Novel approach for deep learning-based market forecasting and portfolio selection incorporating market efficiency

Abstract

Efficient portfolio construction remains a fundamental challenge for investors, especially in market environments that are constantly changing and uncertain. Although existing portfolio optimization models such as the Black-Litterman framework incorporate predictive views, they generally do not account for the varying levels of market efficiency, which can influence the reliability of those views. To address this limitation, we design a new portfolio construction method that explicitly incorporates market efficiency. We propose a novel framework that adjusts the uncertainty of predictive views according to market efficiency levels. Using this framework, we reconstruct the Black-Litterman portfolio and confirm its potential to enhance returns. Utilizing actual data from the past decade, deep learning algorithms have performed better in volatile or inefficient markets. Additionally, by reflecting prediction uncertainty through market efficiency derived from stationary return series, we develop a portfolio that significantly outperforms the benchmarks, including the traditional Markowitz portfolio and the standard Black-Litterman model without market efficiency adjustments. Our approach minimizes losses and maximizes returns across various market conditions. Consequently, this strategy is suitable for pension funds and institutional investors seeking long-term growth and risk management.