over a much longer meline. Go back to September 2008 when Lehman Brothers went bust. The Dow dropped from 11,400 to 8,600 in two weeks nobody's chart predicted a bank that size would actually be allowed to fail. Credit markets froze, banks started toppling like dominoes, and panic took over. But here's where it gets interes ng for the random walk idea: the recovery ming made no sense from a technical standpoint. Markets kept falling ll March 2009, bo oming at 6,547.

Then, suddenly, they reversed and went on an eleven-year tear up to 29,000 by early 2020. People trying to catch the bo om using pa erns mostly got burned because there was no signal the turn happened when the Fed stepped in big, s mulus kicked in, and data started improving. Fast forward to March 2020, and we saw the fastest crash ever from 29,551 to 18,592 in just 23 trading sessions because of COVID fears.

Then came another curveball: a V-shaped bounce that caught everyone off guard. By November, Dow was back at record highs thanks to surprise vaccine announcements and another round of massive s mulus. No chart reader called that recovery shape or ming.

Tesla announced they'd bought billions in Bitcoin, MicroStrategy kept stacking, PayPal enabled crypto payments, and people started freaking out about infla on. None of these corporate moves or policy shi s showed up in price charts beforehand. Each announcement was a surprise that jolted prices in ways no pa ern could've predicted.

Behavioural Change: When Emo on and Randomness Combine Now here's where things get messy. The Random Walk Theory assumes people are ra onal, but we're clearly not. Daniel Kahneman and Amos Tversky spent decades proving that humans make systema c mental errors when dealing with money and risk. We panic when we shouldn't, chase winners, freeze up at the wrong mes all that happens predictably enough to show up in market behaviour.

So, if people are predictably irra onal, shouldn't that create pa erns smart investors can exploit? If everyone overreacts to bad news or follows the crowd, can't you make money be ng against that? This creates a real puzzle. Either markets are efficient random walks where prices reflect all available informa on instantly, or our behavioural quirks create temporary inefficiencies that savvy traders can profit from. It's one of the biggest ongoing arguments in finance, and the answer probably lies somewhere in between.

The Adani-Hindenburg Crisis: A Real-World Case Study in Behavioural Finance The Adani Group meltdown in January 2023 shows exactly how behavioural biases play out in real me. Adani stocks had been on an insane run some had gone up ten mes between 2020 and 2023. This created what psychologists call a "halo effect" where success in one area makes people assume everything else must be golden too.

Gautam Adani was climbing wealth rankings, the group kept winning big infrastructure deals, and investors just assumed the whole empire was rock solid. Then Hindenburg Research dropped a 106-page bombshell on January 24, 2023, alleging stock manipula on, accoun ng fraud, and all sorts of shady dealings. This was textbook unpredictable informa on the kind Random Walk Theory says should move prices because nobody saw it coming. But how the market reacted tells us a lot about whether people process informa on ra onally or emo onally.

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Technical analysts will tell you certain chart pa erns actually work some mes. Support and resistance levels do seem to influence prices, partly because so many traders watch the same levels and create self-fulfilling prophecies when they all buy or sell at those points. Moving average crossovers have shown some sta s cal edge in par cular markets and meframes. And let's not ignore the quan ta ve hedge funds using machine learning and pa ern recogni on some of these guys have made consistent money for years, which suggests there are subtle pa erns hiding in the noise, even if you need massive compu ng power to find them. Fundamental analysis also seems to add value in some cases.

Panic took over immediately. Adani Enterprises crashed from ₹3,442 on January 24 to ₹1,565 by February 2 that's 54% gone in nine days. Adani Ports dropped 39% from ₹761 to ₹462.

Adani Green tanked 45% from ₹1,916 to ₹1,040 in the same window. These weren't measured, calculated moves. This was fear driving the bus.

Some of that selling was ra onal people reassessing valua ons based on new fraud allega ons. But a lot of it was pure emo onal overreac on, investors dumping shares just because everyone else was dumping shares. The speed and severity of the crash showed both things happening at once: markets responding to genuine new informa on like the theory predicts, but also amplifying that response through behavioural biases like herding and panic.

It's a perfect example of how real markets don't fit neatly into either the "perfectly efficient" box or the "totally irra onal" box they're somewhere in between, driven by both informa on and emo on.

Random Walk Theory vs Predictable Market There's plenty of real-world evidence backing up Random Walk Theory. The biggest one? Most professional fund managers can't beat the market year a er year. Academic studies going back decades show that the majority of ac vely managed mutual funds underperform their benchmark indexes, especially once you subtract fees. If markets had predictable pa erns, these pros with their Bloomberg terminals, research teams, and fancy algorithms should crush it consistently. But they don't. Random Walk Theory also explains why hot fund managers cool off. A guy who beats the S&P 500 three years straight might just be lucky, like someone flipping heads five mes in a row.

If you dig deep into financial statements, study compe ve advantages, assess management teams, and understand industry dynamics, you can lt the odds in your favour. It doesn't guarantee anything plenty of "fundamentally sound" companies s ll tank but careful research does seem to improve your probability of picking winners. So maybe markets aren't perfectly random; maybe they're just random enough that bea ng them consistently is incredibly hard but not completely impossible for those with the right tools and discipline.

Conclusion One of the most significant discoveries in modern finance is the rela onship between the Random Walk Theory and the Efficient Market Hypothesis. While Random Walk Theory explains how this efficiency appears through unpredictable price movements, EMH explains why markets are efficient because informa on is rapidly absorbed.

Together, these ideas shaped the rise of index funds, ETFs, and passive inves ng, redefining how both retail and ins tu onal investors approach the markets. Both theories are evident in the Indian market: prices react quickly to new informa on (efficiency) but then fluctuate inconsistently (randomness). The wisdom of pa ence, diversifica on, and discipline in inves ng is reinforced by the fact that, despite behavioural factors and short-term inefficiencies, randomness eventually takes over.

In the end, these theories serve as a reminder that the market is a system to be learned rather than a mystery to be solved, one in which pa ence, discipline, and knowledge are far more valuable than forecas ng.

Sta s cal tests show that past fund performance barely predicts future performance it's nearly worthless as a signal.

Even legendary investors are so rare they might just be the extreme tail end of a luck distribu on. Warren Buffe himself has said most people should just buy index funds instead of trying to pick stocks or me the market. If the greatest investor alive is telling you not to bother, that says something about how hard it is to beat randomness. But hold on there are some cracks in the pure randomness story.

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Three forms of Market Efficiency

In truly efficient markets, no investor can reliably beat the market using informa on or trading strategies. Eugene Fama’s framework breaks down market efficiency into three Forms: weak, semi-strong, and strong. These forms differ based on the type of informa on that is already reflected in prices.

By: Nithish Raj S R

Weak form

Market efficiency describes how well financial markets absorb and reflect all the informa on available about various assets. In an efficient market, prices adjust so quickly to new data that no investor can consistently beat the market without taking on extra risk. This principle, known as the Efficient Market Hypothesis (EMH) and introduced by economist Eugene Fama, suggests that asset prices already capture everything the past trends, public reports, and, in its strongest form, even private insider knowledge. In simple terms, by the me any piece of news reaches an investor, the market has likely reacted to it already.

In the weak-form of the Efficient Market Hypothesis, prices of the security fully reflect all past market informa on, such as previous prices and trading volumes, is already included in current security prices. This implies that investors cannot consistently forecast future prices or earn abnormal returns simply by analysing historical pa erns or trends.

For example, suppose an investor studies past price charts and no ces that a stock has risen for five consecu ve days. Based on this trend, the investor expects the stock to increase again tomorrow and buys shares. In a weak-form efficient market, today’s price already reflects all past price movements, so the stock’s future movement is unpredictable and may not con nue the trend. As a result, using past trading data to forecast future prices does not reliably lead to extra profits for the investor When the US Fed announced interest rate increase, Ni y 50 jumped within seconds, not gradually. The past trends like charts, moving averages could not predict this movement only new informa on i.e., the announcement made had moved the price. This shows that one cannot make profit using the past data which proves the weak form of market efficiency.

Efficient markets promote transparency, fair valua on, and a sense of trust among investors, helping capital flow to businesses that can use it best. S ll, no market is perfect.

Emo ons, specula on, and delays in how informa on spreads can cause short-lived inefficiencies. Despite these occasional gaps, the idea of market efficiency remains a fundamental pillar of modern finance, shaping how investors look at value, risk, and poten al returns.

To test weak-form efficiency, researchers examine whether past returns show any predictable rela onship with future returns.

Although slight correla ons are some mes found, they are usually too minor to yield profits a er accoun ng for trading costs. Another way to test this form of efficiency is by evalua ng specific trading strategies that depend on historical data.

This image explains market efficiency and shows that financial markets set prices to reflect all available informa on about assets. Because of this, traders have equal opportuni es to trade and profit, minimizing extra costs and unfair advantages.

Markets are structured to make it hard for anyone to consistently outperform others through predic ng prices.

If a strategy can regularly generate higher returns a er adjus ng for risk and costs, it would contradict the weak-form hypothesis. Such approaches are linked to technical analysis, where analysts (o en called technicians) study price and volume data to find pa erns that might help predict future movements. Technicians o en argue that investor psychology plays a major role in price movements, and they try to forecast how traders will behave based on past behaviour.

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However, because an infinite number of possible trading rules exist and many rely on subjec ve judgment, it is difficult to defini vely prove or disprove weak-form inefficiency.

Research indicates that technical analysts are generally unable to achieve consistent abnormal returns by relying on historical price movements or technical trading strategies in developed markets. However, studies show that in some developing markets such as Hungary, Bangladesh, and Turkey technical analysis may s ll offer profitable opportuni es Weak form market efficiency has its own shortcomings. If you’re a trader relying on chart pa erns or past price movements to make predic ons, you’ll o en find that these strategies simply don’t deliver steady profits because all of that historical info is already built into today’s prices. This theory also takes no no ce of how investors feelings and biases can occasionally create short-lived opportuni es, so it doesn’t capture every market quirk. Plus, it assumes everyone reacts to new informa on instantly and equally, which just isn’t true in real life some folks spot and act on news much faster than others.

Semi-Strong Form In a semi-strong form efficient market, all publicly available informa on is already built into stock prices. This means details from financial statements like earnings, dividends, or changes in management as well as market data such as trading volumes and past prices, are already reflected in the current price.

Because it includes everything from the weak form, a semistrong efficient market also assumes that price trends and past informa on have no predic ve power. For example, if a company announces a new product a er the market has closed.

As soon as the market opens the next day, the stock price immediately jumps to reflect the posi ve news, even before investors have had a chance to analyse the details fully. This quick adjustment shows that the market has already incorporated all public informa on regarding the announcement. Any a empt to earn extra profits by analysing this news a er the fact would likely be unsuccessful because the market already reflected the new informa on instantly.

The moment the mega merger of HDFC ltd and HDFC Bank into HDFC Bank announced, the market digested public news instantly because of which the stock prices of both HDFC and HDFC Bank jumped immediately, not a er hours or days. Here the traders or investors were not given enough me to react and beat the market which proves the Semi-strong form of market efficiency.

In such a market, analysing public informa on to find undervalued or overvalued stocks doesn’t give investors any real advantage. Prices adjust almost instantly when new informa on becomes available. For example, if a company reports be er-than-expected earnings, its stock price would quickly rise to reflect the news, leaving li le room for investors to earn abnormal returns from that announcement. Since all investors have equal access to public informa on, no one can consistently outperform the market just by studying it. An event study helps shed light on how investors respond to new informa on when it becomes available. For example, the researcher wants to assess how the market reacts when a company issues an announcement of a special dividend. The first step of the process is to choose a period.

Then, the researcher screens all companies that made such announcements in the period, including exact announcement dates, yields, and announcement of ex-dates. Then for each company’s stock, the researcher es mates what the return should have been on the event date using models such as the Capital Asset Pricing Model (CAPM), a basic market model, or general market index returns. Then they find the excess return, which is simply the difference between the actual return and the es mated expected return.

A er calcula ng these excess returns for all the companies studied, the researcher performs sta s cal tests to check whether these abnormal returns are significantly different from zero, helping determine if the event had a meaningful impact on stock prices.

How do event studies related to efficient markets? In semistrong efficient market, stock prices change quickly and accurately as soon as public news is released. So, if the news is good that a company reported be er-than-expected earnings, you’d see its share price jump right away. If the news is bad, the price would drop just as fast.

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If the actual stock returns are higher or lower than expected only during the announcement, it means the market is quickly reac ng to the news which is exactly what we’d expect in an efficient market. This quick reac on doesn’t mean the market is inefficient. But if stocks keep gaining or losing value a er the announcement, that could mean there’s a chance to make extra profits, showing trading opportunity. Generally, though, once news is public, trying to beat the market based on that announcement usually won’t lead to big gains. In many different company specific events including stock splits, dividend changes, and merger announcements, as well as economy-wide events, such as regula on changes and tax rate changes have been examined. Overall, most research supports that developed markets tend to follow semi-strong efficiency, while developing markets some mes show signs of slower or less complete informa on absorp on.

Semi-Strong form assumes all publicly available informa on is instantly and perfectly reflected in prices, but in reality, it has some limita on, processing complex financial disclosures is slow and costly for most investors. While it rules out using fundamental analysis for consistent profits, studies have shown that some professional fund managers do occasionally outperform the market, sugges ng semi-strong efficiency can be violated.

The theory only considers public data and ignores private, insider informa on that could influence prices before public release, so prices may not always reflect the full picture. All investors do not act ra onally or interpret news in the same way, which can lead to short-term mispricing and market anomalies even a er public announcements. These limita ons mean that although markets are quick to react to news, realworld fric ons and human factors prevent perfect efficiency.

Essen ally, strong-form efficiency represents the most extreme level of market efficiency it implies that prices fully and instantly adjust to every piece of informa on, regardless of how it becomes available. Furthermore, because strong-form efficiency includes all other forms of informa on, it automa cally sa sfies the condi ons of both weak-form and semi-strong-form efficiency. That means price trends, as well as public announcements, are already incorporated into market prices. However, in reality, achieving strong-form efficiency is nearly impossible. Most countries have strict laws against insider trading, which prevent individuals with private or confiden al informa on from legally using it for financial gain.

Due to these legal restric ons and the prac cal limits of how fast informa on spreads, markets are rarely, if ever, perfectly strong-form efficient. The strong form of market efficiency has some limita ons. It assumes all informa on that are public and private is instantly reflected in stock prices, but in reality, insider informa on is o en restricted by laws, so it’s not always factored into prices immediately.

Strong Form In a strong-form efficient market, all informa on that could influence a stock’s price both public and private is already reflected in the market price. Public informa on includes things like financial reports, dividend announcements, or market trends that any investor can access. Private informa on, on the other hand, refers to insider knowledge known only to company execu ves, managers, or other insiders, such as upcoming mergers, undisclosed financial results, or new product launches. Example of strong-form market efficiency can be seen in how insider trading laws work. For instance, if a company execu ve who learns about a confiden al merger plan may want to buy shares before the news becomes public. However, in a strong-form efficient market, this insider informa on is already reflected in the stock price, making it impossible for the execu ve to gain abnormal profits.

Regulatory bodies like the SEC ac vely prevent such insider trading to maintain market fairness, suppor ng the idea that no one can consistently benefit from private informa on. Another example is the performance of hedge funds, which o en have access to proprietary data and sophis cated models but typically fail to outperform simple index funds over the long term, sugges ng strong-form efficiency in prac ce.

If a market is truly strong-form efficient, it means that even private or insider informa on is already priced into securi es.

As a result, no investor, not even company insiders, can consistently earn abnormal or excess returns by trading on this informa on.

This makes the strong form to be unrealis c because insider trading laws prevent the free flow of private informa on. Also, the theory suggests that one cannot outperform the market, yet we see cases where some investors do beat the market, showing it doesn’t hold perfectly. Relying on this theory can some mes lead to ignoring market bubbles or crashes because it assumes prices always reflect true value, which is not always the case.

In summary, while the strong-form efficiency theory suggests that no one can gain an advantage in the market even with insider knowledge real-world condi ons make such perfect efficiency unrealis c. Developed markets o en come close to semi-strong efficiency, but strong-form efficiency remains more of an ideal rather than a prac cal reality.

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Impact on Technical & Fundamental Analysis By- Toyesh & Ria Siby When trying to make smart investment decisions, investors look at the market in one of two ways: fundamental analysis or technical analysis. Fundamental analysis is a lot like ge ng to know someone you’re thinking of trus ng with something important. Evalua ng a company is similar to forming an opinion about someone you just met. You would not choose to believe someone just because they appear confident or speak effec vely. Instead, you would like to understand their longterm behaviour on how they handle responsibility, if they keep promises and how consistent and dependable they are.

Fundamental analysis follows the same principle. Rather than relying on market hype or short-term price fluctua ons, it considers a company's financial soundness, management quality, earnings consistency, and long-term growth prospects.

Trust in a person grows via observable behaviour, and confidence in an investment is established by understanding the underlying fundamentals that drive long-term wealth growth.

Fear leaves a mark. Greed leaves a mark. Hesita on, confidence, panic they all leave footprints on charts. Technical analysis is simply the art of no cing those footprints before they fade.And even though fundamental and technical analysis seem like they come from two different universes, they actually fit together in such a natural way. Fundamental analysis helps you figure out what is worth inves ng in the strong companies with real depth and longterm promise. Technical analysis tells you when to step in, when the ming feels right, and when the market is signalling cau on.

This is why seasoned investors rarely choose just one approach.

They blend the two like a pair of tools meant to work side by side. One helps you understand the heart and soul of a company, and the other helps you understand the pulse of the market.

That’s exactly what fundamental analysis feels like. You’re not just staring at a stock price you’re trying to understand the company’s personality. You look at how it makes money, how wisely it spends, whether it’s strong enough to survive tough mes, and if the people leading it genuinely know what they’re doing. Fundamental analysis slows you down in the best way. It makes you look beyond the noise and see the company for what it really is. And every now and then, when you look deeply enough, you come across a company the world hasn’t fully no ced yet a quiet, underrated one with real poten al. Finding that kind of hidden strength feels incredibly rewarding.

Technical analysis, meanwhile, has a completely different vibe.

It’s like going to the beach and simply watching the waves roll in. You're not worried about the underwater currents or what caused the waves. You’re just watching the rhythm the rises, the dips, the repeated pa erns.That’s what technical analysts do.

Instead of reading balance sheets or leadership reports, they read charts. They look at price movements the way someone reads a story wri en in waves. Every spike, every dip, every pa ern reveals something about how people in the market are feeling hopeful, scared, excited, impulsive. Because no ma er how big the financial world seems, it runs en rely on human emo on.

Together, they make inves ng feel less like guesswork and more like a though ul, steady journey, one where you see both the inner truth of a business and the outer behaviour of the crowd.

And when those two perspec ves come together, your decisions become clearer, calmer, and far more grounded.

