Behavioral economics
Habib Morovat; Ali Asghar Salem; Shayan Mohammad Sharifi
Abstract
Several factors influence the growth and development of the stock market. One of these factors is the behavior and performance (investment return) of individual investors. Individual investors are motivated to invest in the stock market for various reasons, such as long-term capital growth, dividends, ...
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Several factors influence the growth and development of the stock market. One of these factors is the behavior and performance (investment return) of individual investors. Individual investors are motivated to invest in the stock market for various reasons, such as long-term capital growth, dividends, and hedging against the decline in purchasing power due to inflation. However, their performance in the market, in addition to general economic and stock market conditions, depends on individual characteristics. In this respect, the present study aimed to examine the significance of demographic, behavioral and investment-related factors on the performance of individual investors in the Tehran Stock Exchange. The demographic factors included age, gender, risk tolerance, patience level, etc. Behavioral biases included factors such as overconfidence and loss aversion, while the investment-related factors encompassed experience, skills, and the frequency of portfolio restructuring. Using a systematic sampling, the study collected the data from 240 questionnaires completed by individual investors in the Tehran Stock Exchange. The ordinal logit model was employed to analyze the data. The results showed that age, gender, and risk tolerance did not significantly affect the performance of individual investors. However, the patience level had a positive and significant effect on performance, with more patient investors achieving higher returns. Overconfidence and loss aversion were found to have a significantly negative effect on performance. Finally, investment experience and skills had a significantly positive effect on the investor performance.IntroductionTraditional finance assumed that investors make rational decisions in the stock market, particularly regarding risk-return trade-offs and utility maximization. However, psychologists have found that human behavior is not as rational as economists assume. Stock market anomalies and empirical research show that investors often act in ways that deviate from rational expectations. These anomalies can be explained by the emerging field of behavioral finance. Behavioral finance explores how psychological factors influence the actions of individuals or groups as investors, analysts, and portfolio managers. It also seeks to understand how emotions and cognitive biases affect the behavior of individual investors (Kengatharan & Kengatharan, 2014).Behavioral biases are defined as systematic errors in judgment. Recent studies have identified over 100 such biases in individual investor behavior. Some researchers refer to these biases as heuristic rules (rules of thumb), while others describe them as beliefs, judgments, or preferences. Psychological factors include heuristic rules or cognitive shortcuts related to information processing, memory errors, emotional and/or motivational influences, and social factors such as family upbringing or cultural norms (Pompian, 2021).The Tehran Stock Exchange (TSE) has been one of the most profitable markets (in terms of nominal yield) in the world in recent years. Compared to other parallel investment markets, it has achieved the highest nominal returns over the past 10 and 5 years. The present study aimed to assess the effect of individual factors on the performance of individual investors in the TSE. These factors were grouped into three general categories: economic–demographic characteristics (e.g., age, gender, education, income level, risk tolerance, and patience level), behavioral biases (e.g., loss aversion and overconfidence), and investment-related characteristics (e.g., investment skills, experience in the stock market, and the frequency of portfolio restructuring. The data was collected in 2021 and 2022. A systematic sample of 240 individual investors was surveyed both in-person and online. Multivariate regression and ordinal logit models were used for data analysis.Materials and MethodsUsing a systematic sampling, the study selected a sample of 240 investors active in the TSE. The data was collected through in-person and online surveys. Multivariate regression was applied to examine the causal relationship between the factors influencing the individual investors’ returns. Since the dependent variable was ordinal, the ordinal logit model was used to conduct the analysis.Results and DiscussionIn order to see if there is a significant relationship between explanatory and dependent variables, we used multiple regression. Since the dependent variable is an ordinal variable, the ordinal logit regression model was used to fit the model. The model fitting results are presented in the table below.Table 1. Model Estimation Results Using the Ordered Logit Model MethodProbabilityOdd ratioCoef*Variables0.3240.7533-0.2833Gender0.4040.9899-0.0101Age0.6331.07690.0760Education0.1591.18040.1659Income0.3790.9627-0.0380Risk tolerance0.0002.8739***1.0557Patience0.0130.7539**-0.2824Overconfidence0.0530.9867*-0.0136Loss aversion0.0031.4317***0.3589Experience0.0281.3268**0.2828Investment skill0.0571.2929*0.2561Stock share0.3290.9408-0.0610Portfolio restructuring0.2300.8534-0.1585Initial investment0.1903Pseudo R2***134.82LR Chi2(8)Source: research calculations based on STATA software *, **, *** Coefficients are significant at 10%, 5%, and 1% levels, respectively.All