A Review of Factors that influence Equity Premium Literature: A Mini-Review Approach

Iman Lubis; Zairihan Abdul Halim

doi:10.56225/ijfeb.v1i1.2

Authors

Iman Lubis Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
Zairihan Abdul Halim Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

DOI:

https://doi.org/10.56225/ijfeb.v1i1.2

Keywords:

equity premium, economic policy uncertainty, commodity, predictive regression, mini-review approach

Abstract

The equity premium (market risk premium) is one of the most crucial a basis for consideration of asset allocation and is one of the centers of asset pricing. Numerous previous researches have examined the factors that predict the size of the premium equity (excess return risk asset less risk-free assets). The premium equity size is why investors choose risky investments (stocks) over non-risk investments (saving products). This study aims to comprehend the predictor of the equity premium. This study was designed using qualitative approaches by reviewing several relevant pieces of literature. A total of 49 articles were collected from Science Direct, Wiley online library, and Taylor & Francis. The results indicated that oil price negatively affects the equity premium, especially during recessions and gold bars or coins. The economic policy uncertainty and return dispersion have negative relationships in China and others but not in U.S. commodities. Economic indicators have failed to predict equity premium in recession but have power with nonparametric tests in bullish markets. Technical indicators are better than economic indicators for predicting equity premium. The policy implication of this review article is the finding of trends in researching premium equity using predictive regression and structured predictive input that focuses more on the U.S. than on emerging markets, and none of the models have reached past 80 percent. Future research should make models analyze technical indicators and news by adding asymmetry, grouping based on equity and commodity distribution, time and profitability, and dynamic and macro models in emerging markets.

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