Linking EMH with Technical and Fundamental Approaches The Efficient Market Hypothesis (EMH) explains how news affects the stock market. This idea says that the market changes almost immediately whenever new informa on comes out.

Prices change so quickly that they show the latest news almost right away. This idea is directly related to both technical and fundamental analysis because these methods use informa on that EMH says is already in the prices. Knowing this connec on helps us understand why investors use different strategies and how markets work with informa on.

Technical analysis is interpre ng the historical data of the market such as price movement with trading volume and chart pa erns. Traders look for trends or repeatedly observed behaviors regarding price moves in the future. The weak form of EMH will say that all historical price informa on has been incorporated into current pricing by the market.

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Therefore, in that sense, in bringing the two together, relying on that past data is the basis. While EMH indeed maintains that this informa on is already absorbed by the market, technical analysis has an important prac cal func on. Technical traders improve ac vity in the market, bringing in further liquidity and correc ng small pricing anomalies, thus aiding the overall efficiency of the market.

Source: Investopedia In contrast, fundamental analysis evaluates whether a company's financial health and intrinsic value are likely derived from earnings reports or company performance, management quality, the industry dynamics, and some economic factors. The semistrong form of EMH states that all existent informa on not priced by the market directly is reflected.

Since that informa on is what fundamental analysts are using for analysis, EMH a aches to the premise that the market incorporates such data too rapidly for analysts to locate underpriced shares consistently. However, it maintains the market's efficiency. The ac vity of many analysts influencing financial informa on and investment decisions makes pricing move closer to the real value of a firm.

Infosys data from 2020-2026

Weak Form EMH and Its Challenge to Technical Analysis The weak form of the Efficient Market Hypothesis (EMH) argues that the stock market has already absorbed every piece of informa on related to a stock’s past. This includes how the stock’s price has moved over me, the trading volume on different days, how many shares were bought and sold, and all the chart pa erns that traders typically analyze. All of this historical data is already built into the stock’s current market price. Because of this, technical analysts—who rely heavily on studying charts, iden fying pa erns, and spo ng trends—face a major limita on. Their en re approach is based on the belief that past behavior can help predict future price movements.

But if the market has already processed and acted upon all this past informa on, then the charts themselves cannot provide any truly new insights that would help a trader gain an advantage. Essen ally, the market has already “used up” everything that technical analysts want to use.

In simple words, the weak form of EMH tells us that yesterday’s prices cannot reliably tell you what tomorrow’s prices will be.

Since the market automa cally reacts to every previous trend, signal, and price movement, technical analysis loses its predic ve strength. This also supports the idea that stock price movements follow something close to randomness. Any meaningful signal that could have been extracted from historical data has already been absorbed into the current price, leaving nothing extra for analysts to benefit from. Because of this, technical analysts cannot consistently outperform the market using past charts alone.

However, despite these limita ons, technical analysis s ll plays a significant and ac ve role in today’s financial markets. Millions of traders around the world con nue to use charts, pa erns, and indicators. Their par cipa on brings important benefits: they add liquidity, meaning there are always buyers and sellers in the market; they make trading ac vity smoother and more con nuous; and they help correct small temporary pricing errors through their frequent trades.

All of this contributes to making the market more efficient, even if the analysis itself cannot guarantee superior returns. So, while the weak form of EMH suggests that technical analysis cannot reliably be used to beat the market on a consistent basis, it does not make technical analysis irrelevant. Instead, it highlights that although traders may not gain an informa onal advantage, their presence and ac ons s ll help the market func on be er, stay more ac ve, and maintain healthy levels of liquidity and price correc on.

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Semi-Strong Form EMH and Its Challenge to Fundamental Analysis

Conclusion: Why EMH Limits the Effec veness of Technical and Fundamental Analysis

The semistrong form of the Efficient Market Hypothesis (EMH) states that the moment any type of public informa on—such as financial statements, quarterly earnings reports, company announcements, press releases, or industry updates—becomes available, the stock market reacts almost instantly. This new informa on is quickly absorbed and reflected in the stock’s price. As a result, fundamental analysis becomes much more challenging because it relies on the very same public informa on to determine whether a stock is undervalued or overvalued.

Fundamental and technical analysis help investors make sense of the stock market in two very different but complementary ways. Fundamental analysis is like ge ng to know a company on a deeper level its strengths, its financial health, and whether it has real poten al for the future. Technical analysis is more about reading the market’s mood by studying price movements and chart pa erns, almost like observing how people react in real me.

Since the market processes and adjusts to this informa on faster than analysts can read, interpret, and act on it, the possibility of earning extra or above average profits from analyzing public data becomes extremely limited. In simple terms, the semi strong form of EMH suggests that fundamental analysts cannot consistently outperform the market because any informa on they use has already been taken into account by the market. The price you see already reflects everything that is publicly known, leaving very li le room for gaining an advantage

Infosys Example: How EMH Challenges Both Types of Analysis

The Efficient Market Hypothesis (EMH), however, reminds us that the market is o en one step ahead. The weak form of EMH says that all past prices and trading data are already built into the current price, which makes it difficult for technical analysts to gain an edge using historical pa erns.

The semi-strong form of EMH adds that all public informa on earnings, announcements, news is absorbed by the market almost instantly. That means fundamental analysts o en find that the price has already adjusted before they can act on new informa on. S ll, both approaches remain valuable.

Fundamental analysis helps iden fy genuinely strong companies, while technical analysis helps understand ming and market behaviour. Even if EMH limits the chance of consistently bea ng the market, these tools guide smarter, more though ul investment decisions.

The Efficient Market Hypothesis (EMH) can be clearly understood through the example of Infosys. Under the weak form of EMH, the idea is that all past price movements and chart pa erns are already reflected in the current stock price.

For instance, even if a trader spots a pa ern like a “double top” in Infosys’ historical chart and expects the price to fall, the market has already absorbed this past informa on. So technical analysis does not give any special advantage because everyone sees the same charts, and the current price already includes all past data. Similarly, the semi strong form of EMH claims that all public informa on is immediately priced in.

A real example is when Infosys releases strong quarterly results: the stock o en jumps within seconds of the announcement. If a fundamental analyst reads the report later and decides the stock is undervalued, the price has already adjusted, leaving no room for extra profit. Through Infosys, we can see how EMH challenges both technical analysis (because past trends are already known) and fundamental analysis (because public informa on is quickly absorbed), making it difficult to consistently “outsmart” the market using informa on everyone already has.

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Efficiency: Theory vs.

Reality By- Shyam Sundar B & Teja Sai Reddy Market efficiency means financial markets adjust prices instantly when new informa on becomes available. This ensures that no investor can consistently earn above-average returns by exploi ng informa on. The Efficient Market Hypothesis (EHM) claims that stock prices always incorporate all per nent informa on, leading to no one being able to outsmart the market on a consistent basis by way of analysis and/or predic on.

Markets, however, never work perfectly. Investors consistently make economically beneficial decisions based off of emo ons, such as fear and excitement. An example of this was back in March 2020 during the ini al crash of COVID-19. Many investors sold their stocks out of fear, causing a drama c and irra onal decrease in the stocks, and the companies holding them did li le to no changes to their long-term fundamentals.

The Behavioural Cri que and MacroInefficiency The most direct a ack on EMH comes from behavioural finance, which a acks the perfect market’s presump on of investor ra onality. This area takes psychological biases into account in the decision-making process, and thus provides a viable reason for why devia ons from the fundamental can persist.

The Failure of the Ra onal Investor Another important result of behavioural finance is that investors are not always ra onal maximisers, but rather driven by emo ons, heuris cs and bias into trading decisions which are sub-op mal and produce observable market inefficiencies.

Here, Andrew Lo’s Adap ve Market Hypothesis (AMH) offers an explana on for these ac ons, posi ng that ‘irra onal’ human behaviour such as greed and fear can be understood through the lens of evolu on.

Transac on Costs and Behavioural Biases In an ideally efficient market, trading, and analy cal processes would reflect perfect ra onality and incur no costs. A ending to real-world func onality, however, markets evince weaknesses, such as transac on costs, which can hinder investor agility and mispricing exploita on, and which can include broker fees, bid-ask spreads, and taxa on. Furthermore, behavioural biases, such as overconfidence, herding, loss aversion, and emo onal rather than analy cal trading can skew the market as well.

The cryptocurrency boom and subsequent crash of 2022, for example, demonstrated how massive specula ve trading fuelled by overconfidence, loss aversion, and herding behaviour may distort prices far beyond their underlying value. Rapid retail par cipa on and momentum-based trading resulted in significant vola lity, while transac on costs and unexpected liquidity constraints hampered the poten al to make persistent anomalous profits.

Following this ini al crash, the market recovered quickly, just ra onal investors returned. This display of behaviour (in the markets) goes to show that while in theory, EHM func ons as intended. In prac ce, however, markets func on many mes in an inefficient manner. The truth about efficiency is much more interes ng, however.

In actual markets, including the Indian capital market, mechanisms like informa on asymmetry, investor sen ment, behavioural biases and market regula on etc., keep prices from immediately and perfectly adjus ng to true value. Quite o en, abrupt changes of policy, announcements on short no ce and specula ve trading will create mis-pricings, short-term fluctua ons and inefficiencies.

India’s stock exchanges the BSE Sensex and the NSE Ni y provide a good laboratory of such a theory. With technology and SEBI’s eagle eyeball, we s ll get pa erns like overreac ons to news, momentum inves ng and calendar effects. This tension between the utopian dream of perfect efficiency and the world we actually experience, however, is at the core of our explora ons of Market Efficiency: Theory vs. Reality.

Such examples demonstrate how real-world markets, despite their technological sophis ca on, frequently deviate drama cally from the outcomes predicted by theore cal efficiency models.

