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A Comprehensive Review of Financial Market Forecasting: From Historical Data to Sentiment-based Approaches

by Komal Batool, Mirza Faizan Ahmed, Ubaida Fatima
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 58
Year of Publication: 2025
Authors: Komal Batool, Mirza Faizan Ahmed, Ubaida Fatima
10.5120/ijca2025925965

Komal Batool, Mirza Faizan Ahmed, Ubaida Fatima . A Comprehensive Review of Financial Market Forecasting: From Historical Data to Sentiment-based Approaches. International Journal of Computer Applications. 187, 58 ( Nov 2025), 6-21. DOI=10.5120/ijca2025925965

@article{ 10.5120/ijca2025925965,
author = { Komal Batool, Mirza Faizan Ahmed, Ubaida Fatima },
title = { A Comprehensive Review of Financial Market Forecasting: From Historical Data to Sentiment-based Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 58 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 6-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number58/a-comprehensive-review-of-financial-market-forecasting-from-historical-data-to-sentiment-based-approaches/ },
doi = { 10.5120/ijca2025925965 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:11:20.791926+05:30
%A Komal Batool
%A Mirza Faizan Ahmed
%A Ubaida Fatima
%T A Comprehensive Review of Financial Market Forecasting: From Historical Data to Sentiment-based Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 58
%P 6-21
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Financial market is stochastic in nature. The movement in financial market is random. One of the reasons of this fact is that the market is sensitive to multiple factors. It is autoregressive in nature, which means that it depends on its past values. Other than that, macroeconomic variables like Gross Domestic Product (GDP), interest rate, gold price or currency exchange rate also cause fluctuations in the market. Along with that the market is also sensitive to socio-political events, news, tweets and trends. The objective of this review is to understand the predictivity of financial markets based on different datasets and different training models. This paper describes a detailed review that how much features have been incorporated in order to predict the financial market and discusses the effect on predictivity of a market by changing these factors. The novelty of this paper is that it elaborates the methodologies used for the forecasting of financial market and the optimal features required for efficient prediction. • This review paper provides a comprehensive overview of research conducted on forecasting of financial markets over past 20 years, focusing on datasets and models employed. • The study categorizes forecasting approaches in three main methodologies: statistical modelling based forecasting, machine learning modelling based forecasting and hybrid modelling based forecasting. • This survey aims to identify the factors that are most significant for the forecasting of financial market by categorizing the studies based on datasets: historical dataset, technical dataset and textual dataset.

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Index Terms

Computer Science
Information Sciences

Keywords

Time series data Stock Market Macroeconomic variables Machine Learning models hybrid model hybrid datasets sentiment analysis

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