Equity premium prediction and optimal portfolio decision with Bagging

Yin, Anwen

doi:10.1016/j.najef.2020.101274

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…The decline in equity premium expectations from 1951 to 1981 stems from FED policies (Smith, 2017). The economic advantage of the Bagging method proposed is that investors are active in managing their portfolios by short-selling and leveraging at better times (Yin, 2020).…”

Section: Resultsmentioning

confidence: 99%

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

Lubis

Halim

2022

IJFEB

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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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Section: Resultsmentioning

confidence: 99%

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

Lubis

Halim

2022

IJFEB

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“…Recent researches that used the bagging method in finance include applications in bankruptcy prediction (Shi et al, 2009), volatility forecasting in derivatives (Yang et al, 2017), profitability in financial institutions (Erdal & Karahanoğlu, 2016), and market portfolio returns (Jordan et al, 2017). Likewise, Yin (2020) used the bagging approach to forecast the equity premium, outperforming models based on historical mean and soft‐threshold methods such as shrinkage estimators.…”

Section: Bootstrap Aggregation (Bagging)mentioning

confidence: 99%

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

Albuquerque

Peng

Silva

2022

Journal of Forecasting

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This paper discusses the application of ensemble techniques for the prediction of time series, presenting an in‐depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches. We also discuss the theoretical foundations of the ensemble‐based models, presenting measures of model stability and the main aggregation methods to combine the forecasts of the individual models, as well as recommendations for future developments for related research agendas.

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On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran

et al. 2022

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Equity premium prediction and optimal portfolio decision with Bagging

Cited by 5 publications

References 46 publications

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

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

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran

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