Ảnh hưởng của tâm lý nhà đầu tư đến chỉ số thị trường chứng khoán: Bằng chứng thực nghiệm tại 6 nước Đông Nam Á

Nội dung text: Ảnh hưởng của tâm lý nhà đầu tư đến chỉ số thị trường chứng khoán: Bằng chứng thực nghiệm tại 6 nước Đông Nam Á

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 INVESTOR SENTIMENT AND STOCK MARKET INDICES: EVIDENCE FROM ASEAN -6 COUNTRIES ẢNH HƯỞNG CỦA TÂM LÝ NHÀ ĐẦU TƯ ĐẾN CHỈ SỐ THỊ TRƯỜNG CHỨNG KHOÁN: BẰNG CHỨNG THỰC NGHIỆM TẠI 6 NƯỚC ĐÔNG NAM Á Nguyễn Phùng Hải Thanh - Phạm Thy Khánh Linh Hồ Minh Hồng - ThS. Nguyễn Ngọc Trâm Đại học Kinh tế quốc dân phunghaithanh29@gmail.com Abstract This paper examines the impact of investor sentiment on the stock market indexes of 6 Asian countries, using data sets from 6 countries from 2010 to 2019 and applying the Random effect model method. This paper found evidence of a positive effect of investor sentiment on stock market index. Research results imply that investors, market participants as well as policymakers need to pay more attention to investor sentiment and its impact on the stock market. Keywords: ASEAN, investor sentiment, stock market index. Tóm tắt Bài viết nghiên cứu vấn đề tác động của tâm lý nhà đầu tư đến chỉ số thị trường chứng khoán của 6 nước châu Á, s ử dụng bộ dữ liệu từ 6 quốc gi a từ năm 2010 đến năm 2019 và áp dụng phương pháp mô hình tác động ngẫu nhiên. Bài viết tìm thấy bằng chứng về tác động tích cực của tâm lý nhà đầu tư (investor sentiment) tới chỉ số chứng khoán . Kết quả nghiên cứu hàm ý rằng nhà đầu tư, các chủ thể tham gia thị trường, và đặc biệt các nhà hoạch định chính sách cần quan tâm nhiều hơn tới tâm lý nhà đầu tư (investor sentiment) và ảnh hưởng của nó tới thị trường chứng khoán. Từ khóa: Asean, tâm lý nhà đầu tư, chỉ số thị trường chứng khoán . 1. Introduction Traditional standard finance rarely considers psychological factors as important compo - nents, which may help to offer a good explanation for stock price behavior. Behavioral finance is a sub-field of financial research, that studies the influence of psychology on the behavior of investors. This field focuses on the fact that not all investors are rational and self-controlled, and are influenced by their own biases. Behavioral finance is becoming more important to finance research because of its explanatory power for market anomalies. Numerous studies on the effect 1140
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 of behavior components find that it plays an important role in affecting stock market volatility and returns. On the other hand, Vietnam, as well as other markets in Southeast Asia are being assessed as containing such opportunities in the coming years. Therefore, the financial market should be focused on the corresponding development. The stock market linkage will create a common play - ground for financial investors in the region. It not only opens up opportunities for investors, se - curities companies to conduct cross-border securities transactions quickly, safely, and efficiently, but also facilitates the Enterprises of ASEAN countries can list shares on each other’s stock ex - changes to raise capital and enhance liquidity. However, it cannot be denied that, even if it is a big playing field, there will be risks. The playing field is large, the number of investors will be very large, so the influence of investor sentiment will also be much greater. Moreover, at present, there are not many studies on the impact of investor sentiment on Southeast Asia’s stock market. Though receiving less attention in comparison to other developed markets, the Southeast Asian region is still considered to have the potential for development in the future. Recognizing this, our research team decided to choose the topic “Investor sentiment and Stock market indexes: evidence from ASEAN-6 countries” (ASEAN-6 is the top 6 economies in the East region South Asia: Indonesia, Malaysia, Thailand, Singapore, Philippines, and Vietnam) to examine whether Investor sentiment factor could explain the volatility and stock return or not, with the desire to give domestic and foreign investors a more specific understanding of the impact of psychological factors, thereby avoiding unfortunate mistakes, manage risks, seizing investment opportunities in the Southeast Asian market, and give recommendations for policymakers in con - trolling the financial system to limit negative effects of investor sentiment in the stock market. 