Salience theory and the cross section of stock returns in emerging market empirical evidence in vietnam

Banking Academy of Vietnam Faculty of Finance - GRADUATION THESIS TOPIC: Salience Theory and The Cross-section of Stock Returns in Emerging Market: Empirical evidence in Vietnam Student name: Gia Tan NGUYEN Class: K21CLCC Student ID: 21A4010507 Instructor: Dr Ngoc Mai TRAN Hanoi, May 2022 Tai ngay!!! Ban co the xoa dong chu nay!!! 17014128986491000000 Banking Academy of Vietnam Faculty of Finance - GRADUATION THESIS TOPIC: Salience Theory and The Cross-section of Stock Returns in Emerging Market: Empirical evidence in Vietnam Student name: Gia Tan NGUYEN Class: K21CLCC Student ID: 21A4010507 Instructor: Dr Ngoc Mai TRAN Hanoi, May 2022 Declaration I certify that the thesis I have presented for examination for the Bachelor degree of the Banking Academy of Vietnam is solely my own work other than where I have clearly indicated that it is the work of others (in which case the extent of any work carried out jointly by me and any others is clearly identified in it) The copyright of this thesis rests with the author Quotation from it is permitted provided that full acknowledgement is made This thesis may not be reproduced without my prior written consent I warrant that this authorization does not, to the best of my belief, infringe the rights of any third party I declare that my thesis consists of 8684 words Signature (full name) Nguyen Gia Tan i Acknowledgements Through the writing of this thesis I have received a great deal of support and assistance I am deeply indebted to my advisor, Dr Tran Ngoc Mai, for her continuous guidance and encouragement Secondly, I am very thankful for my cat – his name is “Meo” He is like my little brother who helps me to reduce stress, anxiety, and boredom during this period I am very happy to include his contribution here Last but not least, I am extremely grateful to my mother, my father I would not be submitting my thesis without their limitless support and reassurance, especially during multiple lockdowns ii Abstract This article aims to test the prediction of salience theory effect that stocks with high salience effect should have low future return in “good” times Consistent with recent empirical evidence, I find that salience theory effect is negatively correlated to future returns Specifically, the difference of six-factor alpha between lowest ST value and highest ST value is 1.64% per month In addition, the coefficient on ST effect in Fama-MacBeth cross-sectional regressions are negative and significant, even after controlling a list of acknowledgement predictor in asset pricing (JEL D03, G11, G12) iii Table of Contents Declaration i Acknowledgements ii Abstract iii List of Tables .v Introduction .1 Literature Review 2.1 Salience theory 2.2 The implications of salience theory and asset pricing 2.2.1 Salience-based asset pricing 2.2.2 Construction of salience theory measure 2.2.3 Control variable 2.3 Empirical research 11 Data .16 Cross-sectional relation between salience and stock returns 18 4.1 Time-series test .18 4.2 Robustness checks of time-series 21 4.3 Fama-MacBeth Firm-level regressions 22 4.4 Impact of limit to arbitrage .25 Mechanism 26 Conclusion 32 References 34 Appendix 37 Appendix A Fama-French Factor Model 37 Appendix B Variable Definitions 38 Appendix C Internship Confirmation 39 iv List of Tables Table Summary statistics 17 Table Decile portfolio analysis .18 Table Characteristics of ST-sorted portfolio 20 Table Robustness check of time-series 22 Table Firm-level Fama-MacBeth regression 24 Table Fama-MacBeth regression: limits to arbitrage 25 Table Firm-level Fama-MacBeth regression 27 Table Firm-level Fama-MacBeth regression 28 Table 9A Fama-MacBeth regressions that vary the degree of probability weighting 30 Table 9B Fama-MacBeth regressions that vary the degree of probability weighting 31 v Introduction A crucial assumption in any model of traditional asset pricing theory is that investors always rationally use all available information to evaluate risky assets (Fama, 1970) Expected utility theory and the standard theory of choice under risk, based on this assumption, have been most used by social scientists to reliably predict the cross-section of stock returns However, a large body of research shows that the decision attitudes to risk can depart from the independence axiom of expected utility (Kahneman & Tversky, 1979; Barberis & Huang, 2008) In an effort to develop nonexpected utility models, Bordalo et al (2012) argue these cognitive biases, then propose new novel theory –“ salience theory”, in which decision marker's attitudes are drawn by the unusual or “salient” attributes of different situations Consequently, decision-makers local thinkers overemphasize the salient attributes, and neglect nonsalient payoffs There is substantial empirical evidence showing that the salience theory has directly derived the predictions, for the cross-section of stock return in the U.S market (Cosemans & Frehen, 