diagnostic control tests for goodness of fit were conducted, including tests for heteroscedasticity (Breusch-Pagan test), collinearity (VIF test), and parallel regressions across different categories (Brant’s test). The null hypotheses of no heteroscedasticity, no collinearity, and the parallel regression assumption were not rejected. In non-linear models such as the logit model, the parameter sign and p-value provide insights into the direction and significance of the effect of coefficients. However, interpreting results directly on the log-odds scale is often impractical. Similarly, expressing results in terms of odds ratios presents challenges, as odds ratios are frequently misunderstood. One common misconception is treating odds ratios as probabilities, which they are not. For these reasons, it is more meaningful to interpret logit models using predicted probability scale, as this approach aligns better with research questions focused on understanding how covariates affect the probability of an event occurring. Table 2 illustrates how the probability of being placed in each category of the dependent variable (investment performance) changes with variations in explanatory variables (patience, overconfidence, investment experience, and financial literacy)-assuming all other variables are held constant at their mean values. For instance, the probability that the most patient individual (patience = 5) falls into the category with the poorest investment performance (I have lost a lot) is 0.006. In contrast, the probability that the same individual belongs to the category with the best investment performance (I have made a lot of profit) is 0.285.Table 2. Final Effects of Changing Explanatory VariablesInvestment performanceI have lost a lot.I have lost.There is no change in my capital.I have made a profit.I have made a lot of profit. Explanatory variables0.275***0.338***0.278***0.102***0.006**1Patience0.117***0.24***0.385***0.24**0.016***20.044***0.118***0.34***0.451***0.046***30.016***0.048***0.198***0.616***0.122***40.006***0.018**0.87***0.604***0.285***50.057***0.145***0.367***0.293***0.035***1Overconfidence0.074***0.177***0.384***0.336***0.027***20.095***0.211***0.390***0.281***0.020***30.122***0.246***0.383***0.231***0.015***40.155***0.279***0.365***0.187***0.012**50.195**0.308***0.337***0.150***0.009*60.155***0.278***0.365***0.188***0.012**1Investment experience0.114***0.236***0.387***0.245***0.017***20.083***0.192***0.388***0.310***0.024***30.06***0.151***0.371***0.382***0.034***40.004***0.115***0.337***0.455***0.047***50.11***0.231***0.388***0.252***0.017***1Investment skills (financial literacy)0.084***0.194***0.389***0.308***0.023***20.064***0.158***0.375***0.368***0.032***30.048***0.127***0.35***0.430***0.042**40.036***0.1***0.315***0.490***0.056***5Source: Research calculations based on STATA software. *, **, *** Coefficients are significant at 10%, 5%, and 1% levels, respectively.These findings demonstrate that greater patience is associated with higher investment performance. However, the relationship between patience and the likelihood of belonging to specific investment performance categories is non-linear. For highly patient individuals, the probability of being in higher performance categories increases up to the third category and then declines. In contrast, for the variable of overconfidence, the probability of the most overconfident individual (overconfidence = 6) achieving the highest investment performance category (I have made a lot of profit) is only 0.009, which is statistically significant at the 10% level. Meanwhile, the probability of this same individual falling into the lowest performance category (I have lost a lot) is 0.195. Non-linear relationships are evident between all explanatory variables and the predicted probabilities of being in various investment performance categories. This non-linearity underscores the value of analyzing final effects over relying solely on the interpretation of coefficients and odds ratios in logit models.ConclusionThe current study aimed to examine the individual factors influencing the performance of individual investors in the TSE, incorporating new explanatory variables and a new model-fitting technique. The analysis using the ordinal logit model revealed that variables such as age, gender, and risk tolerance do not significantly impact the performance of individual investors. However, patience was found to have a positive and significant effect on investment performance, with more patient individuals achieving higher returns. Conversely, overconfidence and loss aversion exhibited negative and significant effects on performance. Additionally, investment experience and skills were shown to positively and significantly influence investors’ performance. Moreover, the findings indicated that TSE investors are affected by behavioral biases, which have a significantly negative impact on their performance, as well as on the overall efficiency and stability of the market. For example, loss aversion leads investors to hold onto losing investments for prolonged periods, reducing portfolio returns. Similarly, overconfidence causes investors to overestimate their ability to evaluate investment opportunities, often disregarding negative signals that could suggest avoiding or selling certain stocks. The study also demonstrated that investment skills (e.g., financial literacy) had a significantly positive impact on investment performance. In contrast, general literacy and unrelated educational backgrounds showed no significant effect on performance. Overall, individuals with greater investment experience and higher levels of patience tend to achieve better outcomes and earn higher returns in the TSE.
Naser Khiabani; ehsan mohammadian nikpey
Abstract
This study examines the impact of a negative shock-attributed to a systemic risk-on the industrial indexes of the Tehran stock market using daily data form 21 January, 2008 to 22 September, 2017. Using a Vector Autoregressive for Value at Risk (VAR-VaR) and a quantile Impulse-response function that was ...
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This study examines the impact of a negative shock-attributed to a systemic risk-on the industrial indexes of the Tehran stock market using daily data form 21 January, 2008 to 22 September, 2017. Using a Vector Autoregressive for Value at Risk (VAR-VaR) and a quantile Impulse-response function that was newly proposed by White et al 2015, we focus on the tail interdependence between industrial index returns (financial institutions) and the market shock index and show how each industrial stock risks contemporaneously and dynamically response to systemic market shocks. Our finding show that there is a significant volatility spillover from a systemic shock to financial institutions in Tehran stock market. However as expected the magnitude of its impact is not the same for all industrial index risks. For examples, the impact of its shock on the bank and metal industrial volatilities is more sizable compared to its impact on the others. And finally, the stock market index has a strong and persistence tail dependency with the chemical and petroleum product industries.
Reza Talebloo; Moloud Rahmaniani
Abstract
In a risky situation probabilities of states are available.Until recently, normal distribution has been used widely in financial applications for a risky situation. Recent studies have shown that normal distribution is not appropriate for financial data and that simple variance of data as an index of ...
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In a risky situation probabilities of states are available.Until recently, normal distribution has been used widely in financial applications for a risky situation. Recent studies have shown that normal distribution is not appropriate for financial data and that simple variance of data as an index of riskiness is a misleading indicator of riskiness. Aumann-Serrano (2008) introduce a new economic index of riskiness to overcome these problems. In this research we use Aumann-Serrano Index to build an optimal portfolio for 23 major stocks in Tehran Stock Exchange. We compare our results with equally weighted portfolio and sharpe-ratio based portfolio and find that economic index of riskiness outperforms others with a 50.6 percent return.
Javad Torkamani; Ali Hosseini
Volume 8, Issue 29 , February 2007, , Pages 75-92
Abstract
The main objective of this paper is to determine the optimum portfolio of the Tehran Stock Exchange with respect to the Value at Risk (VaR) index. Daily data are on the shares of 30 active companies traded in the Tehran Stock Exchange with daily expected return above 0.4 percent in 2004. Optimum portfolio ...
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The main objective of this paper is to determine the optimum portfolio of the Tehran Stock Exchange with respect to the Value at Risk (VaR) index. Daily data are on the shares of 30 active companies traded in the Tehran Stock Exchange with daily expected return above 0.4 percent in 2004. Optimum portfolio is selected subject to the investors' budget, risk and VaR confidence levels. Results reveal that the higher confidence level of VaR requires more diversified portfolio. Therefore, beginner investors and those with higher degree of risk aversion should diversify their budget among shares of various companies. Also, the level of investment affects the combination of the selected portfolio. Results also show that the Risk-Return trade off are in favor of risk averse investors and the change in time length period can also change the optimal portfolio.
