تأثیر گرایش های احساسی سرمایه گذاران بر درجه نقدشوندگی بازار سهام

27
DOI: 10.22099/JAA.2020.35769.1966
 
Journal of Accounting Advances (JAA)
Journal homepage: www.jaa.shirazu.ac.ir/?lang=en
  
The Impact of Investor Sentiments on Stock Market Liquidity

ABSTRACT
 
Received: 2019-11-24
Accepted: 2020-4-12
  The purpose of this study is to investigate the effect of investors' emotional attitudes on stock market liquidity. To this end, the impact of investor sentiment on stock market liquidity has been investigated using the Dabata et al. (2017) model and using data from 14 countries from 2008 to 2018. The Arms Index was used to measure investor sentiment and for measuring the liquidity, traded Value Index (TV), Amihud Liquidity Index (ILLIQ) and Corwin & Schultz Liquidity Index (HLS) were used. The empirical results show that the high level of market liquidity in the previous year has a positive and significant effect on the level of market liquidity in the current year. Investor sentiment has a positive and significant effect on stock market liquidity. The growth rate of money supply and the growth rate of industrial production also have a positive and significant effect on stock market liquidity, and this year's inflation rate has a negative impact on stock market liquidity. According to the results, investor sentiment in the countries studied has a positive effect on the volume of trading in the stock market and can increase trading volume and liquidity of the stock market. Also, part of the liquidity growth has entered the stock market and has been able to increase the volume of transactions in this market.
    

  1- Introduction
       Active and efficient financial markets are an important feature of developed countries, and most developed countries rely on the stock market to finance companies and manufacturing activities. The capital market provides a mechanism by which to make small savings available to macroeconomic investments based on the optimal allocation of resources.بیشتر ببینیدنمایش موارد کمتر According to the behavioral finance theory, it seems that in addition to macroeconomic and accounting variables, the behavioral factors of investors can also affect the volume of corporate stock trading and stock market liquidity. To this end, this study intends to study the impact of investor sentiment and liquidity on stock market liquidity in Iran and selected countries using the 2008-2018 annual data and dynamic panel data method. In a study (Dbata et al., 2017), the static panel data method was used to estimate the model, whereas due to the dynamic nature of the model, it had to be estimated by the dynamic panel data method. Therefore, in this paper, the dynamic model panel method and generalized torque estimators (SGMM) are used to estimate the research model.
 
2- Hypothesis
       The aim of the study is to examine the impact of investor sentiment on stock market liquidity in Iran and selected countries. Thus research hypotheses developed as follows:
       H. Investor sentiments have a significant positive effect on stock market liquidity.
 
3- Methods
       This study examines the impact of investor sentiment on stock market liquidity in the 2008–2018 period. Due to the dynamic nature of the model, the dynamic panel data method is used and first, a research regression model is proposed and then how to measure its variables is shown.The model data is then extracted from the new software and the model is estimated using Eviews 9.0 software. Before estimating the model, the static variables of the model are examined using single root tests. Because if the model variables are unmanageable, the estimated regressions are fictitious and less reliable. If the data were mana, there would be no problem in the model estimation process. But if the data are nameless, cointegration between the model variables must be investigated to study the existence or absence of a long-run equilibrium relationship between the model variables.T herefore, in this study, the co-ordinal test in the panel data is used for coexistence between variables, if necessary. Also, the test of the model's significance, variance heterogeneity in the disturbance and autocorrelation sentences are presented and its results are shown.
 
4- Results
       Three measures of Transaction Value Index (TV), Amihood Non-Liquidity Index (ILLIQ) and Corvin & Schultz Liquidity Index (HLS) were used to measure liquidity. Three separate models were estimated using dynamic panel data method. The results of the regression model estimation showed that the high level of market liquidity in the previous year had a positive and significant effect on the level of market liquidity in the current year. Because the coefficient of dependent variable is positive and at the significant level of 5%, it is statistically significant. The effect of investor sentiment on stock market liquidity is positive and this effect is statistically significant at 5% level. Money growth rate and industrial production growth rate also had positive effect on stock market liquidity and this effect is statistically significant at 5% level. Also, at 5%  level of significance, inflation rate had negative effect on stock market liquidity and this effect was statistically significant only in two models.
 
5- Conclusion
       Financial markets have an undeniable role in improving volume and economic activity, and as globalization and economic integration take place, the need to liberalize and open up financial markets is increasingly felt. Liquidity of financial markets is also one of the positive features of financial markets and it has several factors. One of the factors affecting the liquidity of financial markets is investor sentiment. Based on the presented findings, the present study investigates the effect of investor sentiment on the level of liquidity of these markets in Iran and selected countries. Based on the model estimation results in the countries under study, in almost all estimation models, it can be seen that investor sentiment has a positive effect on stock market trading volume and this effect is statistically significant at the 5% level of significance. Hence, in the studied period and in the countries concerned, the investors' sentiment variable has been able to increase the volume of trading and liquidity of the stock market. The lasting amount of trading value also had a positive effect on the volume of trading on the stock market, and this effect was statistically significant at the 5% level of significance. This means that the high volume of trades in the previous period will increase investor interest in the stock market and can increase the volume of trades in the next period. Money market growth rate also had a positive effect on stock market volatility, which is statistically significant at 5% significance level. In other words, part of the growth of liquidity in the country has entered the stock market and has been able to increase the volume of transactions in this market. Based on the results of the estimated models, the effect of inflation rate on the stock market turnover was negative and these effects were statistically significant at the 5% level of significance. Because inflation is a return on real assets, and if the inflation rate is high, people would prefer to invest more in real assets. Therefore, high inflation in the study period reduced the volume of trading on the stock market. Only in the third model where the Corvin and Schultz liquidity index was considered as the dependent variable of the model, did inflation have a positive effect on the Corvin and Schultz liquidity index, but this effect was not statistically significant at 5% level. The impact of industrial production growth rate on stock market trading volume was positive and these effects were statistically significant at 5% level. In other words, the high rate of growth of industrial production promises favorable economic conditions in the future and increases the incentive to invest in the stock market. Therefore, the high growth of industrial production has a positive effect on the stock market trading volume. The results of the models estimated in this study are consistent with the results of the study by Dabata et al. (2017) and Hu et al. (2019(.
 
Keywords: Investor Sentiment, Stock Market Liquidity, Behavioral Finance.