Smart Stock Price Prediction Algorithm using RNN variant Long - Short-Term Memory (LSTM) - Interactive Tool

Aug 3, 2026

Stock price prediction supports investors and organizations in making better financial decisions. The research proposes an RNN-based approach using a Long Short-Term Memory (LSTM) model to forecast stock prices from historical market signals such as opening and closing prices, daily highs and lows, and trading volume. A preprocessing pipeline cleans and normalizes data to improve input quality. The model combines LSTM with Gated Recurrent Units (GRUs) to capture complex temporal dependencies in sequential series. Performance is assessed with MAE, MSE, and RMSE, and the work provides an interactive, investor-friendly tool to refine trading strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

1 Introduction

Factors Affecting Stock Market Movement

Mitigating Risk in Investing

Day-Ahead Prediction with RNN-LSTM

Model Validation Methods

Machine Learning in Forecasting

Smart Stock Price Prediction Algorithm using RNN variant Long Short-Term Memory Hemank Jain* SCSE, Galgotias University, Greater Noida, 203201, Uttar Pradesh, India * Corresponding author doi: [https://doi.org/10.21467/proceedings.178.1](https://doi.org/10.21467/proceedings.178.1) ABSTRACT Stock price prediction plays a critical role in helping individuals and organizations make informed financial decisions. This research introduces an innovative model based on Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), designed to forecast stock prices. The model leverages historical stock data, including key indicators such as opening and closing prices, daily highs and lows, and trading volumes. To ensure reliable predictions, the study incorporates a comprehensive preprocessing pipeline. This pipeline handles data cleaning and normalization to prepare the input data for analysis. The core of the model is built on advanced RNN architectures like LSTM and Gated Recurrent Units (GRUs), which are well-suited for capturing complex temporal patterns in sequential data—an essential aspect of accurate stock price forecasting. The model's performance is evaluated using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) . These metrics provide a clear measure of the model’s accuracy and reliability. The study highlights the power of RNNs in stock price prediction and introduces an interactive, user-friendly tool tailored for investors and traders, enabling them to refine their financial strategies and make better decisions. Keywords: Prediction, Validation, Precision, RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) 1 Introduction Predicting the stock market is a notoriously complex task because countless factors influence its movements. Core elements like supply and demand for stocks, broader market trends, the overall health of the economy, and the performance of individual companies all play significant roles. However, these factors do not operate in isolation they interact in dynamic and often unpredictable ways. Adding to the complexity is the emotional and psychological aspect of investing. Investor sentiment, or how people feel about certain stocks or the market, can cause sudden shifts in stock prices. For instance, optimism about a company’s future can drive its stock higher, while fear or uncertainty can trigger sell-offs. Additionally, major news events, whether political, economic, or related to a specific industry, can disrupt the market at any moment, creating volatility that is difficult to foresee. To navigate this uncertainty, investors use a variety of tools and strategies to try to predict future market movements. Fundamental analysis, for example, focuses on studying a company’s financial health, such as its earnings, revenue, and growth potential, to determine whether its stock is a good investment. On the other hand, technical analysis looks at historical price patterns and trends to anticipate future behaviour. While these methods provide valuable insights, they are far from foolproof. The stock market is influenced by countless variables, and even the most sophisticated tools and strategies cannot account for every possible scenario. Unexpected events, like the introduction of groundbreaking technology, sudden regulatory changes, or global crises, can dramatically shift market dynamics in ways no one could have anticipated. Given this inherent unpredictability, it is essential for investors to adopt strategies that help mitigate risk. Diversification, or spreading investments across different asset classes, sectors, and regions, is one of the most effective ways to reduce exposure to any single market event. Risk management also involves setting realistic goals, maintaining discipline during market fluctuations, and avoiding emotional decision-making. Perhaps most importantly, a long-term i

FAQs

What model is used for stock price forecasting in the study?

The study uses a Long Short-Term Memory (LSTM) model, an RNN variant designed for sequential time-series prediction, with architectures including GRUs.

Which input data features are used to train the prediction model?

The model uses historical stock information such as opening and closing prices, daily highs and lows, and trading volume.

How is the model’s performance validated?

Validation includes back testing on historical data, along with techniques such as cross-validation and out-of-sample testing to assess generalization.

Aug 3, 2026

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