ABSTRACT
This study attempts to examine the predictability of Google search volume (GSV) and to construct an appropriate investor sentiment index for Islamic stock markets for seven United States (US) Islamic stock indices. Using principal component analysis, we construct an appropriate investor sentiment index for Islamic stock markets that depicts more persistent and higher R-squared values for all these seven US Islamic stocks indices compared to the original Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index of Da, Engelberg, and Gao (2015). The observed results can be attributed to the construction of our investor sentiment index as we have included keywords active in the Islamic stock markets. The findings of this study provide strong predictability evidence for our new sentiment index in the Islamic stock markets.
Notes
1 The Dow Jones system, for example, identifies the following business activities as inappropriate for Islamic investments: Tobacco, Life Insurance, Restaurants & Bars, Broadcasting & Entertainment, Media Agencies, Food Products, Recreational Services, Defence, Distillers & Vintners, Mortgage Finance, Food Retailers & Wholesalers, Consumer Finance, Recreational Products, Specialty Finance, Brewers, Gambling, Hotels, Banks, Full Line Insurance, Insurance Brokers, Property & Casualty Insurance, Reinsurance and Investment Services.
2 Total debt to market capitalisation, accounts receivables to market capitalisation, and cash and interest-bearing securities to market capitalisation should all be 33% of the 24-month average trailing market capitalisation (Narayan et al., 2016).
3 Liquidity (Bank, Larch, & Peter, 2011; Aouodi, Arouri, & Roubaud, 2018), Stock returns (Da et al., Citation2011, Citation2015; Joseph, Wintoki, & Zhang, Citation2011), Volatility (Da et al., Citation2015; Hamid & Heiden, 2015), Earnings Announcement (Drake, Roulstone, & Thornock, 2012; Wang, Choe, & Siraj, 2018), IPO (Da et al., Citation2011; Zhao, Xiong, & Shen, 2018).
4 Other studies include the oil market (Li, Ma, Wang, & Zhang, 2015), tourism market (Siliverstovs & Wochner, 2018; Sun et al., 2019), and stock market (Vlastakis & Markellos, Citation2012).
5 For further detail, see Yao, Zhang, and Ma (2017).
6 Though not reported here, the results from the forecast evaluation did not differ qualitatively when lag 1, 2, 3, and 4 of the FEARS15 and FEARS30 were used to forecast both unconditional and conditional volatility.
7 All the forecast estimates were static.