Impact of the Changes in the Sentiment Index on Market Returns

Topic: Impact of the change in the sentiment index on market return.

Introduction

Stock markets play a crucial role in the economy of any country. The trends of the stock markets, growth or decline, are generally taken as the first predictors of the economic trend of the country, by the global audience. They also show the investor’s confidence in the scope of investment in the economy. Thus, is can be said that the stock markets are an indicator for the change in the economy of the country.

But what indicates the changes in the returns of the stock markets itself? That is what we call the sentiment index. The traditional finance has always assumed that the dissemination of information in the market is complete and absolute and the investors are rationale and thus, the sentiment of the investors is immaterial and does not impact the returns of the market. In simpler words the modern finance theory has been very clear on the fact that it is incorrect to price the sentiment of the investors in the valuation of stocks in the market. Any such sentiment will be quickly done away by means of arbitrage and bring back their value to level where the price is not affected by any sentiment.

The research study wishes to understand is there is a relationship in the sentiment index and the market returns. Upon reaching a conclusion that if there is a relationship in the stock market returns and the sentiment index, then the study wishes to determine what will be the impact of the change in the sentiment index on the stock market returns. Before we move ahead with the research, we need to understand what a sentiment index is.

Behavioural Finance

Recently, a new field of finance has grown which we refer to as Behavioural Finance. Behavioural Finance suggests that anyone, an investor, a seller or a buyer, no one is a completely rational being and we act according to our biases and our perception of the market is a major driving factor in our financial decisions. Thus, going by the theory of behavioural finance we can say that the investors’ sentiments towards the stock market are going to determine the returns of the returns of the stock market. The sentiments of the investors are the factors that lead to the demand of the stocks based on uninformed decisions. As the cost of arbitrage in real markets is high this demand continuously persists.

There have been studies before on the same fact if sentiment of the investors actually lead to and change in the stock market return. There have been conflicting conclusions in these studies and thus the opinion still remains divided. This is precisely the point that the research aims to analyse.

There is another fact that has been ignored in the traditional financial theories which is related to the number of investors. When calculated by number the majority of market participants are individual retail investors who might not have complete information and skill set associated with stock market investments. Thus, their decisions as well, will be based majorly on sentiments instead of market information. This forms one of the prime reasons for the effect of sentiment index on the market returns. When such market participants make purchase decisions based on their feelings then there is a probability of the market securities being mispriced.

Sentiment Index

A sentiment index is a numerical reference to the thinking of the investors in a market around what they perceive about the future cash flows of the markets. It can be used as the confidence or the lack of confidence of the investors in the market and can be proxy for the behaviour of the investors and the influence they have on the market.

Sentiment index is not a very specific term. It is rather vague and ambiguous. There is no exact science to a sentiment index and there are multiple indexes that can be used as a sentiment index or a sentiment indicator. An exact measurement of the sentiment index is yet to be developed for such an index. One simply needs an indicator which shows how bullish or bearish a set of people is towards the market. This should be able to help forecast the future behaviour of the group. Generally this forecast is in a contrarian way. The statement can be better explained by an example. If the sentiment index shows that the investors are getting bearish then it will generally be a contrary signal for the traders that now the market process will start to rise higher instead of getting lower.

Multiple sentiment indices exist which have been referenced as a base sentiment index in multiple studies. The two broad categories of such indices are the consumer behaviour and investor behaviour and their beliefs. An investor needs to understand one important fact that the investor’s sentiment is possibly just one factor which can indicate the movement of the stock markets. It cannot be used as a timing signal for the stock market. In general an indicator might indicate that the prices are going to rise or fall but they do not give hint of the timing of such events.

Types of Sentiment Indicators

There are a few indicators that have been used more as the sentiment index by the market investors and researchers. Following are the example of a few of them:

• CBOE Volatility index (VIX): this is one of the most commonly used index and the market investors generally link this with the fear of investors in the market. A spike in this index is generally because of the purchase of a large amount of put options which indicates that the investors want to protect their portfolios’ value.
• NYSE High/Low Indicator: Investors keep a track of the indicator and compare the new stocks that attain a new 52 week high to the stocks that are at a new 52 week low. If there is a spike in either of the directions then there is bullish or bearish sentiment in the market.
• NYSE Moving Average: generally 200 day moving average is used for this index, where a stock trading above or below the moving average will depict a change in market sentiment.
• Odd-Lot Trading Statistics: this refers to the trade of a stock where the trade size is in an odd lot. This is generally less than 100 for most stocks. An odd lot indicates a trade by a retail investor and a peak on either side will indicate an extreme market sentiment.

Literature Review

There is a large amount of research to empirically show the evidence for relationship between the stock prices and the investors’ sentiments. There have been time series analysis of the relationship between the sentiment indices and stock prices and the conclusion of most of them has been that the current sentiment will be a predictor of lower returns in the future.

