Abstract: Using deep learning for stock market predictions and portfolio optimizations is a burgeoning field of research. This study focuses on the stock market dynamics in developing countries, which are often considered less stable than their developed counterparts. The study is structured in two stages. In the first stage, the authors introduce a stacked LSTM model for predicting NIFTY stocks and then rank the stocks based on their predicted returns. In the second stage, the high-return stocks are selected to form 30 different portfolios with six different objectives, each comprising the top 7, 8, 9, and 10 NIFTY stocks. These portfolios are then compared based on risk and returns. Experimental results show that portfolios with five stocks offer the best returns and that adding more than nine stocks to the portfolio leads to excessive diversification and complexity. Therefore, the findings suggest that the proposed two-stage portfolio optimization method has the potential to construct a promising investment strategy, offering a balance between historical and future information on assets.

Ashy Sebastian and Veerta Tantia, “Deep Learning for Stock Price Prediction and Portfolio Optimization” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150995

@article{Sebastian2024,
title = {Deep Learning for Stock Price Prediction and Portfolio Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150995},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150995},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Ashy Sebastian and Veerta Tantia}
}