Markets Learn and Adjust Over Time (Adap ve Markets) Markets are not sta c; they change over me. A er every great failure such as a bubble or a crash investor take a lesson from the mistake and subsequently their conduct changes. The single concept behind the Adap ve Market Hypothesis (AMH), which views markets as living creatures that are capable of adjus ng to new circumstances, learning from previous ones, and developing new rules or incorpora ng new technologies, is that. Just as an illustra on, a er the 2008 crisis, banks turned more cau ous, regula ons became stricter, and investors more vigilant of the risk. Furthermore, adap ve behaviour is not restricted to investors but also to ins tu ons, market structures, and trading strategies. Financial technology, algorithmic trading, and data analy cs advancements are constantly reshaping how informa on is processed and acted on in markets. Strategies that once produced excess returns tend to lose efficacy as they become more generally used, causing market par cipants to innovate or change their tac cs.

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This evolu onary process confirms the no on that market efficiency is dynamic rather than absolute, arising and receding as par cipants learn, compete, and adapt to changing economic and regulatory

Empirical Studies The Efficient Market Hypothesis focuses on how ra onal investors are and how quickly they react to how efficient a market is. One of the first and most important studies that focused on stock prices and market predic on was done by Eugene Fama in the 1970s. Fama was the first to document that market prices moved in a zig-zag manner and that they are random and therefore unpredictable. He also argued that all the necessary informa on is always available in the market, emphasizing the idea that the market is always efficient.

Numerous studies, including Fama's, documented the idea of efficient market hypothesis resul ng to the random market theory and the idea of stock prices moving in a random manner.

This is an area where all investors are ra onal and the market is efficient, since informa on dissemina on is done quickly and trading occurs without delays.

How Technology and New Investors Changed the Market Markets today are nothing like they were in the past. Computer programs execute trades in milliseconds these days. Algorithms can respond quicker than any human. Individual investors can trade without any hassle through apps like Zerodha, Groww, and Robinhood. Stocks prices can be influenced within few minutes by the online community of users of social-media.

These changes have made markets quicker, more vola le, and at mes less predictable.

The 2021 GameStop event was a demonstra on of how online groups can pump up a stock price even if the company has no strong fundamentals. In the same way, algorithmic trading has been the cause of situa ons such as Flash Crashes, where prices plunge drama cally in a few seconds due to the fact that thousands of algorithms are responding simultaneously. In general, technology and new types of investors con nuously reshape the market, which is in line with the idea that markets are not perfectly efficient, they are always adap ng.

This illustra on shows how a modern stock market processes buy and sell orders. When an investor places an order through a trading app, it doesn’t go directly to the market. Instead, it flows through several automated layers: (• Trading App (UI) – where the order is placed • Network System – sends the order online within milliseconds • Exchange Servers – match the order with buyers or sellers • Post-Trade System – records the trade and updates the investor’s account)

This is also supported by Malkiel studies which documented that the majority of managed funds on a mutual basis do not outperform the market, meaning that managed funds could not predict the market. However, Malkiel goes on and states that even though the investors could not predict the market, the market is in fact efficient. Empirical research studies the market and concludes that the market is efficient.

Behavioural Insights and Adap ve Markets Behavioural finance tells us that investors are not always ra onal. They use mental shortcuts, their decisions are impacted by their emo ons, and most of the me, they do not follow the “perfectly logical” behaviour that is normally assumed by tradi onal finance.

As an illustra on, Prospect Theory indicates that people hate losing more than they like ge ng an equal amount of gain. As a result, investors are inclined to keep stocks that are losing for a longer me and sell those which are winning at a very early stage, even if it is irra onal.

Globaliza on

and

Inter-Market

Linkages

The interconnec on of the markets has been largely influenced by globaliza on. For instance, a situa on such as the raising of the US interest rates or the changing of the oil prices in the world can have an impact on the markets of a country like India within a very short period of me. The dissemina on of the news is facilitated by technology, which in turn leads to simultaneous reac ons by the markets worldwide.

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As a result, global efficiency is never at a fixed level but rather fluctuates due to the fact that panic or specula on, which can now spread as fast as the communica on of the informa on, are able to cause the occurrence of vola lity in different markets at the same me.

Conclusion The concept of market efficiency is s ll one of the most controversial ideas that has been discussed in finance.

Theore cally, prices should reflect all the available informa on, but prac cally, factors such as emo on, technology, and the global environment s ll have an impact on the markets.

It is true that markets nowadays are very fast and data-driven; however, they are s ll exposed to errors coming from human and algorithmic ac vi es. The existence of the retail traders, popularity of certain stocks through social media, and ESG inves ng are some of the factors that show market efficiency is always changing and not stable.

Instead of ques oning whether markets are efficient, it is a be er ques on to ask when and why they become inefficient.

With stricter regula on, more transparent informa on, and increased investor awareness, markets will have the capacity to correct themselves faster and thus come closer to being fair and stable. Real efficiency is not perfec on it is the capability to adapt and learn from each disrup on.

The Empirical Challenge: Ins tu onal Constraints Contrary to the Efficient Market Hypothesis, which posits that asset prices reflect the informa on available at the me, and lose the informa on relevance with the passage of me, market anomalies are devia ons from this theory. These anomalies comprise the size effect, where the returns of small companies bea ng the returns of larger companies in the same industry are evidenced in the Indian mid and small cap rim at the same me small cap companies out performed larger established firms like Reliance or HDFC during the mid-2014 to 2017. The value effect, where the lower growing stocks outperform the higher growing stocks also is evidenced in early recovering state public sector banks during the me when expensive technology stocks are available. Momentum effect is also evidenced that explains in the growing upward trend of stocks during the past period when stocks are gaining the returns of 20 to 30 percent.

The evidence of these consistent pa erns confirms the inefficiencies in the market that EMH face challenges with.

The Debate on Persistence The disputable nature of anomalies o en centres on their exploitability or their being merely sta s cal ar facts. A frequent problem is data snooping and selec on bias, in which zealous researchers tend to find “surprising” results by concentra ng on some samples un l one is found. A real inefficiency may be gauged by whether or not the anomaly con nues to hold in new, unseen samples.

Cri cs also contend that many of the so-called anomalies are not efficiency failures, but rather proxies for legi mate, unmodeled risk. The cases in which the returns in certain periods are higher, with the expecta on that the returns are associated with the higher liquidity risk or informa on asymmetry, and the investors are being rewarded for bearing risk rather than there being a failure in the market. This highlights the importance of using complex, mul -factor models rather than discarding the concept of efficiency.

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Behavioral Challenges to EMH By- Mehul Gupta & Raveena S Behavioural finance emerged because real investors repeatedly behaved differently from what tradi onal finance models expected. Classical theories assumed that people were ra onal, emo onless and always made decisions using logic. But in the real world, investors reacted to fear, rumours, news, losses and social expecta ons. Economists no ced that people made predictable mistakes panicking during crashes, becoming overly op mis c during rallies, following the crowd blindly, or refusing to sell losing stocks due to emo onal a achment.

These human pa erns could not be explained by the Efficient Market Hypothesis (EMH). As these behavioural inconsistencies grew more visible in markets, researchers such as Kahneman, Tversky and later Shiller realised that psychology must be integrated into finance. This realiza on gave birth to behavioural finance, a field that studies how human emo ons, biases and mental shortcuts shape financial decisions. Inves ng has never been a fixed or perfectly ra onal ac vity. Real investors do not behave like emo onless calculators; they are influenced by fear, excitement, regret, personal preferences, past experiences, and the limited informa on available to them in the moment. These emo ons and biases o en shape decisions more deeply than spreadsheets, valua ons or financial models. Because of this, inves ng rarely follows a single logical or predictable path.

Behavioural Finance as a Counterpoint to EMH Behavioural finance challenges EMH by grounding financial decisions in actual human ac ons rather than idealised ra onality. A major intellectual founda on of this field is Prospect Theory, introduced by Daniel Kahneman and Amos Tversky. Their research demonstrated that people fear losses far more intensely than they value equivalent gains. In real terms, losing ₹100 feels significantly worse than gaining ₹100 feels good.

This simple but powerful insight has far-reaching consequences.

For example, an investor holding a stock that has fallen significantly may refuse to sell it simply because accep ng the loss feels unbearable even when the company’s fundamentals have clearly deteriorated. Emo on overrides logic. The same investor might sell a rising stock too quickly, driven by anxiety that the gain could disappear at any moment. Instead of le ng profits grow, they close the posi on early to protect the emo onal sa sfac on of securing a win.

These behaviours are not consistent with the ra onal decisionmaking assumed by EMH. Behavioural finance does not argue that markets are dysfunc onal or collapsing; instead, it highlights that markets are human systems. Prices reflect not just informa on, but also the emo ons of millions of individual par cipants fear, greed, hope, regret and the desire for social valida on. In this sense, behavioural finance adds the missing human dimension to financial theory, explaining why markets deviate from efficiency despite widespread access to informa on.

Cogni ve Biases and Investor Irra onality Two leading experts, Eugene Fama and Robert Shiller, highlight this contrast sharply. Fama, the father of the Efficient Market Hypothesis (EMH), argues that markets are generally efficient and that prices reflect all available informa on. According to this view, investors behave ra onally, and prices quickly adjust to news or data. Shiller, on the other hand, challenges this idea by showing that human psychology, sen ment and collec ve emo on frequently distort prices. He demonstrates that markets can be driven by narra ves, excitement and fears just as strongly as by fundamentals. This contradic on reveals the core reality of financial markets: humans are irra onal, biased and emo onally driven, and these behavioural impulses play a central role in shaping market outcomes. They o en push prices away from theore cal efficiency and create pa erns that EMH alone cannot fully explain. Behavioural finance emerges as a response to this gap, offering insights into how real people think and behave when money is involved.

