Impact of Stock Market Knowledge on the Trading Behavior

Impact of Stock Market Knowledge on the Trading Behavior: Experimental Evidence from a Developing Country Presenter: Kazi Iqbal Co-authors: Asad Islam, Vy Nguyen Extended Abstract

Introduction and Design While the weak financial regulations generally take the blow in the wake of any financial crisis, the role of financial knowledge of the consumers and investors in protecting their financial health has also been increasingly incriminated in both developed and developing worlds. The global financial crisis of the last decade taught us a lesson on the cost of financial illiteracy and this has also spurred renewed interest of the policy makers and academicians in how to make citizens more financially literate. There is a huge body of RCT based evidence on the impact of financial trainings designed for the students, entrepreneurs, retirees, borrowers, migrant workers, remittance receivers, etc. on their financial decision making, particularly saving, investment and borrowing1.

While there is an evidence that financial literacy increases stock market participation (Van Rooij et al. 2011), there is no evidence that financial literacy or the knowledge of the stock market has any impact on the portfolio performance of the investors. Participation is important for stock market development, but it is the portfolio performance that matters for individuals’ welfare and this in turn also determines whether they will stay in the market or not. To the best of our knowledge, ours is the first study to assess the impact of training on stock market on the actual trading behavior of the investors.

We partnered with eight brokerage houses of Bangladesh and their willing clients were randomly selected into treatment (201) and control groups (132). We conducted a three-day long training sessions on fundamental and technical analysis of the stock market. The partnership with the brokerage houses allows us access to the daily transaction and monthly portfolio data of the selected traders. We construct the daily performance measures of these traders and compare these measures across treatment and control groups for both post and pre-treatment periods. We have data for outcome variables for 16 months, 8 months before training and 8 months after training. Our outcome variables include number and volume of buys and sales, realized and unrealized gains and losses, portfolio value and some measures of portfolio diversification. We also conduct baseline surveys on socioeconomic and other characteristics of the traders which have bearing on their trading behavior (personality, self-confidence, self1 For a review of the evidence on economic importance of financial literacy, see Lusardi and Mitchell (2014). efficacy, self-assessed knowledge on stock maker). Finally, prior to the training, we run a labin-the-field session to elicit risk, time, and ambiguity preferences of the traders.

The primary goal of the training was that the participants would learn how to pick a 'good' stock based on fundamentals. The training materials we developed particularly focused on fundamental analysis with some basics of technical analysis. The technical analysis includes the issues of careful inspection of price charts and developing an understanding of the patterns of price change to predict the future behavior of prices. The section on fundamental analysis aims at analyzing a company’s fundamentals to find a stock’s intrinsic value, as opposed to the value it is being traded in the market. In particular, the module on fundamental analysis includes a few concepts such net asset value (NAV), NAV per share, earning per share (EPS), dividend yield, price earning ratio, market price to book NAV ratio, etc.; use of various ratios to understand the intrinsic value of the share and also understanding financial statements of the companies.

Dataset We collect a unique transactions dataset of the participants - the data consists of official hard-copy statements (thus reliable) recording all transactions across the time span from January 2015 to April 2017. Our participants made a total of 100243 transactions over the period, of which 42136 are sales transactions (as compared to 44910 buys order), on average each sale transaction's volume is 5800 shares (the mean volume is 5498 for sale). The average transaction has a value of 182303 taka.

Based on the snapshots of the investors' portfolio on the three days- 30th October 2015, 31st July 2016, and 30th April 2017, we were able to construct and cross-verify traders' daily portfolio over the period from January 2015 to 30th April 2017, excluding non-trading days (weekend and public holidays).

Our market dataset comprises day-end statistics of all common stocks listed on the Dhaka Stock Exchange (DSE) from 1990 to 2017. We exclude stocks that were not actively traded at any given time from 2015 to 2017 or stocks that do not have available information on daily stock returns, trading turnover, market capitalization, and the fraction of shares held by institutional investors.

We also necessarily exclude those who made no transaction over the sample period.

Finally, information on listed companies was collected manually from different sources including DSEBD, CEIC, AMIBROKERAGE, and CRAB (Credit Rating Institution in Bangladesh).

Main result We employ a Difference-in-Difference approach to study the impact of training on trading frequencies and stock selection. Our results indicate that traders who received training improve their portfolio diversification immediately after the intervention. Specifically, they broaden their exposure by including more industries or reducing the size of the most concentrated sector of the portfolio. The treatment group also rely less on the highly saturated industries such as pharmacy or bank.

On the other hand, our intervention also leads to several undesirable trading behaviours. We find mixed results on the impact of training on the number and volume of transactions. Our treated traders tend to increase their trading frequencies after the intervention, both in terms of purchases and sales. The existing literature points out the (over)trading behaviour is usually associated with lower performance. The first explanation for this outcome is that traders need to make necessary transactions to improve their portfolio diversification. However, there is still a subsample of traders

who trade as a form of speculation – it is probable that training encourages overconfidence and traders will react more towards market fluctuation.

We also examine the heterogeneous impacts over along a range of individual behavioural preferences. Using the elicited preference in our artefactual games, we find that the training induces the risk-averse and ambiguity avoiding traders to diversify their portfolio more effectively.