2. Literature review In the world, there are many research papers on the impact of investor sentiment on the stock market. Lee, Jiang, Indro (2002) explored stock market volatility, excess returns, and the role of investor sentiment. The results showed that the excess profit was positively correlated with the change of investor sentiment; However, Brown & Cliff (2004) studied the relationship between investor sentiment and stock market returns in the short term. The results show that sen - timent has little predictability for a stock return shortly. Ho, Chien – Wei (2012) pointed out the positive and negative effects of investor sentiment on asset valuation on the stock market, at the same time; Zhu & Niu (2016) analyzed the mechanism that affects investor sentiment and ac - counting information on stock prices in the stock market. The empirical results show that investor sentiment can change both expected earnings growth and required return, thus affecting stock prices; Investor sentiment in the market finance reflects investor’s attitude to market develop - ments, it can control the stability of the market, especially the financial market. The psychological factor is very complex, highly volatile potential, is one of the factors causing market panic, con - trary to expectations. Most studies on this field utilize the U.S., European data in their tests. For example, Jansen, W. Jos, and Nahuis, J. Niek (2003) found that stock returns and changes in sentiment are positively correlated for nine European countries, with Germany the main exception. Dergiades, T. (2012) found that sentiment embodies significant predictive power concerning stock returns on the U.S. 1141
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 market. Ho, Chien – Wei (2012) explore that the impacts of investor sentiment on stock returns and volatility are country-specific, the study finds high investor sentiment level is followed by a low excess market return for most counties such as the U.S., France, and Italy, with Japan as the only exception where exists a positive sentiment- return relationship, but there is no significant effect for the rest of the sample countries examined (include U.S., European and Asia – Pacific stock markets). In this paper, we attempt to discover the role of investor sentiment in international stock markets in 6 Asian countries during 2010 and 2019. 3. Theoretical framework This study investigates the roles of investor sentiment in international stock markets by examining how the investor sentiment index is related to the stock price index in 6 Southeast Asian countries. According to the EMH, investors, both institutional and individual, are unemotional and rational, cannot beat the market and the market is information efficient. All information about the fundamental value and new announcements is fully reflected in prices. Despite its theoretical success, many researchers argue that investors in general are not fully rational. Even more, Shiller’s (1981) empirical volatility test suggests that the stock market is more fluctuating than the forecasts which following market efficiency. His finding shows that investors and stock prices could be affected by other factors irrelevant to fundamentals. In addition to EMH, there are many classical asset pricing theory that describes risk and return, not only from the past data and firm characteristics but also on exposure to systematic risk (CAPM) or multifactor model (APT, FF ). These models may be more plausible as a rea - sonable model for explaining the average asset returns, a drawback of the approach is that it does not offer a good explain pricing anomalies in the financial market. Roll & Ross’s (1980) test ar - gues that not all investors use risk measurements for their decision-making process. And many factors do not seem to exist entirely in the real market. Being an important field in finance, behavioral finance applies a psychological factor to the study, that focuses on the way investors receive and process information during making their decision. Baker and Wurgler (2006) argue that some anomalies in the stock market can be caused by noise traders which are motivated by something other than fundamental information. Even more, the existence of anomalies implies that either financial markets are inefficient or traditional asset pricing models are inadequately specified. In this paper, we examine whether investor sentiment helps to describe the unexpected volatility and the anomalies in the stock market as a component in model specifications. 4. Methodology 4.1. Data collection In this paper, we use quarterly data for 10 years from 2010 to 2019 of six ASEAN countries (Vietnam, Philippines, Malaysia, Thailand, Indonesia, and Singapore). We decide to use data con - sisting of only six ASEAN countries because of data availability. Investor sentiment data is col - lected from Nielsen quarterly reports, while stock indices and data on macroeconomic factors 1142
