Disposition bias for different investor categories in Borsa Istanbul

Abstract

Financial theory based many of its theoretical models on the rationality of investors which was challenged by behavioral finance since two decades. Disposition bias is among the many biases that investors face with and behaving against rationality assumption. Having its base from Tsversky and Kahneman (1979) prospect theory, in which the individuals are assumed to be loss averse, Shefrin and Statman (1985) named this loss aversion as "Disposition bias" for the behaviors of the investors, basically refer to the tendency to sell the investment held for a loss at a slower rate than the investment held for a gain. Related with our study, it is important to explain the human psychology on positive and negative outcomes. It is found that we approach positive occurrences differently from negative ones. One way or another we overweigh the positive circumstances and underweigh the negative ones and shape our daily choices based on this subjective evaluation. This discrepancy leads to irrational choices and behaviors which is the main issue in coping mechanisms of human beings. This irrational behavior finds its projection on finance through disposition bias. If an investor is exposed to disposition bias, she behaves differently when faced with loser portfolio compared to winner portfolio. The psychology of this two different choices was analyzed by Shefrin and Statman (1986) with prospect theory of Kahneman and Tversky (1979). This theory suggested that investors are risk takers when a loss is occurred and refrain from risk when a gain is certain. Weber and Camerer (1998), Anaert et al. (2008), and Lee et al. (2008) checked it through either experiments, simulations and other kinds of statistical analysis and found statistically significant results favoring the prospect theory, whereas Odean (1998), Jiao (2017), and Barberis and Xiong (2009) found mixed results in favoring this theory and many other such as Kaustica (2010), Hens and Vlcek (2011), Ben-David and Hirshleifer (2012), and Kubinska et al. (2012) found no statistically significant results favoring prospect theory. Other than prospect theory, the disposition bias was tried to be explained through mental accounting and regret aversion by Brown et al. (2006), Dhar and Zhu (2006), Kaustica (2010), Goo et al. (2010), and Rau (2015), Self-attribution Bias by Barber et al. (2007), Direct Causal Effect of Emotions by Summers and Duxbury (2012), Aspara and Hoffmann (2015), Garling et al. (2016), and Chang et al. (2016) but again the results from all these studies are mixed. The main results for the disposition bias analysis was that investors are mainly less willing to realize losses than gains. Disposition bias not only was analyzed with respect to general investor groups, it has been analyzed with many aspects from different investors types, different cultures, to the effects of the disposition bias on the stock prices, wealth of investors, and other behavioral biases. In studying disposition on different behaviors of investor groups, it was hypothesized that when one can find the difference among groups, then one can come up with a better explanatory idea on the reasoning behind the DB. Grindblatt and Keloharju (2001), Shu et al. (2005), Lehenkari and Perttunen (2005), Barbet et al. (2007)., Chen et al. (2007), Boolell-Gunesh (2009), Goo et al. (2010), and Frino et al. (2014) and many others analyzed the effect of gender and/or age on DB, Shapira and Venezia (2001), Shu et al. (2005), Lehenkari and Perttunen (2004) Dhar and Zhu (2006), Weber and Welfens (2008), Boolell-Gunesh (2009), Choe and Eom (2010), and others analyzed the effect of sophistication on DB. However, even if there are many studies on DB the results are mixed and neither the literature could come up with a definite result on the reasoning behind the disposition bias nor the effects of the disposition bias on different groups. Seeing this gap in the literature in our paper we constructed a methodology on analyzing disposition bias through subgroups of investors with newly defined proxies. Our aim is to understand the reasoning behind the differences of the size of disposition bias. For example, we know from literature that not all women or men are exposed to disposition bias but we do not know the determinant of this difference. We know that institutional investors are less exposed to DB but we cannot generalize this to all institutional investors. If we can understand the reasoning behind this difference we can come up with important policy recommendation to reduce this bias, and reduce the wealth reduction effect of DB on investors. Motivated by the above arguments, we perform an analysis on the existence and equivalence of disposition bias across investors. Our main research questions are as follows. Do investors have a disposition bias when grouped in terms of their type, i.e. their gender or status (male, female and legal person); size (small, medium sized and large) and trading frequency (infrequently, occasionally and frequently trading)? Is the disposition effect the same across different groups as well as subgroups, i.e. when their different features are jointly considered? To answer those questions we used an improved methodology on the classification of the subgroups. This was an important gap in disposition bias literature, because there were different proxies on trading size and trading frequency. For instance, when classifying for size, many researchers such as Grindblatt and Keloharju (2001), Brown et al. (2006), Dhar and Zhu (2006), Weber and Welfens (2008) refer to investors' overall portfolio value or asset value (i.e., the value of their portfolio at an instant or an average portfolio value over a time period). Yet, these overall or average values have some limitations in capturing the true size of investors. Similarly, trading frequency is usually measured with average number of trades for a time period as of Lehenkari and Perttunen (2005), Dhar and Zhu (2006), Chen et al. (2007) and others. Indeed, if an investor trades actively at some period but less in others, this means that he/she always keeps a potential to trade actively. An average number, therefore, does not necessarily reflect his/her attitude. Based on this idea we developed new proxies for both trading size and trading frequency benefiting from the intraday investor base data. We calculated the disposition effect both base on numbers and values as of Barber et al. (2007) where many other studies preferred to make their analysis based on number of DB's. Last but not least we are the first paper which makes the comparative analysis of the group of investors through ANOVA and Tukey HSD test. Our study contributes to the literature in the following ways. Most studies consider a single characteristic of investors in terms of gender, size or trading frequency (e.g., female, small or frequently trading) neglecting joint features such as 'frequently trading small female' investor. To fill this gap we run base-, two- and three-level analyses, i.e. by combining all the investor features (type, size and trading frequency). To the best of our knowledge, such an analysis is unique in the literature and helps shed more light on the disposition bias in investor subcategories. Moreover, we propose better proxies for investor size and trading frequency that seek capturing investor sophistication at an intraday setting (detailed in the Methodology section). Furthermore, our calculation of disposition effect is based on both the 'value' and the 'number' of paper gains and losses. Last but not least; although behavioral biases, and in particular disposition bias, have been widely studied worldwide, a detailed investigation on Turkish market is still missing, the reason presumably being the lack of investor level data for research. An exception includes Tekçe et al. (2016) that examines the determinants of various biases of individual investors such as age, gender, experience, wealth and location. Our dataset encompasses the whole investor base in the country (we start with a sample of 462,488 investors and end up with 283,913, after extracting noisy data). Hence, we can catch a large portion of investor activity. In addition, the descriptive statistics obtained on a large dataset reflects the distribution and general characteristics of investors in terms of gender or status, size and trading frequency in Borsa Istanbul.