Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
- Title
- Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
- Creator
- Singh A.; Kandala R.; Nair R.; Suryanarayanan U.; Sharma M.
- Description
- HR firms help drive economic growth by facilitating the acquisition and retention of top talent, fostering innovation and optimizing operational efficiency. The stock prices of these firms serve as a nuanced representation of their standing in the market. However, predicting stock prices proves to be a complex task due to the dynamic nature of the market. This paper delves into finding the most effective approach for forecasting stock prices within the HR sector, employing a diverse range of machine learning techniques. The investigation encompasses utilizing statistical methods like Simple Moving Average, RSI, Stochastic Indicators, and VIX India data alongside 'Machine learning approaches such as Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Gradient Boosting, and Neural Network.' To augment the analysis, a comprehensive study is conducted, integrating both top-performing and bottom-performing HRM firms (Info Edge Ltd and Quess Corporation) based on market capitalization. The outcomes derived from this study aim to lay the groundwork for future research endeavors in the realm of stock predictions specific to the HRM industry. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- HRM firms; Machine Learning; Neural Networks; Statistical methods; Stock price prediction
- Coverage
- Singh A., Christ University, India; Kandala R., Christ University, India; Nair R., Christ University, India; Suryanarayanan U., Christ University, India; Sharma M., Christ University, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh A.; Kandala R.; Nair R.; Suryanarayanan U.; Sharma M., “Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19220.