One of the strongest contribu ons of behavioural finance is its explana on of how cogni ve biases shape decision-making.

These mental shortcuts o en operate unconsciously, subtly influencing choices and crea ng a gap between what investors intend to do and what they actually do.

Overconfidence is one of the most common and destruc ve biases. Many investors genuinely believe they possess superior skill or market insight, even when their performance does not jus fy this confidence. This overes ma on of ability leads to excessive trading, ignoring risks and trus ng personal predic ons over data behaviours that o en result in lower returns than simply holding diversified investments. Loss aversion, rooted in Prospect Theory, leads investors to cling to losing posi ons much longer than is ra onal. The emo onal pain of acknowledging a mistake is so great that they prefer to wait, hoping for a rebound that may never come.

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Conversely, fear of losing accumulated gains leads them to sell winning stocks prematurely, preven ng wealth from compounding.

Market Anomalies: When Markets Behave Irra onally Market anomalies are pa erns in stock returns that should not exist in a perfectly efficient market, yet they appear repeatedly in real trading. For example, stocks that perform well tend to con nue rising in the short term known as momentum because investors react slowly to new informa on. Over longer periods, however, the opposite o en occurs, where previously losing stocks bounce back due to earlier overreac ons.

Seasonal pa erns such as the January effect, where small-cap stocks historically rise more in January, also contradict the idea of ra onal markets. These anomalies reveal that investor psychology, tax mo va ons and ins tu onal behaviour create predictable price distor ons that EMH cannot fully account for.

Herd behaviour is another powerful force. Investors o en follow the crowd, especially when markets appear to be booming or crashing. During euphoric markets, people buy simply because others are buying, crea ng self-reinforcing price surges. During downturns, they panic-sell because everyone else appears to be doing the same. This group psychology creates bubbles and crashes that EMH struggles to explain.

Confirma on bias further complicates ra onal decision-making.

Investors selec vely seek out informa on that supports what they already believe and ignore contradictory evidence. A person bullish on a par cular stock will focus only on posi ve news, dismissing risks or weaknesses.

Recency bias amplifies overreac ons. Investors place too much weight on recent performance, assuming that short-term trends will con nue indefinitely. If a stock has risen quickly, they assume it will keep rising; if it has fallen, they fear further decline even when long-term fundamentals remain stable.

Finally, familiarity bias leads investors to prefer companies they know, such as domes c giants or brands they use daily, simply because they feel comfortable with them. These choices are driven more by emo onal a achment than ra onal evalua on.

Together, these biases paint a clear picture: financial markets are not perfectly ra onal environments. They are shaped by psychological triggers, assump ons and emo onal impulses, which directly challenge EMH’s assump on of logical and informa on-driven decision-making.

Empirical Insights and Real-World Devia ons from EMH EMH assumes that when an asset is mispriced, ra onal investors will exploit this opportunity, and arbitrage will bring the price back to its true value. However, real-world markets rarely behave so neatly. Arbitrage is limited by structural, financial and psychological constraints.

Many markets impose restric ons on short-selling, making it difficult or impossible for investors to profit from overpriced securi es. Even when short-selling is allowed, it carries significant financial risk. Prices may remain irra onal longer than the investor can remain solvent, discouraging many from taking correc ve ac on.

Fund managers face addi onal pressures. If a manager bets against a bubble too early, even if their reasoning is correct, they may show losses in the short term. Investors might withdraw their money, leading to job loss or reputa onal damage. This career risk o en prevents ra onal traders from ac ng against the crowd.

Therefore, inefficiency persists not because investors are incapable of iden fying mispricing, but because real-world constraints prevent them from ac ng decisively. The presence of noise traders amplifies this problem. Noise traders act based on emo on, rumours, hype or incomplete informa on rather than fundamentals. When they move collec vely driven by social media, sensa onal news or community sen ment they can push prices further away from ra onal values.

This combina on of limited arbitrage and powerful emo onal trading allows market inefficiencies to survive far longer than classical financial theory predicts, making behavioural explana ons essen al to understanding market movements.

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The graph illustrates how noise traders, driven by biases and mispercep ons, can some mes achieve higher life me consump on than ra onal investors challenging the strict assump ons of EMH. It shows that human behaviour and irra onal beliefs can temporarily influence prices, crea ng pockets of mispricing that behavioural finance aims to explain.

Even when ra onal investors recognise that a stock is mispriced, they cannot always correct the error. Arbitrage involves risks that EMH overlooks. Short-selling can be expensive and risky, especially when markets move sharply against the ra onal investor. Funding constraints, margin calls and liquidity issues further discourage correc ve ac on.

The cryptocurrency market illustrates this vividly. Prices o en react more to online sen ment, influencer commentary and viral narra ves than to intrinsic value or technological merit.

Social media pla orms can ignite buying frenzies within minutes, while rumours or nega ve announcements can trigger immediate collapses. This hyper-reac ve environment demonstrates how emo onal impulses and social influence can temporarily overpower ra onal evalua on.

Reputa on risk also plays a role. A hedge fund manager who takes a contrarian posi on too early may appear incompetent if markets con nue moving irra onally. Investors may withdraw funds long before the posi on pays off. This forces many managers to follow the trend rather than challenge it, even when their analysis suggests mispricing.

Noise traders intensify this dynamic. Their behaviour is driven by hype, specula on, crowd psychology and emo onal reac ons. They buy because a stock is trending on social media or sell because of a viral rumour. When millions of such investors act together, they overpower ra onal pricing mechanisms and create vola lity that deters informed traders from intervening. Thus, irra onal prices can persist not because markets lack intelligence, but because they are dominated by emo on and constrained by structural limita ons.

Bridging Behavioural Finance and Market Efficiency Andrew Lo’s Adap ve Market Hypothesis (AMH) a empts to reconcile EMH and behavioural finance by proposing that market efficiency evolves based on changing condi ons.

Markets behave efficiently when par cipants act sensibly, informa on flows smoothly and incen ves are aligned.

However, during periods of uncertainty or disrup on such as technological shi s, regulatory changes or specula ve frenzies behavioural forces dominate.

Meme-stock rallies highlight how behavioural finance and EMH must coexist. These rallies occur not due to fundamentals, but due to online communi es, collec ve excitement and social momentum. Prices move because millions of investors emo onally coordinate their ac ons. While EMH explains longterm correc ons, behavioural finance explains why the shortterm market can deviate drama cally. Together, they show that markets evolve, influenced by both informa on and human psychology.

Meme-stock rallies func on similarly. Large online communi es encourage buying not because the company’s fundamentals have improved, but because par cipa on itself becomes a source of excitement and iden ty. These rallies push prices to extraordinary levels before sen ment shi s and the market corrects itself. Both these phenomena reveal how behaviour spreads faster today than at any point in financial history due to the speed of communica on. Crowd-driven ac vity creates feedback loops where rapid enthusiasm fuels further buying and fear triggers equally rapid sell-offs. Markets begin to resemble evolving ecosystems, constantly adjus ng to investor behaviour, learning from each cycle and responding dynamically rather than predictably.

Conclusion The rela onship between EMH and behavioural finance presents a fundamentally contradictory point of view. EMH imagines a world where investors behave ra onally, informa on is processed instantly and accurately, and prices always reflect true value. But in such a strictly ra onal market, investors would face several challenges: opportuni es for outperformance would disappear, emo onal reac ons would have no space to influence decisions, and the complexi es of human behaviour would be ignored. This idealised efficiency does not reflect the psychological pressures, emo onal impulses and informa on overload that investors confront daily. Behavioural finance offers the opposite view that markets are shaped by fear, overconfidence, herd ins ncts and numerous cogni ve biases. Yet this perspec ve alone cannot fully explain the long-term stability, compe ve discipline and eventual correc ons that markets frequently exhibit. Markets aren’t perfectly efficient or en rely irra onal; they operate in a hybrid state. Ra onal forces shape long-term trends, while behavioural biases o en drive short-term swings. Prices eventually adjust to informa on, but emo onal reac ons can cause sharp, temporary devia ons. This blended view recognises both EMH’s structural logic and behavioural finance’s psychological insights. Combining the two offers a more realis c way to understand real markets honouring financial theory while accoun ng for human behaviour.

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Anomalies: The January Effect By- Lekhan Gampala & Yashvi Jain

What is a Market Anomaly?

Market anomalies usually refer to unusual or inconsistent pa erns in stock price movements that should not exist if markets were perfectly efficient. These pa erns in financial markets appear to contradict the Efficient Market Hypothesis (EMH).

According to the Efficient market hypothesis, it is stated that markets fully and instantly incorporate all available informa on into stock prices. If this were always true, no investor could reliably “beat the market” using publicly known informa on, because prices would already reflect all available informa on, so it should be impossible to consistently earn above-average returns. It is a recurring irregularity in markets that should not exist under equilibrium condi ons.

Market anomalies expose weaknesses in common financial assump ons. Also, at mes it refers to distor ons created by tax, regulatory, or ins tu onal constraints. Anomalies suggest that markets do not always behave perfectly ra onal. The prices behave in ways that tradi onal financial theory cannot explain.

A market anomaly is a real-world reminder that markets, while powerful, are not perfectly ra onal or efficient.

Stock market anomalies occur when prices move in ways that standard financial theories cannot quite explain. At the heart of many of these unusual pa erns is human behaviour. Investors, like all people, are influenced by emo ons and biases. They some mes overreact to news, panic when markets fall, or blindly follow trends simply because others are doing the same.

