The Effect of Over-confidence on Investors Decision-Making - Coursework Example

The Effect of Over-confidence on Investors Decision Making Contents Definition of Overconfidence 4 Trader and Retail Investor 4 Decision making:  4 Over-confidence affects investors decision making 5 Discussion of the Topic 5 Number of factors lead to overconfidence 5 How the above factor does affect the overconfidence  6 Overconfidence in respect of retail investors 7 Traders’ perspective with respect to the Terrence Odean Model 8 Traders contribute large amount of shares in the stock market 9 Decision making becomes difficult during high volatility 10 Hard Easy effect 11 Consequences for traders behaviour 11 At micro level 11 At the macro level 14 Counter argument 14 Conclusion 15 References 17 Introduction Definition of Overconfidence Overconfidence in investment decisions is indicative of overestimated judgements without rationalising the events of probable outcomes. It has no logical binding on the decisions of traders or investors. People tend to make decisions based on assumptions rather than facts, leading to overconfidence (Krueger, 2008). Trader and Retail Investor There is a thin difference between trader and retail investor based on investment and return objective. Traders invest for a shorter time period i.e. intraday trade and look to maximise profit through arbitrage. They aim at short term gains by considering the price movements only. On the other hand investors look for capital appreciation of their investment and invest for a longer time frame. They usually reinvest the dividends in the stocks. They are not influenced by market fluctuations and take decisions based on market and fundamental analysis. Investors are of two types i.e. retail and wholesale. Retail investors are individuals who invest in small proportions in stocks and other financial instruments, whereas wholesale refers to corporate who invest in large amounts (Turner, 2006) Decision making:  Traders – Price is an important determinant that influences the decisions of traders. They follow the technical analysis of companies and the market index to arrive at investment decisions. Traders are trend followers; they go long on when they anticipate an uptrend in the stock price and vice versa. Usually they buy, hold and sell stocks based on such price trends, thus influencing their trading volume. Retail Investors – They engage in investing in few stocks and expect capital appreciation rather than differential earnings. They have less knowledge and expertise about the market and analysis tools. Their decisions are influenced by peers, share brokers and experts, thus exhibiting herd mentality. Owing to limited information and low risk appetite, they invest in very few stocks with an optimistic outcome (Ackert and Deaves, 2009). Over-confidence affects investors decision making Over-confidence results from investor psyche or their pre determined mindset. Investors form certain prejudices about the market, company and the stock that intend to invest in. Their emotional quotient guides their decisions, thus making them over-confident. Such behavioural exhibit results in poor investment, resulting in low capital appreciation. Retail investors, owing to their limited knowledge invest in very less stocks and instruments thus are exposed to high risk. They usually follow the notion of good companies have good stocks. Over confidence inhibits investors to diversify their portfolio, thus yielding lower returns (Todd and Gigerenzer, 2012). Discussion of the Topic Number of factors lead to overconfidence People are in general over confident. According to behavioural psychologists people who are overconfident tends to overestimate their knowledge and underestimate the risks that they associate with a particular situation. They have a tendency to think that have a great level of control over their environment. In other words they overestimate their ability to control the environment. Overconfidence also occurs in investment decision making. Although security selection is such a difficult task and has high risk factors involved in it, people exhibit greatest degree of over confidence in this activity (Nofsinger, 2001). The strange factor about overconfidence is that it increases when the accuracy is least in any decision making matter. In fact it is strange to note that overconfidence decreases as accuracy increases from 50% to 80% and in fact when the accuracy is above 80% people actually become under confident. In brief the gap between the two terms that is overconfidence and accuracy is lowest around the 80% mark of accuracy and increases as the accuracy departs from this level. However it is to be noted that the differences between accuracy and overconfidence is not a factor of the human intelligence. In occasions when people are overconfident, confidence band that are set are narrow. That is in these cases the individuals or the investors set their high guesses too low and their low guesses too high (Shefrin, 2000). Most of the times, the investors are thus surprised more often than they anticipate. There are different factors that affect the overconfidence of the investors. The factors are gender, culture, age, experience, knowledge and occupation. How the above factor does affect the overconfidence  The theory in the subject of overconfidence generally points to the fact that overconfident investors generally trade excessively. Some of the researchers have tested this hypothesis by positioning investment based on generation. The researchers point to the fact through the research that there exists a genuine difference in between investors on the basis of overconfidence and gender impact on overconfidence. The research suggests that male is