XGBoost
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A Comparative Performance Analysis of Machine Learning Models for Predicting Technology Sector Stock Returns Using Global Macroeconomic Indicators
Abstract: The financial markets do not depend on a single factor but are the result of interaction of macroeconomic, investor sentiment, and efficient company-specific factors. It is also difficult to predict the stock market especially in the technology industry because they are nonlinear and highly dynamic. The past few years have seen machine learning methods become effective tools in the analysis of financial data and the identification of implicit relationships that …
Published in NOLEGEIN Journal of Financial Planning and Management · Vol. 9, Issue 2, 2026 Read article
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The Accuracy Paradox in AI Equity Markets: A Regime-AwareTrading Strategy using XGBoost and Walk-Forward Optimization
Abstract: This study investigates the “Accuracy Paradox” in algorithmic equity trading by examining whether a machine learning model with near-random directional prediction accuracy can still generate profitable trading outcomes when combined with effective risk- management and market-regime rules. An XGBoost classifier was developed and evaluated using approximately six years of daily market data from five major technology stocks—NVIDIA, Apple, Microsoft, Alphabet, and Meta—covering the period from January 2020 to February 2026. …
Published in NOLEGEIN Journal of Financial Planning and Management · Vol. 9, Issue 2, 2026 Read article
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FinMate: A Cloud-Based AI and Machine Learning Systemfor Smart Finance Management
Abstract: Managing your own money has become a lot harder since the number of digital payments in India has gone up a lot. The most popular tools still rely on fixed keyword heuristics, which means they can't learn, make predictions about the future, or give tax advice. FinMate fixes these problems with a cloud-native microservices platform that combines three machine learning models with a rule-based engine for optimising taxes. An XGBoost …
Published in NOLEGEIN Journal of Financial Planning and Management · Vol. 9, Issue 2, 2026 Read article
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Detecting Financial Vulnerability in Urban Households Using Machine Learning: A Predictive Model for Early Risk Identification
Abstract: Urban households in an emerging economy are facing financial vulnerability which is a growing socioeconomic concern and usually does not notice the traditional credit evaluation methods. This study proposes a machine learning-based model that will predict an early warning of financial vulnerability by integrating both behavioral and socioeconomic factors. A structured questionnaire was used to collect primary data comprising of 328 urban households with Indians. After data preprocess and feature …
Published in NOLEGEIN Journal of Operations Research & Management · Vol. 9, Issue 2, 2026 Read article