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 are collected from the IMF (International Monetary Fund) databases. Our final sample consists of 240 observations. 4.2. Key variables defined 4.2.1. Dependent variable The dependent variable in our model is the stock index. We use six stock indices including the VN-Index of Vietnam, PSE (Philippines Stock Exchange Index) of Philippines, JCI (Jakarta Stock Exchange Composite Index) of Indonesia, SET (Stock Exchange of Thailand SET Index) of Thailand, FTSE Bursa Malaysia KLCI (Kuala Lumpur Composite Index) of Malaysia and STI (Straits Times Singapore Index) of Singapore. 4.2.2. Independent variable We find that the Consumer Confidence Index (CCI) has received attention in previous re - search papers as a potential measure for investor optimism. Research of Fisher and Statman (2002) showed a positive correlation between consumer confidence and investor sentiment (com - piled by the American Association of Individual Investors - AAII) during the period from 1987 to 2000. CCI also reflects consumer sentiment about the current state and prospect in the future of the economy and business cycle, which affects financial markets, especially a large proportion of individual investors. Therefore, this paper will use the Consumer Confidence Index to represent investor sentiment. Data on Consumer Confidence Index for six countries were collected through quarterly re - ports by Nielsen, a company that measures global business performance. Consumer Confidence Index is an adjusted monthly measurement. 4.2.3. Control variable Control variables in this model include 1-Year Government Bond Yield, Consumer Price Index (CPI), and Gross Domestic Product (GDP). The government bond yield is the interest paid on the ownership of a government bond (issued in local and foreign currencies) or in other words, the government bond yield is the interest rate at which the government of the country can borrow. The Consumer Price Index measures the average price of the basket of goods and services pur - chased by a typical consumer. This is an indicator reflecting the trend and volatility of retail prices of consumer goods and services used in the daily life of the population and households. Therefore, it is used to track the change in the cost of living over time. Gross Domestic Product is the basic index to evaluate the economic development of a country or territory, calculated by the market value of all final goods and services produced within a country or territory for a certain time. The relationship between 1-Year Government Bond Yield and the stock price index is ex - pected that when the 1-Year Government Bond Yield increases, the return of securities will de - crease, as concluded by Pesaran & Timmermann (1994). Bulmash & Trivoli (1991) also found that there is a negative relationship between the current US stock price and the Treasury bill rate. According to research by Wongbangpo & Sharma (2002), they believe that CPI is a representative factor for the inflation rate, they also examined the relationship between CPI and stock market index in five Asian countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) and concluded that CPI negatively affects stock market index because higher prices will lead to an 1143
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 increase in production costs and a decrease in future profits. Chatrath (1997) found a negative relationship between stock return and inflation when he examines how the inflation rate affects the performance of the market of India. Omran & Pointon (2001) also studied the relationship between inflation and stock prices of the Egyptian market and they found a negative relationship between them. Research by Ademola (2014) in the Nigerian market has shown that GDP has a positive effect on stock market returns because the higher the GDP growth rate, the more devel - oped the economy and the stock returns will increases. Fama (1981) also