THE ROLE OF INVESTOR SENTIMENT IN EQUITY PRICING AT VIETNAMESE STOCKS MARKET45401

THE ROLE OF INVESTOR SENTIMENT IN EQUITY PRICING AT VIETNAMESE STOCKS MARKET Vu Thi Loan*, Dinh Thi Hai Yen, Cu Huy Nam, Hoang Dinh Khanh University of Economics and Business - VNU ABSTR

THE ROLE OF INVESTOR SENTIMENT IN EQUITY PRICING

AT VIETNAMESE STOCKS MARKET

Vu Thi Loan*, Dinh Thi Hai Yen, Cu Huy Nam, Hoang Dinh Khanh

University of Economics and Business - VNU

ABSTRACT

This research is conducted to explore whether the investor sentiment has ability

to predict the future returns of the stocks trading in Vietnamese securities market The market turnover is selected to be a proxy of the investor sentiment The capital asset pricing model (CAPM) is estimated to provide the intrinsic value

of the stock returns using daily data of VN-index, 10-year Government Bond interest rate, and returns of 5 stocks in Midcap list Using 1,300 observations, this article recognizes the role of sentiment in explaining the difference between actual returns and intrinsic values computed from CAPM Therefore, it is recommended that investor sentiment should not be overlooked in an equity pricing model in order to obtain more reliable outcome

Keywords: investor sentiment, CAPM, market turnover

1 INTRODUCTION

The Efficient Market Hypothesis (EMH) of standard financial theory suggests that the financial market is “informationally efficient,” and rational arbitrage would eliminate irrational effect on asset prices and necessarily brings prices closer to fundamentals However, since the 1970s, many investor abnormal behavior and financial market’s anomalies, which are thought as EMH paradoxes, have begun to emerge At the same time, behavioral asset pricing theory gradually starts to form as a complement to the traditional asset pricing theory According to the irrational form, investors in the actual financial market may be affected by noise, cognitive biases, or investor sentiment Review of psychology literature shows that sentiment can affect market participants’ judgment of future events Johnson and Tversky (1983) provided evidence that people who read sad newspaper articles strongly perceive that bad events have a higher

* Corresponding author

Email address: loanvu.kttn@gmail.com

likelihood to occur than do people who read pleasant newspaper articles People with positive sentiment make optimistic judgments and choices, whereas people with negative sentiment make pessimistic ones (Ark et al.,1988; Bower, 1991), Wright and Bower (1992) Moreover, excessively optimistic or pessimistic beliefs among individual investors induced by sentiment are expected to trigger “noise trading” Therefore,

if investor sentiment plays a critical role in investors’ decision making, incorporating

it into the modeling specification could potentially help to better describe stock price behavior The presence of such sentiment implies that standard asset pricing models, such as the CAPM, overestimate or underestimate the equity risk premium, depending on investors’ relative optimism or pessimism

This article aims to explore the impact sentiment of investors trading at Vietnam securities market on their expectation of stock’s returns The intrinsic values of the stocks are supposed to create from the traditional CAPM The residual values recognized by analyzing CAPM are supposed to be originated by sentiment factor This assumption will be tested by estimating the significant relationship between investor sentiment using trading turnover as proxy and the residual values taken from CAPM

2 LITERATURE REVIEW

2.1 Sentiment recognition and measurement

Behavioral theories predicate that investors may form erroneous stochastic beliefs, either with excessive optimism or pessimism, and therefore incorrectly evaluate asset values, causing asset prices to deviate from their intrinsic values (De Long et al.,1990; Lee et al.,1991; Kumar and Lee, 2006) In early contributions, sentiment was linked

to speculative bubbles (Smidt, 1968), biased expectations (Zweig, 1973), and noise (Black, 1986) As sentiment is related with different attributes, there is no universal definition accepted by literature According to De Long et al (1990), sentiment is investors’ formation of beliefs about future cash flows and investment risks that are not justified by existing facts Shleifer (2000) opines that sentiment is not mere uncorrelated random mistakes but reflects the common judgment errors made by

a large number of investors Brown and Cliff (2004) believe that sentiment is the manifestation of the expectations of market participants relative to a benchmark A bullish (bearish) investor expects returns to be above (below) the average In Baker and Wurgler (2006), it is the propensity of investors to speculate which determines waves of optimism and pessimism

