# HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model

arXiv: https://arxiv.org/abs/2203.08143 · HTML: https://mlsanity.com/paper/2203.08143/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 60 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre](https://mlsanity.com/paper/2107.13502/) (2021)
- [A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions](https://mlsanity.com/paper/2202.11965/) (2022)
- [Exploiting Investors Social Network for Stock Prediction in China's Market](https://mlsanity.com/paper/1801.00597/) (2018)

## Built on by (most cited first, 5 of 5)

- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day](https://mlsanity.com/paper/2203.05835/) (2022)
- [PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification](https://mlsanity.com/paper/2203.11287/) (2022)
- [A smart resource management mechanism with trust access control for cloud computing environment](https://mlsanity.com/paper/2212.05319/) (2022)
- [H3M-SSMoEs: Hypergraph-based Multimodal Learning with LLM Reasoning and Style-Structured Mixture of Experts](https://mlsanity.com/paper/2510.25091/) (2025)

## Abstract (arXiv)

One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately.