Aug 2026· Journal of Business and Social Sciences Research· 0 citations
TL;DR
The use of AI in stock market forecasting has been analyzed, with special emphasis on the NEPSE, and the necessity of implementing AI in decision-making processes has been emphasized here.
Abstract
In this study, the use of AI in stock market forecasting has been analyzed, with special emphasis on the NEPSE. Stock market forecasting continues to be tough because of nonlinearity and volatility; however, AI methods like machine learning and deep learning have proven very promising in improving the accuracy of stock market forecasts. Literature reviews show the potential of AI methods such as LSTM, GRU, CNN, and Graph Neural Network in predicting stock prices. Although the development of AI in developed countries has revolutionized trading, portfolio management, and risk analysis, its implementation in Nepal is still at a very initial stage and has been only used for academic comparison rather than for actual trading purposes. The necessity of implementing AI in decision-making processes has been emphasized here.
. This paper selects daily data from the S&P 500 from 2013 to 2024 and constructs a binary prediction box for stock market direction using average market return as the core indicator. Logistic regression is used as the baseline method to characterize linear relationships; Long Short-Term Memory (LSTM) is used to model...
Zhe-Yu Ding· Proceedings of the 4th Inter...· 0 citations
Investors generally recognize the usefulness of machine learning in improving prediction accuracy, supporting portfolio management, reducing emotional investment decisions, and enhancing financial analysis, and the integration of advanced machine learning algorithms with traditional financial analysis can substantially...
Devuni Varun Kumar, M. Suresh· International Journal of Sci...· 0 citations
Stock price prediction in emerging markets like Nepal remains a complex and challenging task due to high volatility, limited liquidity, and the influence of investor sentiment. This study investigates the performance of three deep learning models-Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal C...
Paras Mishra· Journal of Business and Soci...· 0 citations
. This study analyses the hypothesis of whether machine learning regression models can forecast the stock returns of stocks in the short term on the basis of historical market data. An integrated pipeline of time-series is run to facilitate uniform preprocessing, construction of features and evaluation of models. The d...
Ning-Xing Deng· Proceedings of the 4th Inter...· 0 citations
It is found that stock markets had become more efficient by being data-driven due to the advent of AI, with proper regulation, ethical AI practices and human oversight to maximize the benefits and minimize pitfalls on certain risks.
Aadi Chandra· International journal of soc...· 0 citations
This study investigates the performance of four forecasting models—ARIMA, XGBoost, Long Short-Term Memory (LSTM), and Transformer)—for Apple Inc. (AAPL) stock price prediction using 20 years of historical daily data from Yahoo Finance.
Qian Cheng· Advances in Economics, Manag...· 0 citations
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