These emo onal responses can push prices higher or lower than they logically should be, giving rise to pa erns such as momentum or the January Effect.

Another reason anomalies exist is that informa on does not spread through the market as smoothly or evenly as theory suggests. Some investors get informa on faster, others understand it be er, and smaller or less popular stocks may not get much a en on at all. As a result, prices do not always adjust immediately, crea ng short-lived mispricing that can appear as anomalies.

The market structure plays a major role. Tax rules, fund managers adjus ng their por olios at year-end, or mutual funds trying to make their holdings look impressive (a prac ce known as window dressing) can all influence stock movements in ways that have li le to do with the actual value of companies.

Low-liquidity stocks, which do not trade very o en, can behave even more unpredictably because their prices take longer to adjust or swing sharply with just a few trades. On top of this, tradi onal financial models like the CAPM do not capture all the risks that influence stocks. Real markets respond to factors such as company size, value, vola lity, and momentum, elements that these models largely overlook. When these influences are not accounted for, normal pa erns can seem like mysterious anomalies. Some mes, anomalies are not real at all, they are just sta s cal quirks that show up when researchers test too many ideas on the same data.

The January effect

What are the reasons for Market anomalies to occur?

The January Effect refers to a pa ern in the stock market where share prices par cularly those of smaller firms tend to increase more no ceably in January compared to other months. Many investors sell off stocks in December to reduce their taxable income or to balance their por olios, which can temporarily push prices down. When they begin buying again in January, prices o en bounce back, crea ng the appearance of a strong start to the year. This phenomenon contradicts the Efficient Market Hypothesis (EMH), as it suggests that predictable, seasonal factors can influence market performance, something that should not occur if markets were truly efficient.

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January Effect: What happened over me?

Historical research shows that the January Effect became more no ceable in the U.S. market toward the late 19th century.

Plastun & Plastun’s study covering two centuries found that between 1866 and 1890, January delivered much higher returns, about 0.75% compared to an average of only 0.023% in other months. The pa ern became even more sta s cally dependable from 1891 to 1915, indica ng that the anomaly strengthened as markets evolved, though it gradually faded in the modern era.

This effect is closely ed to investor behaviour. Small-cap stocks, such as those in the Russell 2000, o en jump in January because many investors sell poorly performing shares in December for tax advantages. Once the new year begins, they buy back these stocks, crea ng a quick price rebound. A similar trend appeared in India during the 1990s and early 2000s.

Economic liberaliza on brought more par cipa on from both retail and ins tu onal investors, increasing January buying ac vity.

Tax-loss selling, por olio reshuffling by mutual funds, and strong liquidity inflows o en pushed small- and mid-cap stocks higher at the start of the year.

As the Indian market became more efficient with greater foreign investment, be er technology, and algorithmic trading, the January Effect became weaker. While it s ll emerges occasionally, it is far less consistent today.

Understanding January Effect and Market Anomalies The January Effect is basically when stocks go up way more in January than they should. It should not happen if markets work right, but it does. Back in the 1800s to early 1900s, this thing was obvious. Like, January returns hit 0.75% while other months were just chilling at 0.023%. India saw the same thing happen during the 90s and 2000s when their market started opening to more traders and investors.

So, why is this happening? It is straigh orward. Come December, people sell off their losing stocks to get tax breaks.

When January hits, they buy those same stocks back. Prices jump up quick because of all the buying. Small cap stocks get hit hardest since they are more vola le and sensi ve to this kind of movement. But here is the catch, it does not really work anymore. Markets got way too smart.

Foreign investors showed up, algorithms started trading automa cally, and news travels instantly now. Everyone figured out the pa ern so nobody could make easy money off it anymore. By the 2000s, the January Effect basically faded away.

Market efficiency just killed the whole thing. Now it has basically gone as a reliable trading strategy.

Causes and Effects The January Effect is a market pa ern where stock prices, especially small-cap stocks, o en rise at the start of the year.

This mainly happens because investors sell poorly performing stocks in December to claim tax benefits, which pushes prices down, and then buy them back in January, causing a quick rebound. Ins tu onal investors also contribute by reshaping their por olios in early January, adding fresh demand that li s prices further.

A renewed sense of op mism at the beginning of the year increases risk-taking, especially among retail investors, which boosts buying ac vity. Since small-cap stocks are more sensi ve to shi s in sen ment and liquidity, they tend to show the strongest January gains.

Although modern markets are more efficient and technologydriven, making the effect weaker today, the January Effect s ll appears at mes and can create short-lived trading opportuni es.

Historical data of The January effect (US & Indian market) We have read about the January effect when looking back at old market pa erns in the United States and in India. It refers to the way stock prices some mes show stronger gains in the first month of the year. In the early 2000's period several studies noted that small company shares in the United States rose no ceably each January.

For example, researchers observed that between 2001 and 2005 the average January return for United States small cap indexes was close to 3% while the average return for the other months combined was around 1%.

This strengthened the belief that tax loss selling in December followed by renewed buying in January played a role.

In India the pa ern was more uneven but some figures from that same early period show hints of the same effect. Studies of the Bombay Stock Exchange reported that the Sensex gained roughly 2% in January 2003 compared with about 0.5% in the average month of that year.

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These days, informa on spreads quickly, enabling investors to foresee and account for such effects well in advance.

Because of this, any abnormal returns linked to the January Effect are typically transient or sta s cally insignificant, suppor ng the claim that once market anomalies are widely acknowledged, they frequently wane or vanish.

Conclusion

In the Na onal Stock Exchange smaller companies showed even more varia on. One study noted that mid cap indexes posted gains of around 3% in January 2004 which stood out when set beside the weaker performance seen in several other months.

These numbers led many to ques on what the January effect means for the efficient market hypothesis. If prices fully reflect all informa on there should not be a predictable seasonal rise.

At first the January effect looked like a clear excep on. But as traders became more aware of it the size of the effect began to shrink in both markets.

Supporters of the efficient market hypothesis argue that this fading pa ern shows how an anomaly disappears once investors try to exploit it. In this sense the January effect remains both a short-lived puzzle and a reminder of how markets slowly move back toward efficiency.

In conclusion, while the January Effect has been a prominent feature of market behaviour for many years, it faces significant challenges from both theore cal perspec ves and empirical evidence. Cri cs argue that it is more of a sta s cal ar fact driven by outliers or market inefficiencies, and recent studies suggest that the effect may be fading as markets become more efficient and investor behaviour becomes more ra onal. These cri cisms highlight the ongoing debate about the nature of market anomalies and the limita ons of tradi onal financial theories like the Efficient Market Hypothesis.

Cri cisms and Controversies Surrounding the January Effect There has been a lot of debate about whether the January Effect is even real or just people seeing pa erns where none exist.

Some researchers think it is totally overstated and that investors are basically chasing ghosts. They argue that if you look at the data properly, January does not actually perform that much be er than other months. It is all noise and coincidence.

Then you have got the whole argument about survivorship bias.

Cri cs say we are only looking at companies that stuck around.

What about all the stocks that crashed and disappeared?

Nobody talks about those. So, when we cherry-pick successful companies, January looks good. That is not real though.

There's also massive disagreement over whether tax-loss selling is the reason behind it. Some experts think that explana on is way too simple. They reckon it is more complicated than just people ming their trades around taxes. Weather, holidays, investor psychology, there is too many variables bouncing around. Plus, once word got out about the January Effect, a bunch of investors tried exploi ng it. But the more people tried to take advantage, the weaker it got. So, did the effect even exist, or did people just create it by talking about it? That is the real head-scratcher. Now it barely shows up in modern markets, which makes skep cs say "I told you so."

The fact that a large por on of the evidence for the January Effect is based on historical data that might not be applicable in today's highly efficient and digitalized markets is another significant cri que. Predictable seasonal pa erns are less likely to endure over me due to increased ins tu onal par cipa on, algorithmic trading, and lower transac on costs.

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Anomalies: Momentum & Value By- Ayan Gogoi & Abhishek Reddy Some individuals maintain that financial markets reliably and promptly set accurate prices; however, two recurring pa erns, momentum, and value, suggest this may not always be the case. Momentum indicates that stocks that have increased in value will con nue to rise for a period, while stocks that have decreased will con nue to fall. Value indicates that "cheap" stocks, in terms of valued-based metrics such as earnings or book value, will provide be er long-term returns than "expensive", or growth, stocks.

When new informa on enters the market or investor opinion alters rapidly, momentum trades can quickly unwind, resul ng in significant losses. This was visible during large market correc ons, when stocks that had previously showed strong rising tendencies fell sharply. Such occurrences demonstrate that, while momentum may exist under stable se ngs, it is extremely suscep ble to changes in market expecta ons, suppor ng the no on that behavioural biases rather than fundamentals frequently drive these short-term price swings.

The above graph depicts momentum inves ng, defined as when an investor buys an asset while the price is rising and then sells it a er it has increased even higher. Investors enter on price momentum on the upward wave and exit before the next wave down, a emp ng to ride the wave as it lasts. It is effec vely "buy high, sell higher."

The Value Effect: The Power of Underdogs

Some analysts assert that this is due to higher risk; other analysts assert that it derives from overreac on of investors, and any resul ng mispricing. Anomalies such as these challenge the general no on of "market efficiency" and illustrate the impacts of human behaviour.

The research undertaken in this study will examine the short history of momentum, and value, strategies in the Tunisian stock market from 2013 to 2020, including the impact of trading costs, as well as seasonality.

