ItemConference Paper

Predictive Analytics for Stock Price Forecasting: Machine Learning Techniques in Financial Markets

Title
Predictive Analytics for Stock Price Forecasting: Machine Learning Techniques in Financial Markets
Creator
Sao, Ameet; Verma, Rajesh; Lokannadha, Irala; Nagarajan, S.; Mary, S. Suma Christal; Raj, Infant
Description
Stock market forecasting is significantly challenging because financial data generally exhibits non-linearity, and volatility is highly presented. Traditional methods such as the ARIMA model and NN fail to take a good grasp of intricate and complex temporal patterns in changes related to market trends. By overcoming these limitations, it makes use of LSTM and combines GAN networks. The LSTM exploits the historical stock price data for temporal dependencies, whereas GAN produces realistic synthetic data to augment model training. The Stock Market Dataset was used, and the proposed model was implemented using Python with TensorFlow and PyTorch frameworks. The hybrid LSTM-GAN model resulted in better performance with an RMSE of 0.0125, MAE of 0.0093, and R2 of 0.926, thus outperforming LSTM and traditional forecasting models. This work greatly enhances the accuracy of forecasting, avoids overfitting, and promotes performance in volatile market environments. The results are extremely useful for investors, financial analysts, and trading platforms because they can make better predictions. 2025 IEEE.
Source
International Conference on Intelligent Systems and Computational Networks, ICISCN 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
financial market volatility; generative adversarial networks; LSTM-GAN hybrid model; stock price forecasting; time-series prediction
Coverage
Sao A., Amity University, Rics School of Built Environment, Noida, India; Verma R., Christ (Deemed to be University), School of Business and Management, Ghaziabad, India; Lokannadha I., University of Hyderabad, School of Management Studies, Hyderabad, India; Nagarajan S., Bannari Amman Institute of Technology, Department of School of Management Studies, Erode, India; Mary S.S.C., Panimalar Engineering College, Department of Information Technology, Chennai, India; Raj I., K.Ramakrishnan College of Engineering, Department of Cse, Trichy, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152924-6;
Format
online
Language
English
Type
Conference paper

Citation

Sao, Ameet; Verma, Rajesh; Lokannadha, Irala; Nagarajan, S.; Mary, S. Suma Christal; Raj, Infant, “Predictive Analytics for Stock Price Forecasting: Machine Learning Techniques in Financial Markets,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26046.

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