In 2000 [1], Fisher and Statman found that the American Association of Individual Investors’ sentiment index, which has been used multiple times as a proxy for the sentiment of individual and small investors, had a negative correlation to the S&P 500 index if the sentiment index was lagged by one month. The same study also found that the Wall Street strategists’ sentiment, which can be used as a proxy for the sentiments of large and institutional investors, also had a negative correlation with the S&P 500 index at the same lag.

The same couple of researchers performed another study in 2003 [2]. This time they wanted to see if there is a relationship between the consumer’s confidence and the stock market returns. The study concluded that there was a statistically significant relationship between the consumers’ confidence and the individual sentiment index. Their results showed that a high consumer confidence indicated low future returns in the indices like S&P 500 and NASDAQ.

In 2004 [3], Brown and Cliff concluded that the sentiment level and its changes where strongly and significantly correlated to the then market return. They had used different proxies for the market sentiment. They tested for a causal relationship for the returns of the stock markets and the sentiment levels and its change. They were the ones to suggest that in a short run the stock market returns could be used as a predictor of the market and individual sentiment.

In 2005 [4], Charoenrook used a different sentiment indicator. It was the University of Michigan Consumer Sentiment Index and it was used to conclude that there was a positive correlation between the excess stock market returns and the consumer sentiments. It was further found that the same sentiments have a negative correlation with the future stock market returns when taken at a one month and a one year lag.

There are also studies which predict that the causality is rather other way and actually the stock market returns could be used as a predictor for the future sentiments. This was reported by Wang, Keswani and Taylor [5] in 2006 using multiple proxies. They reported that the sentiment was affected by the returns and the stock market volatility. In 2009 [6] Canbaş and Kandir also reported the same with their data being from the Turkish Stock Markets and their reports mentions that the stock returns in the past are a good predictor of the investor’s sentiments in the future. In 2009 [7], Schmeling suggested that the causality was rather two way. The previous stock market returns were able to determine the future investors’ sentiments and the historical investors’ sentiments predicted future stock market returns. He used an international pooled analysis for the same.

There has also been investigation about the effect of the sentiment of investors on the different categories of stocks. In 2006 [8], Baker and Wurgler were able to conclude that there will be a much higher impact of individual investors’ sentiment on the stocks which have a high valuation. The same impact was seen for the stocks which were harder for investors to arbitrage. Similarly, in 1991 [9], Lee, Shleifer and Thaler found that in case of small stocks they were generally owned by individual investors and such investors were more likely to trade based on market noise than concrete facts. Thus, these small stacks saw a greater deviation in price with changes in investor sentiments when compared to large stocks.

In 2006 [10], Kumar and Lee found that the individual investors used to purchase and sell the stocks in tandem, i.e. there was a systematic correlation between their buying behaviour. In 2005 [11], Brown and Cliff saw that the sentiment had a much more significant impact on growth stocks when compared to value stocks. This finding was in accordance with the findings of Lee, Shleifer and Thaler.On the contrary, in 2006 [12], Lemmon and Portniaguina were able to provide evidence that the impact of sentiment on value stocks was larger when compared to growth stocks. Hence, there is contradictory evidence supporting multiple notions when investor sentiment is concerned.

Research Methodology and Data

The research aims to study if there is an impact on the stock market returns when the investor’s confidence changes. To further the analysis the time frame of the study will also be important. The impact might not be immediate and may take time to propagate in the stock markets. Thus, the complete aim of the study is to check causal effect of investor sentiment index on stock market returns and detect the lag that is present.

Data

As a proxy to the stock markets S&P 500 has been chosen as the composite index. It will be used for the entirety of the research. As the investor sentiment two indices have been used:

1. CBOE Volatility index: Commonly known as VIX, the index is a fear index and shows if the investors fear a high fluctuation in the stock market returns.
2. AAII Sentiment index: The index is made using a customer survey and the percentage of responders is divided into bull, bear and neutral investors. For the purpose of having a single predictor the bull bear spread will be used as the data.

All three time series are weekly in nature.

Research Methodology

The first aim of the research is to identify if there is a relationship in the sentiment indices and the stock market returns. For that purpose, correlation has been chosen as the suitable statistical method.

In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation [13] is any statistical association, though in common usage it most often refers to how close two variables are to having a linear relationship with each other. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a limited supply product and its price.

As the lag has to be determined as well the, both the indices will be correlated to the following lags of the sentiment Index: no lag, 1 week, 2 week, 4 week, 12 week and 26 Week. The lag that shows the highest correlation coefficient will be chosen as the lag at which the sentiment affects the market.

After that the regression analysis will be performed with both the indices as the factor for the market return, keeping the lag in consideration for the market return. As both the lags can be different, the indices will be advanced by the same amount instead.

