Machine Learning for Stock Market Forecasting: A Decision Support Framework Using Infosys Historical Data |Chapter 2 | New Advances in Business, Management and Economics Vol. 7

The volatility of stock markets makes prediction a complex but essential task for investors seeking to optimise their decision-making. This study presents a data-driven decision support model for stock market prediction using historical stock price data of Infosys Ltd., a leading IT firm in India. By applying data mining techniques—specifically classification and rule-based prediction—the model aims to identify meaningful patterns from past trends and assist in forecasting future price movements.

The study uses monthly trading data from the National Stock Exchange for the period 2010 to 2015, including open, high, low, and close price values. These values were transformed into symbolic categories (rise/fall) for analytical clarity. Using the ESTARD Data Miner tool, decision trees and rule-based classifiers were generated to derive actionable prediction rules. The rules were further tested using a What-If analysis for real-time prediction scenarios.

The findings demonstrate the effectiveness of symbolic conversion and decision tree modelling in predicting the stock trend classes. This predictive framework holds potential for guiding investors in making more informed buy/sell decisions and enhancing the reliability of investment strategies based on historical data patterns.

 

Author (s) Details

Sanjeev Gour
Medicaps University, Indore, MP, India.

 

Sanjana Sharma
Acropolis Institute of Technology & Research, Indore, MP, India.

 

Prerita Kulkarni
Medicaps University, Indore, MP, India.

 

Shimna Mohan K.
Medicaps University, Indore, MP, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nabme/v7/5193

Investment Behaviour for Small Investors in the Hong Kong Stock Market | Chapter 4 | Bubbles and Behavioral Finance

 

We extend Hon’s (2012) paper to identify and analyse the important factors that capture the behaviour of small investors in the Hong Kong stock market, especially during the financial crisis. Exploratory factor analysis is employed to analyze the data, we find that the reference group is the most important factor and monitoring investments is the second important factor.

 

Author(s)details:-

 

Tai-Yuen Hon (Research Affiliate)
Business, Economic and Public Policy Research Centre, Hong Kong Shue Yan University, Hong Kong

 

Wing-Kwong Au (Associate Professor)
Department of Social Work, Hong Kong Shue Yan University, Hong Kong.

 

Wing-Keung Wong (Chair Professor)
Department of Finance, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taiwan.

 

Please See the book here :- https://doi.org/10.9734/bpi/mono/978-81-973195-8-7/CH4

Recent Advances in Commerce, Management, and Tourism | Book Publisher International

 The edited book, "Rethinking Commerce, Management, and Tourism - Post-COVID New Normal," presents a comprehensive exploration of the transformative impact of the COVID-19 pandemic on these critical sectors. As the world grappled with unprecedented challenges, businesses, policymakers, and scholars faced the imperative to adapt and innovate.

This volume assembles a diverse range of perspectives, offering multidisciplinary insights, real-world case studies, and practical guidance. Topics such as social media marketing, Caravan tourism, medical tourism, entrepreneurship, sustainability, consumer behaviour, human resource management, financial markets and instruments are a few among others.

With a commitment to advancing knowledge and fostering resilience, this book aims to inspire fresh ideas, stimulate discussions, managerial implications and contribute to the collective effort to revive and rebuild the business. It serves as a compass for navigating the evolving dynamics of commerce, management, and tourism in the aftermath of the pandemic.

Author(s) Details:

Prem Kumar,
Department of Management, Garden City University, Bangalore, India.

Baby Niviya Feston,
Department of Management, Garden City University, Bangalore, India.

Swetha Appaji Parivara,
Department of Commerce, Garden City University, Bangalore, India.

Sumit Kumar Singh,
Department of Management, Garden City University, Bangalore, India.

Please see the link here: https://stm.bookpi.org/RACMT/article/view/12024

Determining the Relationship between Coronavirus and Stock Market Volatility in Emerging Countries | Chapter 7 | Current Aspects in Business, Economics and Finance Vol.5

 The present study optimize the cost of dairy products in manufacturing in a dairy factory so that consumers should get advantaged from the price of separate product deals in the request. also, this study helps to reduce the running and functional losses of the dairy factory. The study was accepted in the Experimental Dairy Factory, public Dairy Research Institute, Karnal( Haryana), with a running capacity of ten thousand liters per day, to optimize the cost of Dairy products manufactured in the dairy factory. Burfi and Ice- cream are among the most pivotal Indian dairy products reused in a dairy factory and extensively consumed. Primary and Secondary data were both used to conduct this study. Primary compliances and interviews with factory workers round out the primary data. Secondary data, similar as milk flux, its operation pattern, and product affair, were gathered from colorful factory checks. To reduce the cost of each component used to make Burfi and ice cream, an profitable study of this dairy product in a dairy factory is needed. The dairy product will keep its presence in the competitive request, and as a result, the consumer will profit from this optimum price. thus, calculated the product cost was in a dairy factory, and the fixed and variable cost was24.56 and75.44 percent for Burfi and34.01 and65.99 percent for ice cream, independently. This study shows that the dairy factory has fat product of3721.69 kg and mugs of 100 ml of affair Burfi and Ice- cream, independently, after the break-even point. This study suggests that the small milk patron and tone- help groups should engage in value- added product processing on their own by taking training from institutions to induce income and employment to support the pastoral frugality.                                         

Author(s) Details:

Muhammad Mar’I,
Department of Banking and Finance, Near East University, North Cyprus, Mersin 10, Turkey.

Turgut Tursoy,
Department of Banking and Finance, Near East University, North Cyprus, Mersin 10, Turkey.

Please see the link here: https://stm.bookpi.org/CABEF-V5/article/view/8454