Abstract
Stock portfolio construction is a difficult task which involves the simultaneous consideration of dynamic financial data as well as investment criteria (e.g.: investors required return, risk tolerance, goals, and time frame). The objective of this research is to present a two phase deep learning module to csonstruct a financial stocks portfolio that can be used repeatedly to select the most promising stocks and adjust stocks allocations (namely quantitative trading system). A deep belief network is used to discover the complex regularities among the stocks while a long short-term memory network is used for time series financial data prediction. The proposed deep learning architecture has been tested on the american stock market and has outperformed other known machine learning techniques (support vector machine and random forests) in several prediction accuracy metrices. Furthermore, the results showed that our architecture as a portfolio construction model outperforms three benchmark models with several financial profitability and risk-adjusted metrics.
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Kawy, R.A., Abdelmoez, W.M., Shoukry, A. (2021). Financial Portfolio Construction for Quantitative Trading Using Deep Learning Technique. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_1
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