AIBC 2024 Introductory Series: The Essentials of Quantitative Trading
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AIBC Global Research Team continues to provide unparalleled student development opportunities in 2024
It is with great pleasure that we welcome you to the Asia Investment and Banking Conference (AIBC) 2024, where this year's theme, "Redefining Boundaries," encapsulates our commitment to shaping the future of finance In line with this vision, we proudly present the #AIBCResearch series an endeavour to merge academic research with industry insights, developed by our student analysts.
This curated collection of articles aims to provoke thought and challenge thinking AIBC's dedication to student development, strategic talent discovery, and meaningful industry connections is exemplified through our research initiative. These components are integral to our foundational goal of equipping emerging finance professionals with the tools to transcend traditional barriers.
We invite you to engage fully with this series, allowing the insights within to broaden your perspectives May your journey through the #AIBCResearch series inspire you to redefine your own boundaries and contribute to the ever-evolving narrative of the finance industry.
The Introductory Series is a practical collection aimed at providing a clear view of the foundational elements of finance. It consists of eight research booklets, each one concentrating on a key area within the finance sector, topics which will be part of the AIBC 2024 conference discussions
These booklets are created to cut through the complexities of finance, offering clarity to readers from all backgrounds Our objective is to equip you with a basic yet comprehensive understanding of each financial domain, underlining its specific functions, the challenges it encounters, and the potential it holds within the global financial framework.
Investments in assets outside of stocks, bonds, and cash, including real estate, commodities, and collectables.
Private investment funds employ various strategies to earn active returns for their investors, often using leverage and derivatives.
The process of developing, operating, maintaining, and selling assets to maximise investor returns.
Financial services dealing with the creation of capital for other companies, governments, or other entities through underwriting or acting as the client's agent in the issuance of securities.
Banking services offered to high-networth individuals, providing personalised financial advice and solutions.
The use of mathematical models and algorithms to identify trading opportunities and manage investments automatically.
Investment in private companies or buyouts of public companies, aiming to restructure or improve their operations and profitability before selling them for a profit.
The buying and selling of securities, commodities, and other financial instruments in financial markets, through either facilitating transactions for clients or trading on their own firm’s behalf.
Quantitative trading is a type of trading that uses quantitative analysis and mathematical models to analyse the change in price and volume of securities in the stock market, in order to evaluate trading opportunities It is traditionally employed by hedge funds such as Citadel and Bridgewater and financial institutions, as the transactions are large
Quantitative traders utilize programming languages to conduct web scraping (harvesting) to extract historical data on the stock market This data is then used as an input for mathematical models Subsequently, the trader derives the assumption of the change in price and volume of securities by collecting, reviewing, and analysing historical data Every data set reveals patterns and quantitative trading extracts these patterns
An example would be that a trader might spot that volume spikes on Apple stock are quickly followed by significant price moves. Then they will build a program that looks for this pattern across Apple’s entire market history. If this pattern has resulted in a rise in share price 95% of the time in the past, then the model will predict a 95% probability that similar patterns will occur in the future.
Quant traders develop systems to identify new opportunities. While every system is unique, they usually contain the same components:
Transparency
Transparency
2: Components of quantitative trading system (Source: WallStreetMojo)
Before creating a system, quant traders will conduct research on the desired strategies. This often consists of a hypothesis (an assumption about the change in price and volume of a share). Subsequently, the trader will decide how frequently the system will trade. High-frequency trading (HFT) systems open and close any positions each day, while lowfrequency ones aim to identify longer-term opportunities.
This involves applying the strategy to historical data to predict how it might perform on live markets. This helps quant traders to further optimise their systems. One obstacle to the accuracy of this model is identifying how much volatility a system will see as it generates returns.
The execution component in a system can range from fully automated to entirely manual. An automated strategy usually uses an Application Programming Interface (API) whereas a manual one may entail the trader calling up their broker to place trades. A key part of execution is minimising transaction costs, which may include commission, tax, slippage and the spread.
This encompasses the optimal capital allocation theory. This is the means by which capital is allocated to a set of different strategies. This involves complex mathematical calculations, especially when dealing with leverages. In addition, one’s psychological profile is also considered. A common bias is loss aversion, where a losing position will not be closed out due to the pain of having to realise a loss.
Mean reversion strategies are based on the idea that
Mean reversion are based on the idea that asset prices tend to revert to their historical mean or asset prices tend to revert to their historical mean or average level. Any deviations should, eventually, revert average level. Any deviations eventually, revert to that trend. Traders identify overbought or oversold to that trend. Traders identify overbought or oversold conditions and take positions anticipating a reversal to conditions and take positions a reversal the mean. the mean.