more overconfident than their female counterpart while it comes to investing (Barber and Odean, 2001). This is documented by the fact that men trade more excessively than women. This excessive trading by men as compared to women decreases their net return by far greater margins that the decrease of the net return of women. The fact that male are more over confident is evidenced by the fact they are more risk taking than women and perhaps it is in their genes In accessing the cultural impact on negotiation it is found through research that was done on people belonging to countries like Taiwan, Japan, and United States (Juslin, Winman and Olsson, 2000). Through their research the researchers concluded that in the Chinese culture overconfidence seems to be extremely strong and the evidence also suggests that overconfidence is weakest among Japanese people. Not only are Chinese more over confident but also they are relatively more inconsistent. This is probably so because the type of reasoning that such type of tasks demand or the thing that one has to make probability judgements is uncommon in the Chinese culture. So their inconsistency is due to these factors as compared to the Japanese or American people who are more accustomed and less overconfident and less inconsistent. Another important factor that is analyzed in the overconfidence perspective is age. The researchers tell that when a trader starts trading he is not sure of his ability. So, in his initial days of trading as the trader starts getting some success he becomes overconfident about him and takes too much credit for his success (Odean and Gervais, 2001). The researchers state that a trader’s confidence level is high in his initial days of trading. As time passes the trader comes to recognize his ability and overconfidence decreases. Thus it can be said that with increase in age and experience overconfidence decreases. Overconfidence in respect of retail investors In case of retail investors it happens most of the time that the investors do not have all the time to analyze all the stocks of all companies. This limited and knowledge on the part of the part of the investors results in being him overconfident. Thus the investors who are overconfident tend under diversify their portfolio and this actually results in greater risk for the retail investors. However this apparent taking of more risk by the investors does not result in more return for him as the risk that the investors’ takes is not based on proper judgement and is not a calculated one. It happens most of the time that when the brain is presented with less amount of information it tries to make predictions that will be applicable to future situation. Most of the time in this assessment of future and in this predictions the individual tends make over estimation of the benefits and lower estimation of the risks involved. This factor leads the investor to face greater loss. However the fact remains that at least initially when the brain is faced with a complex situation the brain tries to make a random guess and almost that guess is based on overconfidence. Traders’ perspective with respect to the Terrence Odean Model Odean’s market model describes the process of how a trader over time achieves the ability to take sound investment decisions and how biases in their perception affect their learning, leading to overconfidence. The model assumes that the trader does not know his ability and his learning based on his experience adds to his confidence. The model analyses the trader’s over-confidence in respect to trade volume, price volatility, expected profit and lower utility and over-confidence (Gigerenzer, 2000). A trader makes investment decisions based on his historic performance i.e. success and failures and market signals. It is observed that with more number of successes, the traders bias increases and builds over-confidence. With the rising success rate the trader engages in more trade, thus increasing the trade volume for that period (Yong, 2013). The trader acts on his bias, which makes him over-confident and engages in aggressive trading. His aggressiveness pulls the trade volume, resulting in increase in price of the security. This influences the price volatility (Gregoriou, 2009). Over time, as the trader moves up the learning curve, he becomes less aggressive and invests optimally, thus his expected profits starts rising over the time horizon and vice versa (Bernstein and Bernstein, 2000). The expected utility is relative to the trader’s level of over-confidence. An in-experienced trader is likely to be more over-confident than an experienced trader. Over-confidence indulges in aggressive trading, reduces the expected profitability and the utility derived from the investment. The converse is also true, as the trader moves up the learning curve, his portfolio has low aggressive index, indicating high expected profit and utility (Baker and Ricciardi, 2014). Traders contribute large amount of shares in the stock market Stock traders proportion in the total trade volume accounts for more than fifty percent in all major stock exchanges. The high frequency trading affects the market equilibrium, resulting in price volatility. Such pattern of trade accounts for 77% in UK stock trades (€3.9 trillion) and 50-70% in US stock trades. The rest share is characterised by retail investors’ contribution (Washington’s Blog, 2015). Figure 1: Stock trade distribution in UK. Figure 2: Stock trade distribution in US The above figures represent the total stock distribution between high frequency trade volume and retail trade volume of UK and US. In both the exhibits stock traders volume accounts for more than 60% of total stock trades compared to the retail investors share. Decision