found a strong positive correlation between stock returns and GDP. Our variables are presented in the following table 1. Table 1: Variables in the model Variables Construction Source Expectation Dependent Stock index lnRETURN: Natural loga - IMF databases variable return rithm of stock price index of each country Independent Consumer Confi - CCI: Consumer Confidence Nielsen reports Positive Variable dence Index Index (quarterly) Control Government GBY: 1 Year Government IMF databases Negative variable Bond Yield Bond Yield (%) Consumer lnCPI: Natural logarithm of IMF databases Negative Price Index Consumer Price Index (CPI) Gross Domestic lnGDP: Natural logarithm of IMF databases Positive Product Gross Domestic Product (GDP) (Source: Authors’ synthesis) 4.3. Model This research analyzes the data in both cross-sectional and time-series dimensions, so the panel data model is the most suitable regression model. Moreover, this model also shows more diverse information, more degrees of freedom, better performance, and less multicollinearity be - tween variables. For a panel data type regression model, the three most common regression meth - ods are (1) Pooled OLS, (2) Fixed Effects Model (FEM), and (3) Random Effects Model (REM). The Pooled OLS model is the simplest and most rudimentary approach assuming that both the intercept and the angular coefficients do not change with time and space. lnRETURN i,t = β0 + β1CCI i,t + β2GBY i,t + β3lnCPI i,t + β4lnGDP i,t + u i,t (1) However, the above model does not answer the question of whether the market index has changed between countries over time because it grouped all the observations even though coun - tries have differences. This is inconsistent with the fact that each country is a separate entity with its own characteristics, so the Pooled OLS model can lead to consequences such as the occurrence 1144
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 of correlation between random error and independent variable, assumptions about the classical linear regression model are violated, the estimation results are biased and unstable. The Fixed Effects Model (FEM) model fixes this problem by assuming the intercept for each country is different and remains constant over time. lnRETURN i,t = βi + β1i,t CCI i,t + β2i,t GBY i,t + β3i,t lnCPI i,t + β4i,t lnGDP i,t + u i,t (2) Although the disadvantages of the Pooled OLS model have been improved and it is easy to apply, the FEM approach in panel data processing still has some limitations such as small de - grees of freedom if the sample size is small. Therefore, in panel data analysis we can also approach the Random Effects Model (REM) model. From the FEM model, the intercept βi can be expressed as βi = γ1 + εi. Instead of considering the intercept βi of countries fixed effect, we consider this intercept as a random variable with mean γ1 and εi as a random error with mean zero and constant 2 variance σ ε. lnRETURN i,t = γ1 + β1i,t CCI i,t + β2i,t GBY i,t + β3i,t lnCPI i,t + β4i,t lnGDP i,t + u i,t +εi = γ1 + β1i,t CCI i,t + β2i,t GBY i,t + β3i,t lnCPI i,t + β4i,t lnGDP i,t + w i,t (3) To make the decision to choose between three models, this paper will conduct two of the three tests: Hausman Test, Breusch - Pagan Lagrange Multiplier Test, F-test according to the fol - lowing procedure. (Source: Authors’ synthesis) Picture 1: Test process diagram 5. Summary statistics The summary statistics are presented in Table 2, which shows that the variables in the paper have quite small standard deviations, which means the variation is relatively uniform. However, the CCI varied strongly in the range 135 to 78 with a standard deviation of 12,652 because this variable has a large value. 1145
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Table 2: Summary statistics Variables N Mean St. Dev. Min Max Skewness Kurtosis lnRETURN 240 7.711 0.822 5.964 9.056 0.0651 0.0000 CCI 240 109.062 12.652 78 135 0.4430 0.0000 GBY 240 3.576 2.543 0.235 12.869 0.0000 0.0006 lnCPI 240 4.640 0.116 4.209 4.932 0.1447 0.0004 lnGDP 240 4.361 0.538 2.744 5.648 0.2593 0.2104 (Source: Authors’calculation) The correlation matrix in Table 3 shows that the correlation coefficient between the in - dependent variables is not high. This correlation matrix table also shows a preliminary correlation between the dependent variable and the independent variables. While three variables CCI, lnCPI, lnGDP are positively correlated with the lnRETURN, GBY has a negative