2021) and developed & emerging countries (Cakici & Zaremba, 2021) In this study, we examine whether the salience effect also exists in the Vietnam stock market Compared to the U.S market, or developed market, the Vietnam market has its salient features in information disclosure (Craig & Diga, 2002), market efficiency (Rizvi & Arshad, 2014), and institutional arrangement (Nguyen et al., 2021) Our goal is to see if salience models, which account for a variety of decisionmaking theory puzzles, such as Allais paradox, can also help us understand decision makers' attitudes in the Vietnam market Moreover, the Vietnam stock market has been becoming more accessible to foreign investors Examining the salience effect in the cross-section of predictability returns in this market is therefore of growing interests to both academic and investor professionals The empirical results provide strong support for the predictive power of salience theory effect in the cross-sectional of Vietnam stocks First, time-series analyses show that stock with salient upside earn higher return over the next month than salient downside A difference between high portfolio and low portfolio is economically and statistically significant over the sample period 2010-2021 After adjusting for risk, the difference in return are still pronounced, with six-factor alphas of 1.64% By employing Fama-MacBeth regression, the result confirms stronger cross-sectional relation between ST and expected return The effect of ST is even economically and statistically significant, after controlling additional power predictor This finding is consistent with Cosemans & Frehen (2021) who finds a negative relationship between ST effect and future returns In addition, I further study the effect of salience components on the crosssection of capital market, namely salience-weight (SW) and equal-weight (EW) past returns, I explore that salience distortation and salient trading volume is larger economically and statistically significant Through Fama-MacBeth analsys, the result provide the evidence that both salience distortation and salient trading volume negatively correlate to future expected return It is consistent with a hypothesis that stock with high level of salience distortation are lower expected return than stock with low level of salience distortation To ensure the effect of salience theory, I also alter the degree of probability degree of salience My salience theory measure remains larger economically and statistically significant after varying a degree of salience measurement, but it still has little impact on predict ability of salience theory on stock return Specifically, the degree to which salience distorts decision weights, could reduce the effect of salience theory if this degree is near (  → ) It implies that the more we assume the investors are rational, the less predictability of salience effect on asset pricing theory The study is organized as follow Section provides the decision-making puzzles and the implications salience theory on asset pricing Section report the descriptive summary and dataset Section reports time-series analysis and firm-level cross-sectional regressions Section focuses more mechanism of salience theory effect Section concludes Literature Review 2.1 Salience theory In Bordalo et al.’s (2012) model (henceforth BGS), the local thinkers faced with a choice set that consists of two lotteries L1 and L2 The lotteries are defined over a state space S that contain N states Each state s  S occurs with probability  s and lottery Li delivers payoffs xsi in state s I assume the local thinker uses a linear value function v ( x ) = x , where lottery payoffs are evaluated to a reference point of zero Without any salience distortions, the local thinkers evaluate Li as: V ( Li ) =   s v ( xsi ) (1) sS The decision-maker departs from equation (1) by underweighting the lottery's least salient state in S There are two steps in salience distortion First, the lottery payoff Li generate a salience ranking among the states in S Second, based on this salience ranking, the probability in equation (1) is replaced with a transformed, i max lottery-specific decision weight  s Let xs , xs , respectively denote the smallest and largest payoffs in xs , then Bordalo et al (2012) define the salience of state for each lottery is a continuous and bounded function that satisfies three conditions: Ordering: If for states s , s  S , and  xsmin , xsmax  is a subset of  xsmin , xsmax  , then  ( xsi , xs−i )   ( xsi , xs−i ) j Diminishing sensitivity: If xs  for any j = 1, , then for any   ,  ( xsi +  , xs−i +  )   ( xsi , xs−i ) j j Reflection: For any two states s , s  S such that xs , xs  for j = 1, , we have  ( xsi , xs−i )   ( xsi , xs−i ) if and only if  ( − xsi , − xs−i )   ( − xsi , − xs−i ) To measure the salience of the payoff xsi of lottery i in state s , Bordalo et al (2012) propose the function  ( x , xs ) = i s xsi − xs xsi + xs +  (2) where   and x =  i xsi / N , with N denoting the number of lotteries N