The Momentum Effect: Riding the Wave The momentum effect is a simple concept: stocks that have risen tend to con nue rising for some me, and stocks that have declined tend to con nue declining. This has been shown in research publica ons by Jegadeesh and Titman, and is one of finance's great mysteries, as this should not happen in a completely efficient market.

The main driver appears to be human behaviour humans are slow to incorporate good news, they follow the herd as people do, and they o en buy even more as prices rise, thus causing a con nua on in the momentum as winners keep winning un l something happens. In addi on, we know that investors tend to underreact to good news and overreact to bad news which may help explain the persistence of momentum. This study found considerable momentum returns once we took out the crisis years. Another issue with momentum-based techniques is their fragility during moments of sharp market reversals.

The value effect refers to the no on that lower-priced stocks outperform higher-priced stocks over me. The stocks that are "cheap" are generally the stocks that the market has ignored o en because the company is in an uninspiring industry, or recently the company received bad news. The investors have reacted and priced them too low partly due to a behavioural overreac on where they have completely disregarded the fundamentals. However, eventually, the bad news stops being foremost in investors' mind, and the stock prices return to their fundamentals.

Value inves ng is a paradigma c strategy mixing pa ence and common sense: simply put, the idea is to buy good companies when they are out-of-favour. Warren Buffe captures this in his famous quote, "Price is what you pay; value is what you get."

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Although value tends to struggle during periods in which our a en on is caught up in a glamour or hysteria (tech booms) based on hype, value s ll shows that as groups we can price things incorrectly.

Value investment, however, has its limita ons. Stocks may remain inexpensive for extended periods due to structural issues such as poor corporate governance, fading industries, or persistent financial crisis. In such circumstances, low prices may indicate serious risks rather than transient mispricing. This makes value inves ng a challenge of pa ence, as returns are frequently unequal and need a long investment horizon. As a result, the value impact does not ensure success, but rather rewards diligent analysis, cau ous stock selec on, and the ability to discriminate between companies that are temporarily overlooked and those experiencing more serious, long-term issues.

What the chart displays: The y-axis (the ver cal line) signifies growth in index value (star ng near 100). The x-axis (the horizontal line) signified the years along the way from 2016 to 2023. There are two lines accoun ng for performance: Blue line: MI India Top10 (Momentum)& Green line: Ni y50 Value20 (Value)

The Indian Market Context India presents a one-of-a-kind environment that has been iden fied and successful exploited by investors for momentum and value anomalies.

Momentum vs. Value: The Great Debate Momentum inves ng and value inves ng are fundamentally different ways of thinking about the market. Momentum focuses on what’s working now, while value relies on what should work over me. Momentum benefits from trend, velocity and bullish investor psychology. When markets are in a strong uptrend, momentum stocks generally lead the charge higher as investors seek winners. Momentum stocks naturally a ract buying interest, liquidity, and rapidly rising prices. In an uptrend when investor confidence is high, the market rewards the trend-followers handsomely.

Value inves ng requires correc on, pa ence, and ra onal appraisal. Value investors want stocks whose promise has been neglected, or are undervalued because of their state of repair at the moment. Value investors are rewarded for their pa ence usually a er a downturn, when sen ment is at its lowest and price is depressed. When bullish condi ons return, the underlying companies involved are not only able to re-establish fair value but have created value through apprecia ve cash flows to drive a rapid apprecia on.

Many professional investors today deploy two styles in their investment arsenal: 1. Momentum for shorter-term tac cal opportuni es and 2. Value for longer-term compounding and wealth preserva on.

By keeping a balance between both styles, they are be er equipped for different cycles in the market.

Momentum in India: Studies of the NIFTY 500 and BSE 200 indices inves gated whether momentum strategies actually worked. They found that momentum strategies tend to outperform during market rallies as retail par cipa on increases. India's markets o en result in rallies of investor excitement, propelled by news, social media and rising disposable incomes. With momentum, the phenomenon tends to be self-correc ng: when a stock starts to rally, retail investors can pile into the stock, pushing the price even higher. This creates the psychological feedback loop a momentum strategy is banking on.

Value in India: Value inves ng is very much a part of Indian history, with many celebrated investors origina ng in India: Rakesh Jhunjhunwala, Porinju Veliyath, Ramesh Damani, Vijay Kedia, etc. have built wealth by inves ng in high quality, undervalued companies. The Indian market has been historically inefficient, especially in earlier decades of limited ins tu onal par cipa on and informa on asymmetries between the local retails and the domes c ins tu ons, crea ng idiosyncra c value opportuni es. Many of India's "mul -bagger" opportuni es are just the stories of exploited, mispriced businesses that were discovered over me.

Anomalies and Market Progress:

The chart displays the performance of two dis nct investment strategies over me: 1. MI India Top10 – a momentum-based large-cap strategy 2. a value-based factor index going by Ni y50 Value20 and run

by NSE

As the Indian markets con nue to develop, with features such as significant retail par cipa on, unreliable informa on flow, and episodic spells of overreac on they con nue to show both momentum and value anomalies. That makes India a par cularly interes ng environment to disciplined investors who are able to detect behavioural elements, govern their emo ons, and stay on defined courses of ac on. In short, India’s blend of increasing retail behaviour, emo onal trading cycles, and structural inefficiencies makes sure anomalies exist as a tangible characteris c of the market that can be observed and taken advantage of.

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The graph describes two types of strategies for investing:

Not a Single Formula for All: The most successful investors cannot simply follow one set of screens or a fixed formula. Instead, they modify their approach based on market regimes, combine quan ta ve signals with qualita ve judgement and understand when to accept risk and when to become defensive.

Conclusion

The White Line – Momentum Strategy: The white line indicates a trend-following path.

It has an upward turn a er every rise and con nues upward.

This is indica ve of momentum inves ng; inves ng on rises and selling on the starts of downturns. It is important to note the numbers such as 20567.95, 16975.95, and 11015.07 signify the price movement levels the momentum trader is responding to.

It means that momentum investors con nue to ride the exis ng trend. When the market price is going up, they will con nue to invest in the asset un l the momentum stops.

Momentum and value effects demonstrate clearly that markets do not always behave as the Efficient Market Hypothesis would predict; prices adjust slowly, investors behave imperfectly, and simple pa erns—recent winners keep going up, and undervalued stocks perform well—persist over different markets and me periods. These behaviours are primarily driven by cogni ve biases of humans, such as underreac on, overreac on, and herd behaviour. The consistent presence of these factors indicates that markets are not perfectly efficient, but reflect human behaviour, leading to predictable opportunity, and risk.

Ul mately, both momentum and value serve as reminders of how it is not only data that drives markets but emo on. There would be no momentum without the collec ve op mism that accompanies a group of investors, and value inves ng opportuni es occur by exhibi ng pa ence in a chao c environment.

The Yellow Line – Value Strategy The yellow line is much more stable and rises slowly but surely.

It does not follow every rise and fall. This, explains value investing; buying the undervalued stocks and being very patient until the price eventually rises. A value investor will ignore all the short-term volatility of the price and focus on the fair value of the investment over the long run.

Limita ons and Cri cisms There are shortcomings for each strategy, and neither is certain to be successful.

Weakness of momentum: High Returns, High Risk Momentum investors can suffer significant losses when markets turn on a dime. If a trend reverses or there is sudden news to impact the markets, high-flying stocks will be crushed. As discussed earlier, constant churn of capital results in high transac on costs which eat away at net returns.

Weakness of value: Requires Pa ence, O en for Years The biggest cri que with value inves ng is that you fall into the “value trap”: meaning, a company looks cheap, but stays cheap for two or even three years due to a lack of catalysts. As a value investor, your stock may also trail for extended periods of me, tes ng your pa ence and convic on. In modern markets, where informa on moves quickly and business models change quickly, tradi onal value metrics may not accurately capture intrinsic worth.

Strategy will Degrade Over Time We understand that as investors learn and embrace the strategies discussed above, markets become smarter. A strategy becomes more popular means, price adjustments will occur quicker, they have less of an edge, meframes of opportunity will diminish.

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Active vs. Passive Investing By- Nakka Niharika & D. Pavan Ac ve and passive inves ng are simply two approaches to growing your money, each suited to different preferences and lifestyles. Ac ve inves ng is for those who enjoy being handson tracking news, comparing companies, and trying to spot opportuni es before others do. It’s like gardening: constantly adjus ng, nurturing, and making decisions to achieve be er results. While it can be rewarding, it also demands me, a en on, and energy.

Passive inves ng, on the other hand, is for people who prefer a calmer approach. Instead of hun ng for the next big stock, passive investors put their money into index funds or ETFs and let the market grow naturally over me. It’s similar to plan ng a tree and le ng it mature year a er year you check in occasionally but don’t need to manage every small detail. This method is low-maintenance, cost-effec ve, and focused on long-term growth.

Examples: HDFC Top 100 Fund HDFC Top 100 Fund is a classic example of ac ve inves ng, where expert fund managers carefully choose large-cap companies they believe can outperform the market. Instead of simply copying an index, the fund studies each business’s growth poten al, financial strength, and long-term prospects.

The por olio is ac vely monitored, and stocks are added or removed based on changing market condi ons and company performance. Investors rely on the fund manager’s skill to generate higher returns than benchmark indices. In short, it’s an ac vely managed fund that aims to beat the market—not

just follow it.

The graph shows that if you had invested ₹10,000 five years ago, the HDFC Top 100 Fund (ac ve fund) would have grown slightly more than the Ni y 100 TRI (passive benchmark). The ac ve fund gave a ~1% higher yearly return than the index.