making becomes difficult during high volatility Traders are usually over-confident while taking investment decisions. They use the stock price metrics to forecast the expected price of the security. Owing to their limited scope of market and fundamental analysis, they face difficulty in taking decisions in high volatile conditions. During such conditions, market becomes unpredictable, resulting in poor decision. Traders are hit with such volatility s they invest to earn in the short run. The volatility situation does not recover in the short run, rather it adjusts itself in the long run, thus traders find it acute when faced with such market conditions (systematic or unsystematic) (Klier, 2009). Ed Miliband’s political interference led to the fall of FTSE 100 index. The energy stocks were most hit by the volatility in the Oil sector. Such external event affects the learning bias of traders. Traders were at a risk when the energy stock prices fell. Usually they bet on the uptrend and down trend of stock prices and make gains. The oil sector crisis was an unforeseen event that was beyond the anticipation of the traders, which affected their learning bias/ confidence leading to decision making problems. The situation became acute because the time of such volatility and its stretch could not be accurately predicted (Teall, 2012). Hard Easy effect The hard easy effect as applied to traders when they select security with a given market volatility, explains that traders over estimate the outcome of their investment when faced with high volatility and underestimates the outcome of their investment with low volatility. Traders when faced with high volatility, becomes overconfident which affects their decisions. It further impacts the trading volumes, resulting in new volatility. Thus, there exists a vicious cycle i.e. overconfident traders add to the pre existing volatility. Hard easy effect explains the learning bias of traders under stringent market conditions and how their decision further hits the volatility (Bernstein, and Bernstein, 2000). Consequences for traders behaviour At micro level Cognitive bias indicates a trader’s level of over-confidence in taking investment decisions. Moving on the time horizon reduces the trader’s learning bias and makes him more able to take sound investment decisions. Traders form their decisions based on market knowledge and their past success and failures, it does not consider fundamental analysis, resulting in lower expected profit and increased volatility. Figure 3: Demand for a security as a function of overconfidence The above figure represents the price and quantity demanded of a particular stock, with given levels of overconfidence of two investors. A is D3 with high bias and B is D2 with low bias. They both invest in X plc stock. They both exhibit different demand elasticity with the price fluctuations of X plc. A is highly biased and his decisions are based on his own judgements, whereas B is less bias than A and takes his decision based on market cues. If x plc’s stock price fluctuates B will be the first one to adjust the quantity of the shares. A will react at a later point than B as he believes in his own ability and ignores the market. Thus with change in the stock price, the demand will also be affected based on the given level of overconfidence. Figure 4: Gross and net return with different trading intensities The above figure represents the gross and net returns from portfolios which are classified in 5 different portfolios (groups) based on turnover. Q1 shows a low gross and net return difference Q5 shows a high spread in the gross and net returns from the portfolio. There is a significant negative difference between the gross and net returns from the portfolio across the groups Q1, Q2, Q3, Q4 and Q5. Such differences are attributed to high transaction costs, differential risks and bid ask spreads. The net returns for all investors were below the market rate of return by more than 3%. Out of the total traders 20% of them who have aggressively traded account for a return, which is below the market rate of return by 10%. For example: A and B invest in X plc’s mutual fund with an annual return of 10%, transaction cost of 3% and initial investment of $100 each. Both A and B buys 10 units of X plc with NAV of $10. After a year A buys 20 units more and B buys 30 units more. X plc’s NAV has increased from $10-$12, A’s gross return is $360, transaction cost is $10.2 ( 3%*$100 + 3%*240) and net return is $349.8. B’s gross return is $ 480, transaction cost is $ 13.8 and net return is $ 466.2. Thus, from the above example, it can be concluded that with higher volume of trade, the transaction cost increases and the net return margin falls (Odean and Gervais, 2001). At the macro level Overconfidence is positively related to speculative bubble. Traders’ learning bias leads to a certain level of overconfidence that creates volatility in the market that affects the entire trading community. Though traders are heterogeneous, yet owing to large trade volumes affect the entire investor community. Overconfidence of traders guides their decision to buy/ sell or hold stocks. Owing to their learning bias and aggressive trading practices the security prices artificially increase, like a bubble. The prices of the security soon adjust itself with the market forces of demand and supply, resulting in the bubble burst that affects the entire investor community. Such a situation was witnessed during the dot com bubble in the late ‘90s where investors took advantage by investing in the stocks of internet based companies which drove the growth of the IT industry. The 90’s saw the onset of tech companies, IPO’s grew to 457, mostly IT companies. Out of 457 IPO’s more than 200 