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

Adaptive Mutation-Enhanced Random Forests for Predictive Modeling in Volatile Financial Markets | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 September 2025 V1 Latest version Share on Adaptive Mutation-Enhanced Random Forests for Predictive Modeling in Volatile Financial Markets Authors : Asad Ali 0009-0001-1944-5259 [email protected] and Danish Nawaz Authors Info & Affiliations https://doi.org/10.22541/au.175829742.21439317/v1 148 views 90 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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. Supplementary Material File (my paper 111.pdf) Download 562.77 KB Information & Authors Information Version history V1 Version 1 19 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adaptive random forests ensemble learning mutation mechanism Authors Affiliations Asad Ali 0009-0001-1944-5259 [email protected] Department of Computer Science, University of Oxford View all articles by this author Danish Nawaz Department of Computer Science, University of Lahore View all articles by this author Metrics & Citations Metrics Article Usage 148 views 90 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Asad Ali, Danish Nawaz. Adaptive Mutation-Enhanced Random Forests for Predictive Modeling in Volatile Financial Markets. Authorea . 19 September 2025. DOI: https://doi.org/10.22541/au.175829742.21439317/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175829742.21439317/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a026cc42385409d6',t:'MTc3OTkwMzU1NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();