Equity return predictability, its determinants, and profitable trading strategies

Abstract

This paper explains cross-market variations in the degree of return predictability using the extreme bounds analysis (EBA). The EBA addresses model uncertainty in identifying robust determinant(s) of cross-sectional return predictability. Additionally, the paper develops two profitable trading strategies based on return predictability evidence. The result reveals that among the 13 determinants of the cross-sectional variation of return predictability, only value of stock traded (a measure of liquidity) is found to have robust explanatory power by Leamer's (1985) EBA. However, Sala-i-Martin's (1997) EBA reports that value of stock traded, gross domestic product (GDP) per capita, level of information and communication technology (ICT) development, governance quality, and corruption perception are robust determinants. We further find that a strategy of buying (selling) aggregate market portfolios of the countries with the highest positive (negative) return predictability statistic in the past 24 months generates statistically significant positive returns in the subsequent 3 to 12 months. In the individual country level, a trading rule of buying (selling) the respective country's aggregate market portfolio, when the return predictability statistic turns out positive (negative), outperforms the conventional buy-and-hold strategy for many countries.

Analysis of the relevance of sentiment data for the prediction of excess returns in a multiasset framework

“…The world of finance and economics is multifactorial. Including other data such as commodity prices (Black et al, 2014), the geographic location of companies (Boubaker et al, 2019), business cycles (Liu et al, 2021), information on equity block trades (Kurek, 2014(Kurek, , 2016, and other derived economic data (Baetje, 2018;Cenesizoglu et al, 2019;Rahman et al, 2021) would help predictions. Furthermore, it soon became apparent that including extrafinancial data in order to quantify certain intangibles (such as how the public feels about a stock, or how environmentally sound a company's activity is) has its place in statistical models.…”

Liquidity and short-run predictability: Evidence from international stock markets

“…Still, there is as yet no comprehensive study of the role of illiquidity in predictability in international markets. Studies of the determinants of predictability and efficiency often cover international markets but rarely include specific liquidity measures (e.g., Ben Rejeb & Boughrara, 2013;Rahman, Khan, Vigne, & Uddin, 2021;Shansuddin & Kim, 2010).…”