Equity return predictability, its determinants, and profitable trading strategies

Md Lutfur Rahman, Mahbub Khan, Samuel A Vigne, Gazi Salah Uddin

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)162-186
Number of pages25
JournalJournal of Forecasting
Volume40
Issue number1
Early online date17 Jun 2020
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Extreme bounds analysis
  • Fifty equity markets
  • Return predictability
  • Trading strategies

ASJC Scopus subject areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Modelling and Simulation
  • Strategy and Management
  • Management Science and Operations Research

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