Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model

• URL: http://arxiv.org/abs/2204.01850v1
• Date: Wed, 2 Mar 2022 14:15:14 GMT
• Title: Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model
• Authors: Jaydip Sen, Saikat Mondal, Gourab Nath
• Abstract summary: This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical economic sectors of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. An LSTM model is also designed for predicting future stock prices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical economic sectors of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Sector-wise portfolios are built based on their ten most significant stocks. An LSTM model is also designed for predicting future stock prices. Six months after the construction of the portfolios, i.e., on Jul 1, 2021, the actual returns and the LSTM-predicted returns for the portfolios are computed. A comparison of the predicted and the actual returns indicate a high accuracy level of the LSTM model.

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