# Stock Portfolio Optimization Using a Deep Learning LSTM Model

arXiv: https://arxiv.org/abs/2111.04709 · HTML: https://mlsanity.com/paper/2111.04709/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Autoregressive Convolutional Neural Networks for Asynchronous Time Series](https://mlsanity.com/paper/1703.04122/) (2017)
- [Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2009.10819/) (2020)
- [A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing](https://mlsanity.com/paper/1912.07700/) (2019)
- [Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models](https://mlsanity.com/paper/2103.15096/) (2021)
- [Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH](https://mlsanity.com/paper/2105.13898/) (2021)
- [Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model](https://mlsanity.com/paper/2104.06259/) (2021)
- [A Relaxed Optimization Approach for Cardinality-Constrained Portfolio Optimization](https://mlsanity.com/paper/1810.10563/) (2018)

## Built on by (most cited first, 9 of 9)

- [Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks](https://mlsanity.com/paper/2202.02728/) (2022)
- [Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market](https://mlsanity.com/paper/2211.07080/) (2022)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks](https://mlsanity.com/paper/2210.00984/) (2022)
- [Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading](https://mlsanity.com/paper/2410.17212/) (2024)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Portfolio Optimization: A Comparative Study](https://mlsanity.com/paper/2307.05048/) (2023)
- [Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks](https://mlsanity.com/paper/2309.13696/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)

## Abstract (arXiv)

Predicting future stock prices and their movement patterns is a complex problem. Hence, building a portfolio of capital assets using the predicted prices to achieve the optimization between its return and risk is an even more difficult task. This work has carried out an analysis of the time series of the historical prices of the top five stocks from the nine different sectors of the Indian stock market from January 1, 2016, to December 31, 2020. Optimum portfolios are built for each of these sectors. For predicting future stock prices, a long-and-short-term memory (LSTM) model is also designed and fine-tuned. After five months of the portfolio construction, the actual and the predicted returns and risks of each portfolio are computed. The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.