mlsanityData to 14 Oct 2026

Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model

arXiv 2202.02723 ↗First posted 6 Feb 2022q-fin.PM cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 6 Feb 2022.

Builds on

4 of 13 references.

References · 4

  1. 2020Applications of deep learning in stock market prediction: recent progresscited by 51
  2. 2019A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processingcited by 23
  3. 2021Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Modelscited by 15
  4. 2021Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Marketcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1.

5 papers.