mlsanityData to 7 Oct 2026

Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

arXiv 2203.01326 ↗First posted 2 Mar 2022q-fin.PM cs.LGcited by 4

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Abstract (arXiv)

Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past prices of the stocks are extracted from the web from January 1, 2016, to December 31, 2020. Optimum portfolios are designed on the selected seven sectors. An LSTM regression model is also designed for predicting future stock prices. Five months after the construction of the portfolios, i.e., on June 1, 2021, the actual and predicted returns and risks of each portfolio are computed. The predicted and the actual returns indicate the very high accuracy of the LSTM model.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

3 of 14 references.

References · 3

  1. 2020Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Modelscited by 24
  2. 2019A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processingcited by 23
  3. 2020Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseriescited by 13

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Marketcited by 0
  2. 2023Portfolio Optimization: A Comparative Studycited by 0
  3. 2023Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stockscited by 0
  4. 2024Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learningcited by 0