correlation. This is completely consistent with the fact that when the consumer confidence index increases, it means that consumer’s attitude towards the economy is completely positive, causing the market index to increase. Similar to CPI and GDP, when these two indexes increase, it means that the economy develops well and has a positive impact on the stock price index. When government bond yield increases, investors tend to turn to buy government bonds for safety. Conversely, when the gov - ernment bond yield falls, investors buy a stock for its profits. Table 3: Correlation matrix lnRETURN CCI GBY lnCPI lnGDP lnRETURN 1.0000 CCI 0.4766 1.0000 GBY -0.2401 0.1725 1.0000 lnCPI 0.4797 0.3943 -0.0336 1.0000 lnGDP 0.5471 0.3854 -0.0340 0.6403 1.0000 (Source: Authors’calculation) The Variance Inflation Factor (VIF) of the independent variables in the model is shown in Table 4. Accordingly, all VIF coefficients, including the average VIF are greater than 2, and 1/VIF is also greater than 0.5, so it can be concluded that the model is not suffered from the mul - ticollinearity phenomenon. 1146
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Table 4: Variance Inflation Factor Variables VIF 1/VIF lnCPI 1.78 0.562417 lnGDP 1.76 0.567279 CCI 1.28 0.778937 GBY 1.05 0.954950 Average VIF 1.47 (Source: Authors’calculation) 6. Findings To decide to choose between three models, the study will conduct two tests: the Hausman Test and Breusch-Pagan LM Test. Table 5: Hausman Test and Breusch-Pagan LM Test Hausman Test Breusch-Pagan LM Test Chil 2 0.12 2929.65 Prob > chil 2 0.9982 0.0000 H is true H is true Conclusion 0 1 (choose REM and not (choose REM and not choose FEM) choose Pooled OLS) From the results obtained from the two tests above, it can be concluded that the Random Effects Model is more suitable for this data series than the Fixed Effects Model and the Pooled OLS model. Table 6: Random Effect Model results (Source: Authors’calculation) 1147
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 From the results of Table 6, it can be seen that the model can explain 43.12% of the change of the independent variable, in which the cross-sectional dimension is 51.59% and the time-series dimension is 68.22%. At the same time, from the regression results, we can see that the coefficient of CCI is 0.0069127 and is statistically significant at 1%, showing that CCI has a positive influ - ence on lnRETURN. Specifically, when the consumer confidence index increased by 1%, the logarithm of the stock price index increased by an average of 0.0069%. Besides, the impact of the control variables GBY and lnGDP is consistent with previous literature. As government bond yields go up, the stock market index drops, and the higher the GDP growth rate the better for the stock market and stock prices. However, the effect of lnCPI is contradictory to the result that CPI negatively affects stock prices of previous studies. An in - crease in CPI will directly increase input costs, which in turn increases production costs and output prices. This reduces business profits and stock returns. The securities of businesses also become less attractive, which means a decrease in investment in the stock market. However, there are also studies with similar results, as according to Boudoukh and Richardson (1993), Graham (1996), Choudhry (1999), Naik and Padhi (2012), Adusei (2014) all conclude that the relationship between CPI and stock prices is positive. And some studies from Hardouvelis (1988) and Pearce & Roley (1985) found no clear relationship between Stock prices and inflation. According to the findings of Spyrou (2001), the explanation of the inflation-stock return relationship in the emerg - ing markets of the sample during the 1990s may be was caused primarily by money rather than real activity. It means the main determinant of inflation is not output growth, but an increased money supply. We see the reasonableness in a positive relationship. The time studied is a period of 10 years since 2010, after the global financial crisis 2008, countries begin to develop their economies again by expansionary monetary policy. Moreover, because of thin trading, lower liq - uidity, and possibly less informed and less rational investors than more developed markets, emerg - ing markets might expect differences in equity price behavior than developed equity markets. To check the robustness of our result, we want to test whether investor sentiment in the past can have an impact on the stock market index. To answer this question, we use a lag of CCI as an independent variable. The results are presented in Table 7. Table 7: Regression result with CCI i,t -1 lnRETURN CCIi,t -1 0.0063 GBY -0.0214 lnCPI 0.76 lnGDP 0.2706 constant 2.3947 R2 within 0.6996 R2 between 0.5306 R2 overall 0.4390 Note: *p p p<0.01 (Source: Authors’calculation) 1148
