دانلود رایگان مقاله احساسات شادی روزانه توییتر و پیش بینی بازدهی سهام

Abstract

Using a novel investor sentiment proxy extracted from Twitter, this paper investigates whether investor sentiment as expressed in daily happiness has predictive power for stock returns in 10 international stock markets. To account for complex relationships between sentiment and stock returns, a Granger non-causality test in quantiles is used. Our empirical results indicate that the causal relations vary across different quantiles. We observe that the causal relationship from happiness sentiment to stock returns exist only in high quantiles interval. The causal relationship from stock returns to happiness sentiment exists only in the tail area.

4. Concluding remarks

In this paper, we have explored the causal relationship between Twitter's daily happiness sentiment index and stock markets for 10 international markers from the perspective of quantile causality. A key advantage of Granger non-causality in quantile is in its ability to model economic relationships more richly than Granger non-causality in mean. Several interesting conclusions have been concluded. For the Granger causal from happiness sentiment to stock returns, the results show that there are significant differences at different points in the return distribution. In the case of S&P/ASX 200, S&P/TSX, FTSE 100, Hang Seng, and S&P 500, we conclude that happiness sentiment does not Granger cause stock returns. The results correspond to quantile sub-intervals indicate that the significant causality from happiness sentiment to stock returns for τ ∈[0.05, 0.95] derives from upper levels of quantiles. For almost all of markets, there is causal from stock returns to happiness sentiment only for a high or low level of happiness sentiment. Regarding possible policy implications, investor sentiment has no effect on the stock market performance when the market in bear and normal phases. Different markets in the world take different approaches to cope with the effects of social media on stock market volatility.