ENHANCED INVESTMENT STRATEGIES THROUGH PREDICTIVE ANALYTICS OF STOCK PRICES

Authors

  • B.Sravani , T. Laxmi Sritha , Shireen Fatima, T. Laxmi Prasanna

DOI:

https://doi.org/10.47750/

Keywords:

Recurrent Neural Networks , Sequential Data, Historical Data Analysis

Abstract

Stock market prediction has been an area of interest for economists, investors, and researchers for decades. In India, the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) are among the largest stock exchanges, with daily trading volumes crossing ₹50,000 crore. The primary objective of this study is to leverage Regression and Long Short-Term Memory (LSTM) models for predicting stock prices by analyzing historical data, considering factors like opening price, closing price, high, low, and volume, to 
improve the accuracy of investment strategies. 

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Published

.

Issue

Vol. 15 No. 5 (2024)

Section

Articles