Statistical arbitrage involves identifying and exploiting Statistical arbitrage involves and exploiting price inefficiencies between related securities. This price inefficiencies between related securities. This builds upon the assumption that a group of similar builds upon the assumption that a group of similar stocks should perform similarly on the markets. Traders stocks should perform similarly on the markets. Traders use statistical models to predict price movements and use statistical models to predict price movements and execute trades that capitalize on temporary execute trades that capitalize on temporary discrepancies. discrepancies.
This strategy seeks to profit rfrom the relationship
This strategy seeks to profit rfrom the relationship between an index and the exchange-traded funds between an index and the exchange-traded funds (ETFs) that track it. For instances, if ABC Limited were (ETFs) that track instances, if ABC Limited were to join the FTSE 100, then numerous EFTs that track the to join the FTSE 100, then numerous EFTs that track the FTSE 100 would have to buy ABC Limited shares. As a FTSE 100 would have to buy ABC Limited shares. As a result, by utlilising ultra-fast execution systems, quant result, by utlilising ultra-fast execution systems, quant traders can capitalise on this rule and trade ahead of traders can capitalise on this rule and trade ahead of the forced buying, then sell it back to the ETF managers the forced buying, then sell it back to the ETF managers for a higher price. for a higher price.
This involves various methods to spot an emerging
This involves various methods spot an emerging trend using quantitative analysis, such as monitoring trend using quantitative analysis, such as monitoring sentiment among traders at major firms and predicting sentiment among traders at major firms and predicting occasions at which traders should buy and sell certain occasions at which traders should buy and sell certain shares in large quantities. shares in large quantities.
This strategy involves building a model that can identify
This strategy involves building a model that can identify when a large institutional firm is going to make a large when a large institutional firm going to make large trade. For firms that wish to make larger orders trade. For firms that wish to make larger orders without affecting the market price, they will route their without affecting the market price, they will route their orders to multiple exchanges such as different brokers orders to multiple exchanges such as different brokers and dark pools to disguise their intentions. If the and dark pools to disguise their If the designed model can ‘break the code’, quant traders can designed model can ‘break the code’, quant traders can purchase the shares in advance and sell them art a purchase the shares in advance and sell them art a higher price. higher price.
This strategy seeks to identify markets that are affected
This strategy seeks to identify markets that are affected by these general behavioural biases - often by a specific by these general behavioural biases - often by a specific class of investors. Behavioural bias recognition seeks to class of investors. Behavioural bias recognition seeks to exploit market inefficiency in return for profit. exploit market inefficiency in return for profit.
Using algorithms means quant firms can eliminate human bias and emotion that may negatively impact the trades.
Automated execution and high frequency trading (HFT) allows traders to make more trades than if they were inputting orders manually Therefore, firms benefit from more rapid execution of trade, which increases the likelihood of capturing profits in a volatile market
Even if programmers design a sophisticated model, market conditions can change quickly, rendering the theory which the strategy was built on irrelevant Therefore, the uncertainty arising from volatility in the market could undermine even the most effective algorithms
The integration of HFT into quant trading suggests traders can quickly analyse large amounts of data for multiple markets at once, which increases the efficiency of decision-making and reduces the need for human resources
Quant trading requires a large base of capital and a range of knowledge not accessible to most individual traders. Intead, it is the financial institutions and large quant firms such as Citadel that benefit the most from the rise of quant trading
The models and algorthimic systems utilised in quant trading are only as good as the quant traders make them to be
Knight Capital Group was an American global financial services firm that managed an average daily trading volume of 3.3 billion trades. The issue began when Knight Capital deployed new trading software that inadvertently reactivated an outdated piece of code known as "Power Peg," which was intended to manage orders differently from the new system This reactivation caused the system to send a large number of erroneous buy and sell orders, leading to significant disruptions in stock prices As a result, Knight Capital acquired large positions in various stocks at unfavorable prices, rapidly accumulating substantial financial losses The company’s internal systems failed to detect the issue promptly, and by the time the problem was identified and corrective actions were taken, the losses had already escalated to $440 million, and resulted in a 75% fall in the group’s share price
The Knight Capital incident highlights several critical lessons for the trading industry Comprehensive testing of all software updates and deployments is essential, including simulated environments that replicate live trading conditions to identify potential issues. Establishing robust fail-safes and redundancy measures can mitigate the impact of system failures, while real-time monitoring and alert systems help identify anomalies and unusual trading patterns quickly.
Despite the implementation of highly efficient algorithms and automation in executing trading ideas, human judgment remains essential in designing, validating, and overseeing quantitative trading strategies. Complex models require careful human evaluation before implementation, as humans are prone to errors If a minor coding mistake is overlooked, high-frequency trading (HFT) systems can amplify the error, potentially leading to significant losses
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