Investor sentiment measures can be classified into three categories i.e., direct, indirect, and meta measures, broadly based on how the sentiment measures are collected Direct or survey-based sentiment measures include the Investor’s proxies

in the form of a composite sentiment measure (Clarke & Statman, 1998; Fisher & Statman, 2000; Lee et al., 2002; Baker & Stein, 2004; Brown &Cliff, 2004) Two popular survey-based sentiment measures in the market are Intelligence survey and the American Association of Individual Investors survey The American Association for Individual Investors (AAII) has conducted a sentiment survey by polling a random sample of its members each week since 1987 The respondents are asked whether

they are bullish, bearish, or neutral about the future condition of the stock market

in six months Only subscribers to AAII are eligible to vote and they can only vote once during the survey period As the respondents to this survey are individuals, this can be interpreted as a measure of individual sentiment Investor Intelligence (II) has compiled its sentiment data weekly by categorizing approximately 150 market newsletters since 1964 Newsletters are read and marked starting on Friday each week The results are reported as percent bullish, bearish, or neutral on the following Wednesday Since many of the writers of these newsletters are current or past market professionals, the ratio of bullish to bearish responses compiled by II can be considered as a proxy of institutional investors’ sentiment (Wang et al., 2006)

The second type of measure called “indirect measure” is based on financial indicators which are interpreted in terms of bullish or bearish trend Some researchers (Zweig, 1973; Lee et al, 1991) have used closed end fund discount (CEFD) as sentiment proxy Researchers (Pan and Poteshman, 2006) have also used derivatives data (put call ratio, Volatility Index (VIX), open interest and options premium) as sentiment proxy Brown and Cliff (2004) used various sentiment proxies to analyze the relationship between investor sentiment and stock returns These include: survey data from Investor’s Intelligence; technical indicators such as the ratio of the number

of advancing issues to declining issues; variables that relate to particular types of trading activity such as margin borrowing, the percent change in short interest, and the ratio of specialist’s short sales to total short sales; and many others Specially, sentiment can be computed by 3 formulas The first sentiment is the ratio of the number of advancing issues (ADVt) to declining issues (DECt)

The second indirect indicator can be expressed as:

Where:

HI represents the number of the new highs and LO represents the number of the new lows It measure is designed to capture the relative strength of the market (Brown and Cliff (2004)) The extension of the first measure leads to the ARMS index is a modification of ADV/DEC, which incorporates volumes This measure is the ratio of the number of advances to declines scaled by their respective volumes:

The ARMS measure can be interpreted as the ratio of volume per declining issue to the volume in each advancing issue If the index is greater than one, more trading is taking place in declining issues whilst if it is less than one more volume in advancing stocks outpaces the volume in each declining stock The trading volume can be assessed either by the number of traded issues or by the trading volume where V

𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠2 =𝐿𝐿𝐿𝐿𝐻𝐻𝐻𝐻𝑡𝑡

𝑡𝑡

𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠1 =𝐴𝐴𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡

𝑡𝑡

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡=

𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡

𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣𝑣𝑣𝑣𝑣𝑡𝑡

⁄ 𝐴𝐴𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡

𝐴𝐴𝐷𝐷𝐷𝐷𝑣𝑣𝑣𝑣𝑣𝑣𝑡𝑡

⁄

and T denote respectively the number of traded issues and the trading volume

Baker and Wurgler (2006, 2007) construct a composite index of sentiment based on the first principal component of six variables, namely the closed-end fund discount, New York Stock Exchange (NYSE) turnover, the number and average first-day returns

on Initial Public Offering (IPO), the equity share in new issues, and the dividend premium Baker et al (2012) used four proxies (volatility premium, total number

of IPO, first day IPO returns, and market turnover) to construct investor sentiment indices for six major international stock markets The third category is a combination

of direct and indirect sentiment

2.2 CAPM and its judgments

The capital asset pricing model (CAPM) of William Sharpe (1964) and John Lintner (1965) marks the birth of asset pricing theory (resulting in a Nobel Prize for Sharpe

in 1990) Four decades later, the CAPM is still widely used in applications, such as estimating the cost of capital for firms and evaluating the performance of managed portfolios According to the CAPM modeling approach, the expected return on any traded asset in excess of a risk-free return is proportional to the systematic risk of the asset as measured by its covariance with a market-wide portfolio return:

Where:

R i is the return on stock prices;

R f is the risk-free rate;

R M is the return on the market index;

βi is the measure of relative risk or stock price beta

The underlying message of CAPM is that the only significant factor that matters

is the overall market risk premium (R M - R f ) which can be related to any risky asset

by adjusting it according to the asset’s riskiness relative to the market The key assumptions of CAPM are:

