Adaptive Mutation-Enhanced Random Forests for Predictive Modeling in Volatile Financial Markets

Adaptive Mutation-Enhanced Random Forests for Predictive Modeling in Volatile Financial Markets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive Mutation-Enhanced Random Forests for Predictive Modeling in Volatile Financial Markets Danish Nawaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7611297/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Volatile financial markets present a critical challenge for predictive modeling, as rapid price fluctuations, non-stationarity, and high noise levels often reduce the accuracy of traditional machine learning methods. Conventional ensemble approaches, including Random Forests, provide robustness against overfitting but struggle with adaptability to dynamic shifts in market conditions. To address these limitations, this study introduces the Adaptive Mutation-Enhanced Random Forest (AMERF) model, which integrates an evolutionary mutation mechanism into the tree construction process. By dynamically mutating feature subsets and decision thresholds during training, AMERF improves model diversity and adaptability to sudden market transitions. Experiments were conducted using historical stock price and technical indicator datasets from the S&P 500 and NASDAQ indices, covering highly volatile periods such as financial crises and pandemic-induced fluctuations. Data preprocessing included normalization, rolling-window feature engineering, and volatility-based segmentation to enhance temporal dependencies. Comparative evaluation against baseline Random Forests, Gradient Boosting, and LSTM models demonstrated that AMERF achieved superior performance, yielding improvements of up to 7% in predictive accuracy, with notable gains in F1-score, precision, and Sharpe ratio optimization. The primary contributions include: (1) introducing a mutation-driven adaptation strategy for ensemble models in finance, (2) demonstrating robustness across extreme volatility regimes, and (3) providing evidence of practical applicability in algorithmic trading and risk management. This research highlights the potential of evolutionary ensemble learning to bridge the gap between static models and dynamic financial realities. Future work will explore hybrid deep learning–mutation ensembles for real-time predictive trading systems. Theoretical Computer Science Adaptive Random Forests Mutation Mechanism Ensemble Learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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