ICICI Pruden al Blue-chip Fund Ul mately, neither approach is universally be er. Some investors enjoy the challenge and engagement of ac ve strategies, while others appreciate the peace and consistency of passive inves ng. Many people even blend the two being mostly passive while occasionally making ac ve choices.

Choosing the right approach comes down to your comfort level, the me you can dedicate, and what helps you stay consistent with your financial goals.

Ac ve Inves ng An ac ve investor frequently buys and adjusts investments based on performance and future poten al, aiming to outperform the market. This hands-on approach relies on deep research, analysis, and mely decision-making to capture shortterm opportuni es and avoid underperforming assets.

Whether done by individuals or professional managers, ac ve inves ng o en uses tools like fundamental analysis and requires con nuous monitoring of market trends and company financials to make informed investment choices.

Ac ve inves ng is a hands-on approach where managers con nuously track markets to manage risks, seize short-term opportuni es, and customize por olios to meet individual goals. This flexibility can help avoid losses during downturns and capture gains that passive strategies may miss. However, it comes with higher costs due to frequent trading and o en requires larger investments and exper se. Overall, ac ve investing offers more control but demands more time, skill, and resources to be effective.

ICICI Pruden al Blue-chip Fund is an ac vely managed large-cap fund where experts carefully select strong, stable blue-chip companies based on their earnings poten al, leadership quality, and long-term compe veness. Instead of simply tracking an index, the fund ac vely adjusts its por olio as market condi ons change to capture be er opportuni es.

Investors rely on the manager’s insights to try and outperform benchmark returns, making it a disciplined ac ve inves ng approach focused on high-quality, industry-leading companies.

The graph shows that the ICICI Pru Blue chip Fund has delivered be er 5-year returns than the Ni y 100 TRI, proving mild but consistent outperformance of ac ve management.

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Examples: Effect of Ac ve inves ng on Risk and Returns

Vanguard S&P 500 ETF:

Ac ve funds show a wide range of returns and risks. Some, like SBI Equity Savings, are very stable but lag their benchmarks, while others such as ICICI Midcap and HDFC Large Cap deliver strong returns with solid outperformance. Nippon Small Cap gives the highest returns but with higher risk, and Kotak Equity Opportuni es underperforms with very high market sensi vity.

Overall, ac ve funds can outperform the market, but they come with higher vola lity and greater risk compared to passive op ons. At the same me, ac ve funds' performance is heavily reliant on the fund manager's ability, market ming, and inves ng plan.

While some managers can spot opportuni es and manage risk successfully, others may struggle to regularly outperform the market a er deduc ng fees and expenditures. Frequent por olio turnover can also raise expenses and lower net returns for investors. As a result, the success of ac ve inves ng is frequently inconsistent, emphasizing the significance of cau ous fund selec on and realis c expecta ons for riskadjusted performance.

The Vanguard S&P 500 ETF (VOO) is a popular passive inves ng op on in the U.S. because it simply tracks the S&P 500 index, which includes 500 major companies across sectors like technology, finance, healthcare, and consumer goods. Instead of relying on a fund manager to pick stocks, VOO aims to match the market’s performance by mirroring the index.

This makes it a low-cost, easy, and reliable choice for long-term investors who trust the steady growth of the U.S. economy and prefer a simple investment approach without the need for constant monitoring or ac ve decision-making.

Behaviour of ac ve Investors Ac ve investors constantly buy and sell, aiming to beat the market using their analysis or intui on. This hands-on style o en triggers biases like overconfidence, herding, and emo onal reac ons. Frequent decisions can lead to impulsive or poorly med trades. While ac ve inves ng can deliver higher returns in good markets, it also carries higher risk.

Overall, success demands strong research, discipline, and emo onal control.

Passive Inves ng

The graph shows that the Vanguard Growth Index ETF (VUG) has massively outperformed the S&P 500 over 20 years, delivering nearly double the market’s return.

SBI Ni y Index Fund: SBI Ni y Index Fund is a passive mutual fund that mirrors the Ni y 50 Index. Its goal is to replicate the performance of Ni y 50 by inves ng in the same stocks in the same propor on. Being a passive fund, it has low management fees and is ideal for investors seeking long-term growth with minimal por olio management, without trying to outperform the market.

Effect of Passive inves ng on Risk and Returns

A passive investor focuses on long-term growth by holding mutual funds or ETFs that track major market indices instead of picking individual stocks. These funds automa cally mirror benchmarks like the S&P 500 or DJIA, adjus ng only when the index changes. This simple buy-and-hold approach provides broad diversifica on, benefits from overall market growth, and helps investors stay steady through short-term market fluctua ons. Passive inves ng offers low fees, high transparency, tax efficiency, and simple diversifica on by tracking indices like Ni y 50 or S&P 500. However, it lacks flexibility, cannot avoid poorly performing stocks within the index, and generally provides only market-matching returns rather than outperforming. Overall, it’s a stable, low-cost approach but cannot react quickly to risks or short-term opportuni es.

Passive index funds usually deliver steady, predictable returns with lower risk. Most funds like SBI Ni y, ICICI Midcap 150, and HDFC Sensex slightly outperformed their benchmarks with low vola lity and posi ve alpha. Nippon Small cap 250 showed very strong risk-adjusted returns with minimal vola lity. Kotak Ni y Next 50 performed well overall but carried higher risk and a nega ve alpha. Overall, index funds offer stable, lower-risk growth, even if returns are generally lower than ac ve funds.

Behaviour of passive Investors Passive investors focus on long-term growth by holding index funds or ETFs without reac ng to daily market swings. Their rules-based approach reduces emo onal decisions, stress, and impulsive trading. They stay disciplined and consistent, benefi ng from steady market growth over me. However, in developing markets like India, behavioural biases can s ll affect new investors with low financial awareness. Overall, passive inves ng encourages calmer, more stable decision-making.

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Performance comparison of ac ve, passive and market returns

this emo onal pressure because you don’t need to track daily market movements or react to short-term vola lity.

By consistently inves ng and staying pa ent, individuals allow the market’s natural growth to work in their favour. According to Buffe , this simple, disciplined, and stress-free approach not only reduces mistakes but o en results in be er returns than a emp ng to me the market or follow complex ac ve strategies.

The Role of EMH in Understanding Ac ve Inves ng Performance EMH challenges ac ve inves ng by sta ng that stock prices already reflect all available informa on, making it hard to beat the market consistently. This means ac ve investors must rely heavily on skill and research to find rare inefficiencies.

Something EMH says is difficult. In short, EMH forces ac ve inves ng to jus fy its higher costs by proving it can find opportuni es the market has supposedly already priced in.

EMH is the founda on of modern passive inves ng Philosophy The comparison chart shows how ac ve and passive funds perform against market returns. Passive funds closely track benchmarks like NIFTY 50 and NIFTY Next 50, offering stable and predictable returns. Ac ve funds show greater varia on, some mes outperforming or underperforming depending on fund managers’ decisions. This highlights that passive inves ng priori zes consistency, while ac ve inves ng carries higher poten al gains but more risk. Overall, the chart emphasizes the trade-off between stability and performance variability.

The Efficient Market Hypothesis says stock prices already reflect all available informa on, so consistently bea ng the market is extremely difficult. That's why EMH challenges ac ve inves ng finding “undervalued” stocks or ming the market rarely pays off a er costs. Instead, EMH supports the idea of simply owning the market through low-cost index funds and ETFs. With fewer trades and lower fees, passive inves ng lets more of your money stay invested and grow over me. Overall, EMH shows that long-term, diversified, passive inves ng is the most realis c and rewarding approach for most people.

Warren Buffe ’s Recommenda on of Passive Funds for Most Investors

Conclusion

Warren Buffe believes that most investors should choose passive funds because consistently outperforming the stock market is extremely difficult, even for seasoned experts. Ac ve inves ng requires deep research, con nuous monitoring, and the ability to make accurate decisions under pressure skills that the average investor may not have the me or resources to develop. Instead of picking individual stocks, Buffe recommends inves ng in low-cost index funds like the S&P 500, which automa cally spread your money across hundreds of well-established companies. This broad diversifica on reduces risk, lowers costs, and helps investors benefit from long-term market growth without needing specialized knowledge.

Another major reason behind Buffe ’s preference for passive funds is the power of compounding and cost efficiency. Index funds usually carry very low expense ra os because they don’t require fund managers to constantly buy and sell stocks. Lower fees mean more of your money stays invested and grows over me. Buffe trusts passive inves ng so strongly that in his own estate plan, he has instructed that most of the money should be invested in a simple S&P 500 index fund. This reflects his belief that over long periods, an index fund will outperform most ac vely managed strategies due to lower costs and steady market-driven returns.

Buffe also highlights the behavioural advantage of passive inves ng. Most people make emo onal decisions when markets fluctuate panic selling during downturns or buying aggressively during rallies. These reac ons o en lead to poor long-term performance. Passive inves ng eliminates much of

Ac ve and passive inves ng each offer valuable benefits, and neither strategy is universally superior. Ac ve inves ng provides flexibility, quicker decision-making, and the poten al to outperform the market but it also requires deep research, emo onal discipline, and higher costs. Passive inves ng, guided

by EMH, offers simplicity, lower fees, and long-term stability by following market trends rather than trying to predict them.

Investors who prefer a hands-on approach may lean toward ac ve strategies, while those seeking predictable, low-stress growth o en choose passive funds.

In the end, the choice depends on an investor’s goals, risk tolerance, and the me they can commit. Many individuals benefit from a balanced approach—using passive funds as a founda on for steady growth, while selec vely using ac ve strategies to capture addi onal opportuni es. By understanding both methods and how they behave in different market condi ons, investors can build a diversified, disciplined plan that supports long-term wealth crea on.

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