(i) Investors are rational, mean-variance optimizers;

(ii) Investors have homogenous expectations;

(iii) All assets are publicly traded;

(iii) Investors can borrow or lend at a common risk-free rate

The CAPM can be graphically expressed in the form of security market line (SML) The SML shows the trade-off between risk and expected return as a straight line

𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠3 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣𝑣𝑣 =

𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣

⁄ 𝐴𝐴𝐷𝐷𝐷𝐷𝐷𝐷𝑣𝑣

𝐴𝐴𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣

⁄ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠4 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣=

𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣

⁄ 𝐴𝐴𝐷𝐷𝐷𝐷𝐷𝐷𝑣𝑣

𝐴𝐴𝐷𝐷𝐷𝐷𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑣𝑣

⁄

𝑅𝑅𝑖𝑖 = 𝑅𝑅𝑓𝑓+ 𝛽𝛽𝑖𝑖(𝑅𝑅𝑀𝑀− 𝑅𝑅𝑓𝑓)

which intersects the vertical axis at risk-free rate

Early investigations mainly supported the CAPM, but more recent results show that the CAPM is not able to explain the observed returns If other variables, such as book-market ratios, book-market value of a company or price-earnings ratios are included the beta has no significant influence on the observed returns Therefore, it is reasonable

to assume that other factors may as well influence the expected returns CAPM also couldn’t provide a solution as to why rational investors behave so irrationally when markets do not act as efficiently as they are supposed to The assumptions underlying the CAPM are also very restrictive Some restrictions such as the absence

of transaction costs and taxes, unlimited borrowing and lending at the risk free rate and short sales have been lifted by more recent contributions without changing the results significantly From that judgment, the field of “behavioral finance” has evolved that attempts to better understand and explain how emotions and cognitive errors influence investors and the decision-making process

2.3 Related empirical studies

Recently, researchers worldwide try to link the investor sentiment with the traditional CAPM Nicholas & Mobeen (2018) investigate the role of investor sentiment in asset pricing In particular, they explore whether this investor sentiment has the ability to be predicted by the residuals from the capital asset pricing model (CAPM) The analysis makes use of data for S&P500 firms on a daily basis, spanning the period of

1995-2015, as well as certain panel methodological approaches The results suggest that the residuals from the CAPM model gain explanatory power for investor sentiment Piyus and Sanjay (2019) construct investor sentiment indices and evaluate the role

of the sentiment-based factor in asset pricing to explain prominent equity market anomalies such as size, value, and price momentum for India Based on the findings, the asset pricing models, including the more recent Fama French 5 factor model, are not fully able to explain the small firm effect which is captured by sentiment-based factor Lorraine et al (2019) use a daily sentiment composite index constructed from a set of proxies and Generalised Autoregressive Conditional Heteroscedasticity models on the research in South African market over a period spanning July 2002

to June 2018 The results show that there is a significant connection between investor sentiment and stock return volatility which shows that behavioural finance can significantly explain the behaviour of stock returns on the Johannesburg Stock Exchange

There is significantly limited number of related studies taken in Vietnam Phan Thi Nha Truc (2019) constructs sentiment index from Turnover, dividend premium, CEFD and number of IPOS, share volume from 341 listed companies in the period of

2005 and 2017 and presents a relationship between investor sentiment and those variables Nguyen Ngoc Tu Van (2019) examines the impact of investor sentiment on the market index in ASEAN countries and finds that the market index is a function

of investor sentiment computed by CCI (Consumer Confidence Index) and other control varibles Review of literature in taken inside Vietnam shows that the literature

on financial risk pricing has not yet empirically tested whether asset pricing models,

such as the capital asset pricing model (CAPM) are capable of capturing investor sentiments over equity pricing

3 METHODOLOGY

The study aims to investigate the investor sentiment can affect the equity prices by estimating the relationship between investor sentiment and CAPM residual values Daily data is obtained from 5 companies in the midcap group in 52 weeks from 15/10/2018 to 25/11/2019 The number of observations reaches 1,300 The reason for selecting midcap stocks is that their high liquidity with reasonable prices are attractive to individual investors trading at Vietnamese stock market The descriptions

of selected stock are given in Table 1 These are five companies having the highest market capitalization and the highest number of outstanding stocks among the group

Table 1 Description of the sample

Code Full name Number of listed stocks Market capitalization (VND billion)

Source: HOSE

There are two models created in the paper The original CAPM reflecting the relationship between stock returns, risk-free rate and the market returns

