Search of Attention in Financial Market

Chong, Terence Tai Leung and Li, Chen (2020): Search of Attention in Financial Market.

Abstract

This study employs correlation coefficients and the factor-augmented vector autoregressive (FAVAR) model to investigate the relationship between the stock market and investors’ sentiment measured by big data. The investors’ sentiment index is constructed from a pool of relative keyword series provided by the Baidu Index. We target two composite stock indices, namely the Hang Seng Index and the Shanghai Composite Index. We first compute the Pearson product-moment correlation coefficient to find the degree of correlation between keywords and composite stock price indices. Then, we apply the FAVAR model to obtain the impulse response of stock price to the investors’ sentiment index. Finally, we examine the leading effects of keywords on stock prices using lagged correlation coefficients. We obtain two main findings. First, a strong correlation exists between investors’ sentiment and composite stock price: Second, before and after the launch of the Shanghai-Hong Kong Stock Connect, the keywords affecting the fluctuation of the Hang Seng Index are different.

• Item Type: MPRA Paper
• Language: English
• Keywords: Baidu Index, Stock Connect
• Subjects: G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading

References

[1]. Bernanke B S, Boivin J, Eliasz P. (2005). Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics, 120(1): 387-422. [2]. Brynjolfsson E, Hu Y J, Rahman M S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4): 23-29. [3]. Challet D, Ayed A B H. (2014). Do Google Trend data contain more predictability than price returns?. arXiv preprint arXiv:1403.1715. [4]. Choi H, Varian H. (2012). Predicting the present with Google Trends. Economic Record, 88(s1): 2-9. [5]. Da, Z, Engelberg J, Gao P. (2011). In search of attention. The Journal of Finance, 66(5): 1461-1499. [6]. Fernald J G, Spiegel M M, Swanson E T. (2014). Monetary policy effectiveness in China: Evidence from a FAVAR model. Journal of International Money and Finance, 49: 83-103. [7]. He Q, Leung P H, Chong T T L. (2013). Factor-augmented VAR analysis of the monetary policy in China. China Economic Review, 25: 88-104. [8]. Li H, Yang S. (2013). Application of Linear Regression Analysis Model in Stock Investment. Mathematical Computation, 2(2): 36-39. [9]. Liu L X, Shu H, Wei K C J. (2017). The impacts of political uncertainty on asset prices: Evidence from the Bo scandal in China. Journal of Financial Economics, 125(2): 286-310. [10]. Luong T A, Shi C M, Wang Z. (2019). The impact of media on trade: Evidence from the 2008 China milk contamination scandal. Available at SSRN 3164244. [11]. Preis T, Moat H S, Stanley H E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3(1684): 1-6. [12]. Vosen S, Schmidt T. (2011). Forecasting private consumption: Survey‐based indicators vs. Google trends. Journal of Forecasting, 30(6): 565-578. [13]. Wu L, Brynjolfsson E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales//Economic analysis of the digital economy. University of Chicago Press, 89-118. [14]. Xu S Y. (2014). Stock price forecasting using information from Yahoo Finance and Google trend. UC Berkeley. [15]. Liu Y, Lv B, Peng G (2011). Predictive power of Internet search data for stock market: A theoretical analysis and empirical test. Economic Management, 33(1):172-180.