Application of Features and Neural Network to Enhance the Performance of Deep Reinforcement Learning in Portfolio Management

Gu, Fengchen; Jiang, Zhengyong; Su, Jionglong

doi:10.1109/icbda51983.2021.9403044

Cited by 12 publications

(1 citation statement)

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“…Finally, deep reinforcement learning approaches have been extensively used in portfolio optimization [46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: Related Workmentioning

confidence: 99%

Portfolio Optimization: A Comparative Study

Sen,

Dasgupta

2024

Artificial Intelligence

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Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

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“…Finally, deep reinforcement learning approaches have been extensively used in portfolio optimization [46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: Related Workmentioning

confidence: 99%