(1)

R it is the daily stock return calculated from the difference between stock price in t

and t-1 R f is the interest rate of 10-year Government Bond and R M is the VNINDEX return computed daily in 52 weeks In order to identify the role of market sentiment

to the asset pricing, the residual values from model 1 will be tested in model 2 with the existence of investor sentiment

(2) The investor sentiment is the market turnover in day t-1 as applied in researches taken by Baker and Wurgler (2006, 2007); Baker et al (2012) Data is collected from Thomson Reuters Eikon and analyzed by SPSS 25.0

4 RESULT’S ANALYSIS

4.1 Overview of Vietnamese securities market and VN-Index

The introduction of Vietnamese securities market is marked by the establishment

of Hochiminh City Stock Trading Center on 20 July 2000 In the initial stage of

𝑅𝑅𝑖𝑖𝑖𝑖− 𝑅𝑅𝑓𝑓 = 𝑐𝑐 + 𝛽𝛽𝑖𝑖(𝑅𝑅𝑀𝑀− 𝑅𝑅𝑓𝑓)

𝑒𝑒𝑖𝑖𝑖𝑖 = 𝑎𝑎0+ 𝑎𝑎1𝑠𝑠𝑒𝑒𝑠𝑠𝑠𝑠𝑖𝑖−1+ 𝛾𝛾𝑖𝑖

the securities market, there were only 2 listed companies with total capitalization of

986 billion VND (amounting to 0.28%/GDP in 2000) Up to end of March 2019, Vietnam’s stock market has 686 listed companies and listed investment funds in

2 stock exchanges in Hochiminh City (HOSE) and in Hanoi (HNX) The number of trading accounts is 3.3 million The number of foreign investors increased 2.2 times since Vietnam

The Vietnam Stock Index or VN-Index is a capitalization-weighted index of all the companies listed on the Ho Chi Minh City Stock Exchange The index was created with a base index value of 100 as of July 28, 2000 VN-index is computed by timing the number of listed stocks and their corresponding prices taking account the contributions of the stocks to the index Figure 1 presents the volatility of VN-index

in the studied period

Figure 1 VN-index volatility Source: HOSE statistics

4.2 The model’s analysis results

Table 2 Descriptive statistics of the variables

(%) (%) (%) VNI Market turnover (1,000 VND)

Source SPSS 25.0 analysis

This part demonstrates the analysis results of 2 models: model (1) measures the relationship between daily returns of individual stocks and their systematic risk- beta; model (2) explores the impact of investor sentiment on the stock pricing in the market In the first step, the descriptive statistic of 4 variables in 52 weeks are presented (Table 2) The daily mean of sample stock returns is 0.02% while the mean of daily risk free rate and VN-index (VNI) in the last 52 week is 0.01% and 0.41% respectively

Table 3 describes the analysis of the CAPM where the dependent variable is the difference between daily stock return and the risk-free rate while the independent variable is the market premium As shown in table 2, market premium significantly affects the stock return with coefficient of 0.032

Table 3 Baseline CAPM estimation

Variable Coefficient Std Error t Statistic Sig.

Source SPSS 25.0

The question of whether investor sentiment recognized by the daily trading turnover

of the market has ability to explain the residual values provided from table 1 is answered by running the model with residual values dependent variable and the trade turnover is an independent variable The significance of the model emphasizes the substantial role of the investor sentiment to predict the future values of stock expected returns Specifically, an increase of 1 billion in trading volume can lead to 0.01% increase of difference between actual returns and return computed from the original CAPM in the next day

Table 4 Model with investor sentiment

Source SPSS 25.0

5 CONCLUSION

Originating from the literature finding on the measurement of investor sentiment and its correlation with the stock returns, this research is taken to identify the evidence

of this relationship in a frontier stocks market Using market turnover of the previous day as a proxy for investor sentiment, the research explore its role in the existing of individual return deviation from its intrinsic values Therefore, in the first step, the baseline CAPM is created to recognize the intrinsic values using daily data from 5 stocks in midcap group and VNI as proxy for the market index The residual values

of the model then are regressed with investor sentiment as an independent variable The analysis results show that, investor sentiment should be treated as a priced

factor because of its significant role in making the expected returns of the stocks different which what are estimated by CAPM The implication of this finding is that the investor sentiment should not be underestimated in equity pricing model For future research, more than one proxy for investor sentiment should be tested to emphasize the impact of sentiment on return expectation

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