companies share price doubled in the initial days. The share price growth was mainly because of bulk purchase by the traders, who anticipated the uptrend and invested in the tech companies. By the end of 2001, the share prices took a hit, 76 IPO’s were made, but the price did not double as experienced in the earlier issue. It was believed that traders who invested in the stocks have booked their gains and moved out of the tech industry. Such an action saw the fall in major IT Company’s share price. The traders’ action could not be justified as the boom phase only lasted for a few years that affected the entire investor community who also invested looking at the potential of the industry (Hooper, 2003). Counter argument Overconfidence at times yields positive return to the investors and traders. At times investors use superior information or their past experience to predict the market. Through proper conviction and learning bias it can have positive returns for the investors. The source of superior information can also be another reason when actual stock performance exceeds the expected performance of the investors/ traders. Such returns will have effect in the short run as it is of a speculative nature (Hooper, 2003). It is observed in earlier situations where such learning bias of traders has resulted in positive outcomes. When access to market information and various analysis metrics are limited, then the investors’ conviction in the choice of security becomes a major determinant in their investment strategy and decision. Though such speculative investment is based on the investment objective of the trader or investor, it is not a healthy way to form decisions as it does not take into account a 360 approach to analysis vis-a-vis technical and fundamental analysis (Sipley, 2010). Conclusion Though there are counter arguments to be speculative about investment decisions, it should not consider it, as it creates a bubble and will affect the entire community and not the speculator alone. Traders as well as investors should not be overconfident about their investment decisions as it does not consider the important market metrics like, industry, company, index and fundamental analysis. Overconfidence eliminates any rational judgements and only factors the quantitative aspects of the investment decision to buy/sell or hold. Though the investment objective is a major factor governing the decision principle i.e. trade or invest, looking for gains in the short run does not maximise the investors/ traders health. Investors should not believe in the myth of good companies are good stocks as they follow a herd mentality and eliminates their own conviction and rationale, leading to low expected returns. The portfolio traders/ investors should diversify their stock portfolio so as to minimise the risk exposure of the entire portfolio. Investors usually invest in single stock with high expectations of capital appreciation. They should include more number of negatively co related stocks in the portfolio, thus ensuring that the loss from one stock does not decrease the overall return of the investor, rather the returns from the good stocks should be used to offset the losses from the bad stocks (Willis, 2011). References Ackert, L., and Deaves, R., 2009. Behavioral Finance: Psychology, Decision-Making, and Markets. Ohio: Cengage Learning. Baker, K., H., and Ricciardi, V., 2014. Investor Behavior: The Psychology of Financial Planning and Investing. New York: John Wiley & Sons. Barber, B. M., and Odean, T. 2001. Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly journal of Economics, pp. 261-292. Bernstein, J., and Jacob Bernstein, J., 2000. The Investors Quotient: The Psychology of Successful Investing in Commodities & Stocks. New York: John Wiley & Sons. Gigerenzer, G., 2000. Adaptive Thinking: Rationality in the Real World. New York: Oxford University Press. Gregoriou, N., G., 2009. Stock Market Volatility. Florida: CRC Press. Hooper, G., 2003. Stock Market Trading: The Event Driven Method. Canada: Trafford Publishing. Juslin, P., Winman, A., and Olsson, H., 2000. Naive empiricism and dogmatism in confidence research: A critical examination of the hard–easy effect. Psychological review, 107(2), pp. 384-396. Klier, O., D., 2009. Managing Diversified Portfolios: What Multi-Business Firms Can Learn from Private Equity. Heidelberg: Springer Science & Business Media. Krueger, I., J., 2008. Rationality and Social Responsibility. New York: Psychology Press. Nofsinger, John R. 2001.Investment Madness: How Psychology Affects Your Investing--and what to Do about it. London: Financial Times Prentice Hall. Odean, T., and Gervais, S., 2001. Learning to be overconfident. The review of financial studies. 14(1). pp.1-27. Shefrin, H., 2000. Beyond greed and fear: Understanding behavioural finance and the psychology of investing. Oxford: Oxford University Press. Sipley, R., 2010. Market Indicators: The Best-Kept Secret to More Effective Trading and Investing. New York: John Wiley and Sons. Teall, L., J., 2012. Financial Trading and Investing. Massachusetts: Academic Press. Todd, M., T., and Gigerenzer, G., 2012. Ecological Rationality: Intelligence in the World. New York: Oxford University Press. Turner, T., 2006. Short-Term Trading in the New Stock Market. New York: Macmillan. Washington’s Blog., 2015. High Frequency Trading Dominates UK Stock Market. [Online] Available at: http://www.washingtonsblog.com/2011/01/high-frequency-trading-dominates-uk-stock-market.html. [Accessed on 14.03.2015] Willis, R., 2011. Cognitive Investing: The Key to Making Better Investment Decisions. Indiana: AuthorHouse. Yong, P., 2013. Behavioural Investing: Understanding the Psychology of Investing. Singapore: Trafford Publishing. Read More