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 From the regression results, it can be seen that the CCI index of the previous period had an impact on the stock price index of the next period with a quite high level of significance. Thus, it can be said that the consumer confidence index not only affects the stock price index but also can be used to make forecasts about the stock market in the future, helping investors to make the best decision. 7. Dicussion Examining the relationship between investor sentiment and the stock market performance has become an important issue in financial research on the financial market. There are many pre - vious studies in the developed market. In this study, we also tried to look at this relationship, by taking the CCI consumer confidence index to represent investor sentiment and examining the ef - fect of the CCI on the emerging and frontier markets (the dataset used in this study consists of 6 ASEAN countries). This result has shown that a change in CCI’s consumer confidence index can explain the stock market price index. Specifically, when the CCI increase in consumer confidence means that investors have an optimistic sentiment about the market, the stock price index will also in - crease accordingly. This can be explained by the fact that positive sentiment can change investors’ expectations about the current state and future cash flows and thus affect stock prices overall. This result gives a good explanation that in many cases, traditional finance cannot give a reason - able explanation. This research results also show that the lag value of CCI has an impact on the stock price index, that is, investor sentiment has an impact on the stock price index. Therefore, the psychological factor can be used to forecast future stock market volatility and prices. Furthermore, the paper examines the interpretability of the macro variables for the stock market index and finds that while government bond yields have negative effects, GDP and CPI have a positive effect on the stock market. 8. Policy recommendation This paper suggests that to boost the sustainable development of the stock market, policy - makers should pay more attention to the sentiment of investors. The government should focus on the increase in the market efficiency of information, which can make individual investors receive more trustful information. Besides, it is necessary to im - prove the knowledge for investors, which can be useful in many situations such as managing risks, allocation assets, and selecting portfolios. The more knowledge they have, the more rational decision they make. We also need to focus on developing institutional investors, because they have investment strategy and experience, which will help stabilize the market and limit shocks. Vietnamese stock exchange should consider setting the sentiment indices in the market such as indicators of The University of Michigan Consumer Sentiment Index, The Conference Board Consumer Confidence Index, The Investors Intelligence Sentiment Index, These Indices can help investors and policymakers to develop and adjust the regulatory policies. State Security Commission of Vietnam should also note the psychological factors in the construction of securi - ties policies and legislation. 1149
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 REFERENCES 1. Brown, Gregory W. 1999. “Volatility, Sentiment, and Noise Traders.” Financial Analysts Journal, vol. 55, no. 2 (March/April). 2. Fisher, K. L., Statman, M. (2003). Consumer confidence and stock returns. Journal of Portfolio Management, 30, 115-127. 3. Lee, C., Shliefer, A., Thaler, R. H. (1991). Investor sentiment and the closed-end fund puzzle. The Journal of Finance, 46(1), 75-109. 4. Schmeling, Maik (2009) “Investor Sentiment and Stock Returns: Some International Evidence” Journal of Empirical Finance, Vol.16, issue 3, 394-408. 5. Lee, Charles M., Andrei Shleifer, and Richard H. Thaler (1991), “Investor Sentiment and the Closed-End Fund Puzzle.” Journal of Finance, vol. 46, no. 1 (March), 75–109. 