STOCK MARKET GRAPH PREDICTION

STOCK MARKET GRAPH PREDICTION

Registration ID: IJNRD_220490

Published ID: IJNRD2405230

DOI: Click Here to Get

M.Aruna Devi , K.S. Mukdhesh Kannapiran , S.Pravin Kumar , M.Porrus Jenix Raja

Stock market prediction, Predictive accuracy, Decision Making Algorithm Finance Investment Strategies, Algorithmic trading

Traditional methods often rely on statistical models and machine learning algorithms to forecast market trends. However, integrating class types into the prediction process can provide a deeper understanding of market behavior and improve prediction accuracy. This paper presents a novel approach to stock market graph prediction by incorporating class types into the analysis. The proposed methodology involves three main steps: data preprocessing, feature extraction, and prediction model development. Firstly, historical stock market data is collected and pre-processed to remove noise and ensure data quality. Next, various class types are identified within the data, such as bull, bear, and sideways markets, using advanced clustering techniques. Features are then extracted from each class type to capture their distinct characteristics and patterns. Based on the extracted features, prediction models are trained using machine learning algorithms such as support vector machines, random forests, and recurrent neural networks. These models are tailored to each class type, allowing for more accurate predictions under different market conditions. Additionally, ensemble techniques are employed to combine the predictions from individual models, further enhancing overall performance. Comparative analysis with traditional methods reveals significant improvements in prediction accuracy, particularly during volatile market periods. Furthermore, the interpretability of the class-based models enables investors to gain valuable insights into market dynamics and make informed decisions. In conclusion, integrating class types into stock market graph prediction offers a promising avenue for enhancing forecasting accuracy and understanding market behavior.

Published Paper   E-Certificate

"STOCK MARKET GRAPH PREDICTION", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 5, page no.c262-c271, May-2024, Available :https://ijnrd.org/papers/IJNRD2405230.pdf

Volume 9 Issue 5, May-2024

Pages : c262-c271

Paper Reg. ID: IJNRD_220490

Published Paper Id: IJNRD2405230

Downloads: 000121185

Research Area: Information Technology 

Country: Tirunelveli, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405230.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405230

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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