In statistical modelling, regression analysis [14] is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modelling and analysing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’). More specifically, regression analysis [15] helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Analysis

Following are the results of the correlation analysis:

	AAII Bull-Bear Spread	VIX
S&P Return	-0.04364	-0.46674
S&P Return 1 Lag	-0.01189	0.172241
S&P Return 2 Lag	0.059463	0.057739
S&P Return 4 Lag	-0.13744	0.019369
S&P Return 12 Lag	-0.08705	-0.01352
S&P Return 26 Lag	-0.07985	0.046984

The direction of the correlation is not of any material importance here and rather the magnitude is essential. We can see that in case of AAII spread the correlation is highest at a lag of 4 weeks. Whereas, in case of VIX, the highest correlation is at 0 lag. Thus, from the correlation we can conclude that fear seeps in quickly whereas the survey sentiments take approximately 1 month to be reflected in the stock market returns.

We now move forward to regression for which the indices are moved instead. The following is the result of the regression analysis:

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.498452
R Square	0.248454
Adjusted R Square	0.240976
Standard Error	0.014659
Observations	204
ANOVA
	df	SS	MS	F	Significance F
Regression	2	0.014278	0.007139	33.22441	3.42E-13
Residual	201	0.04319	0.000215
Total	203	0.057469
	Coefficients	Standard Error	t Stat	P-value
Intercept	0.030141	0.003709	8.126289	4.44E-14
VIX	-0.00189	0.000244	-7.75878	4.24E-13
AAII Bull-Bear Spread	-0.018	0.007926	-2.27111	0.0242

Both the indices together are able to predict approximately 25% of the total deviation in the market return. We can see that both the indices also have a negative coefficient. The coefficient of VIX is much smaller compared to AAII Spread but when we account for the fact that AAII Spread is a difference of percentages then the VIX coefficient is larger. Fear seems to have a larger impact on the market returns than survey sentiments.

Conclusion

Based on the literature survey and the analysis performed in the research, it can be concluded that there is definitely a relationship between both the stock market returns and the investors’ sentiments. Every sentiment has a different time horizon in which it has the maximum impact and there are different categories of stocks that see more impact than others.

The literature remains divided on the extent, time frame and the direction of the impact of sentiments on the stock returns. The analysis showed that the effect of fear was larger and much quick when compared to the survey sentiments. The survey sentiments provide options for all three sentiments of bear, bull and neutral. Thus, it includes an effect of rationale in the impact. It can be said that fear is more related to impulsive decision of the investors and thus, it can be seen quicker than the other.

As the impact coefficients are very small thus, it cannot be used as a timing signal for the movement of the market and doing that will not be advisable. There is scope for more research on a global scare to understand how cultural background of investors around the globe affects the relationship developed in the research above. The time-horizon, and the direction, both can potentially change based on the cultural background.

REFERENCES

1. Fisher, K. L., Statman, M. (2000). Investor sentiment and stock returns. Financial Analysts Journal, 56(2), 16-23.
2. Fisher, K. L., Statman, M. (2003). Consumer confidence and stock returns. Journal of Portfolio Management, 30, 115-127.
3. Brown, G. W., Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
4. Charoenrook, A., (2005). Does Sentiment Matter? Working Paper, Vanderbilt University.
5. Wang, Y. H., Keswani, A., Taylor, S. J. (2006). The relationships between sentiment, returns and volatility. International Journal of Forecasting, 22(1), 109-123.
6. Canbaş, S., Kandır, S. Y. (2009). Investor Sentiment and Stock Returns: Evidence from Turkey. Emerging Markets Finance and Trade, 45(4), 36-52.
7. Schmeling, M. (2009). Investor sentiment and stock returns: Some international evidence. Journal of Empirical Finance, 16, 394-408.
8. Baker, M., Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645–1680.
9. Lee, C., Shliefer, A., Thaler, R. H. (1991). Investor sentiment and the closed-end fund puzzle. The Journal of Finance, 46(1), 75-109.
10. Kumar, A., Lee, C. (2006). Retail investor sentiment and return comovement. The Journal of Finance, 61(5), 2451–2486.
11. Brown, G. W., Cliff, M. T. (2005). Investor Sentiment and Asset Valuation. Journal of Business, 78(2), 405-440.
12. Lemmon, M. Portniaguina, E. (2006). Consumer confidence and asset prices: some empirical evidence. Review of Financial Studies, 19(4), 1499–1529.
13. Cohen, J.; Cohen P.; West, S.G. & Aiken, L.S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Psychology Press. ISBN 978-0-8058-2223-6.
14. David A. Freedman, Statistical Models: Theory and Practice, Cambridge University Press (2005).
15. R. Dennis Cook; Sanford Weisberg Criticism and Influence Analysis in Regression, Sociological Methodology, Vol. 13. (1982), pp. 313–361.