6. Elton, Edwin J., Martin J. Gruber, and Jeffrey A. Busse (1998), “Do Investors Care about Sentiment?” Journal of Business, vol. 71, no. 4 (October), 477–500. 7. Wang, Y. H., Keswani, A., Taylor, S. J. (2006), “The relationships between sentiment, returns and volatility”, International Journal of Forecasting, 22(1), 109-123 8. Wugler, J., Zhuravskaya, E. (2002), “Does Arbitrage Flatten Demand Curves for Stocks”, Journal of Business, 75(4), 583–608. 9. Canbaş, S., Kandır, S. Y. (2009), “Investor Sentiment and Stock Returns: Evidence from Turkey”, Emerging Markets Finance and Trade, 45(4), 36-52. 10. Baker, M. and Wurgler, J. (2007), “Investor sentiment in the stock market”, Journal of Economic Perspectives, Vol. 21 No. 2, 129-151. 11. Bai, J. (2009), “Panel data models with interactive fixed effects”, Econometrica, Vol. 77 No. 4, 1229-1279. 12. Solt, Michael E., and Meir Statman (1988), “How Useful Is the Sentiment Index?”, Fi - nancial Analysts Journal, vol. 44, no. 5 (September/October) 13. Otoo, M. Ward (1999), “Consumer Sentiment and the Stock Market”, Board of Gov - ernors of the Federal Reserve System, 14. Jansen, W. Jos and Nahuis, J. Niek (2003), “The Stock Market and Consumer Confi - dence: European Evidence”, Economics Letters, Vol.79, 89-98. 15. Brown, W. Gregory and Cliff, T. Michael (2004) “Investor Sentiment and the Near- Term Stock Market”, Journal of Empirical Finance, Vol.11, 1-27. 16. Bremmer, Dale (2008), “Consumer Confidence and Stock Prices”, 72nd Annual Meet - ing of the Midwest Economics Association Hyatt Regency, Chicago, Illinois, 17. Fisher, Kenneth and Statman, Meir (2002), ‘‘Consumer Confidence and Stock Re - turns’’, Working paper, Santa Clara University. 18. Shefrin, H. (2008), “Risk and return in behavioral SDF-based asset pricing models”, Journal of Investment Management, 6 (4), 4–22. 1150
12. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 19. Stambaugh, R., Yu, J., Yuan, Y. (2012), “The short of it: investor sentiment and anom - alies”, Journal of Financial Economics, 104 (2), 288–302. 20. Benhabib, J., Liu, X. and Wang, P. (2016), “Sentiment, financial markets, and macro - economic fluctuations”, Journal of Financial Economics, Vol. 120 No. 2, pp. 420-443. 21. Dergiades, T. (2012), “Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy”, Economics Letters, Vol. 116 No. 3, 404-407. 22. Ho, Chien – Wei (2012), “The role of investor sentiment in Asset Pricing”. 23. Bulmash SB, Trivoli GW (1991), “Time-lagged Interactions between Stock Prices and Selected Economic Variables”, The Journal of Portfolio Management Summer, 17(4), 61-67. 24. PraphanWongbangpo, Subhash C. Sharma (2002), “Stock market and macroeconomic fundamental dynamic interactions: ASEAN-5 countries”, Journal of Asian Economics, Vol.13, 27-51. 25. Chatrath, A., Ramchander, S., & Song, F., (1997), “Stock Prices, Inflation and Output: Evidence from India”, Applied Financial Economics, Vol.7, 439–445. 26. Omran, M., & Pointon, J. (2001), “Does the Inflation Rate affect the Performance of the Stock Market? The case of Egypt”, Emerging Markets Review, Vol2, 263–279. 27. Onabote A. Ademola (2014), “Stock Market and Economic Growth in Nigeria”, Inter - national Journal of English Literature and Social Sciences, Vol.2, 97-106. 28. Eugene F. Fama (1981), “Stock Returns, Real Activity, Inflation, and Money”, The American Economic Review, Vol. 71, No. 4, 545-565. 29. M. Hashem Pesaran, Allan Timmermann (1994), “Forecasting stock returns an exam - ination of stock market trading in the presence of transaction costs”, Journal of Forecasting, Vol - ume13, 335-367. 30. Graham, F. C. (1996) Inflation, real stock returns, and monetary policy, Applied Finan - cial Economics, 6, 29–35. 31. Spyrou, S. I. (2001) Stock returns and inflation: evidence from an emerging market, Applied Economic Letters, 8, 447–50. 32. Hardouvelis, G. A. (1988). The predictive power of the term structure during recent monetary regimes. Journal of Finance. 43(2): 339-356. 33. Choudhry, T. (1999). Inflation and the rates of return on stocks: evidence from high in - flation countries. Journal of International Financial Markets, Institutions and Money.11:75-96. 34. Shiller, Robert J., 1981. “Alternative tests of rational expectations models : The case of the term structure,” Journal of Econometrics, Elsevier, vol. 16(1), pages 71-87, May. 35. Roll, R., & Ross, S. A. (1980). An empirical investigation of the arbitrage pricing theory. The Journal of Finance, 35(5), 1073-1103. 1151