[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127359-en":3,"doc-seo-127359-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127359,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Application Of Machine Learning Algorithms to Free Cash Flows Growth Rate Estimation - Research Findings","Machine learning (ML) delivers higher accuracy for financial time-series forecasting than traditional statistical approaches. Most prior work targets high-frequency market pricing, while fundamental data such as free cash flows (FCF) is typically available only on a quarterly basis. This study evaluates nine ML algorithms on small-sample datasets and benchmarks them against ARIMA for growth-rate estimation. Experiments on 100 US companies show low test-set errors, with k-nearest neighbor achieving the best accuracy using only 33 observations without overfitting.","Available [online at www.sciencedirect.com](online at www.sciencedirect.com)  \nProcedia Computer Science 00 (2023) 000–000  \n[www.elsevier.com](www.elsevier.com/locate/procedia)[/](www.elsevier.com/locate/procedia)[locate](www.elsevier.com/locate/procedia)[/](www.elsevier.com/locate/procedia)[procedia](www.elsevier.com/locate/procedia)  \nInternational Neural Network Society Workshop on Deep Learning Innovations and Applications  \n(INNS DLIA 2023)  \nApplication Of Machine Learning Algorithms to Free Cash Flows  \nGrowth Rate Estimation  \nIvan Evdokimova,∗, Michael Kampouridisa , Tasos Papastylianouba School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK  \nb Institute of Public Health and Wellbeing, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK  \nAbstract  \nMachine learning (ML) demonstrates superior accuracy in financial time-series forecasting compared to traditional statistical models. While most studies focus on applying ML algorithms to high-frequency pricing data, the availability of fundamental financial data is limited as it is generated quarterly. This paper investigates the performance of nine ML algorithms in small sample data sets, against an ARIMA model—frequently used for financial time-series forecasting—serving as a benchmark. Results obtained from 100 US companies indicate that the majority of ML algorithms exhibit low error rates on the test set, outperforming benchmark results. Notably, the k-nearest neighbor algorithm achieves the highest prediction accuracy among the algorithms considered, even with only 33 data observations, while avoiding overfitting.  \n© 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([http:](http://creativecommons.org/licenses/by-nc-nd/4.0/)[//](http://creativecommons.org/licenses/by-nc-nd/4.0/)[creativecommons.org](http://creativecommons.org/licenses/by-nc-nd/4.0/)[/](http://creativecommons.org/licenses/by-nc-nd/4.0/)[licenses](http://creativecommons.org/licenses/by-nc-nd/4.0/)[/](http://creativecommons.org/licenses/by-nc-nd/4.0/)[by-nc-nd](http://creativecommons.org/licenses/by-nc-nd/4.0/)[/](http://creativecommons.org/licenses/by-nc-nd/4.0/)[4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/)[/](http://creativecommons.org/licenses/by-nc-nd/4.0/))  \nPeer-review under responsibility of the scientific committee of the International Neural Network Society Workshop on Deep Learning Innovations and Applications.  \nKeywords: Machine Learning; Forecasting; Financial Fundamentals; Time-Series Modeling; Free Cash Flows Forecasting  \n1. Introduction  \nMuch of the academic literature on the applications of ML algorithms to finance tends to focus on pricing data [15], i.e. the market price of a financial security (e.g. common stocks, bonds, etc.) . This is typically the case due to the open-source availability and abundance of such data: each year a total of 252 data points are created, assuming daily frequency. Fundamental data, on the other hand, such as revenues and free cash flows, usually used by creditors, auditors and investors to perform their analyses, is in short supply as companies only disclose such figures on a quarterly basis, thus generating four data points per annum.  \nFree Cash Flows (FCF) is a measure of cash available for shareholders after all the necessary expenses and investments were met. Fundamental investors aim to predict the growth rate of FCFs in order to estimate a business’  \n∗ Corresponding author.  \nE-mail address: [ie20391@essex.ac.uk](ie20391@essex.ac.uk)  \n1877-0509 © 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([http:](http://creativecommons.org/licenses/by-nc-nd/4.0/)[//](http://creativecommons.org/licenses/by-nc-nd/4.0/)[creativecommons.org](http://creativecommons.org/licenses/by-nc-nd/4.0/)[/](http://creativecommons.org/licenses/by-nc-nd/4.0/)[licenses](http://","cbCaikqTlrmEPEEE","https://ap.wps.com/l/cbCaikqTlrmEPEEE","pdf",196439,1,10,"English","en",105,"# Abstract\n# Introduction\n## Data availability: pricing vs. fundamentals\n## Free cash flows and growth rate forecasting\n# Methodology\n## Regression algorithms vs. ARIMA\n## Target variable and feature construction","[{\"question\":\"Why focus on free cash flows instead of pricing data?\",\"answer\":\"Fundamental data such as free cash flows are disclosed quarterly, providing fewer observations than daily pricing data. This limited availability makes growth-rate forecasting more challenging and motivates the study.\"},{\"question\":\"What benchmark model is used to evaluate the ML algorithms?\",\"answer\":\"The study benchmarks nine machine learning regression algorithms against an ARIMA model, which is widely used in finance time-series forecasting.\"},{\"question\":\"Which algorithm performs best and under what data size?\",\"answer\":\"The k-nearest neighbor algorithm achieves the highest prediction accuracy and does so even with only 33 data observations, while avoiding overfitting.\"}]","Application Of Machine Learning Algorithms to Free Cash Flows Growth Rate Estimation - Research Findings | PDF",1785938487,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-algorithms-to-free-cash-flows-growth-rate-estimation-research-findings","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-algorithms-to-free-cash-flows-growth-rate-estimation-research-findings/127359/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why focus on free cash flows instead of pricing data?","Question",{"text":75,"@type":76},"Fundamental data such as free cash flows are disclosed quarterly, providing fewer observations than daily pricing data. This limited availability makes growth-rate forecasting more challenging and motivates the study.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What benchmark model is used to evaluate the ML algorithms?",{"text":80,"@type":76},"The study benchmarks nine machine learning regression algorithms against an ARIMA model, which is widely used in finance time-series forecasting.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performs best and under what data size?",{"text":84,"@type":76},"The k-nearest neighbor algorithm achieves the highest prediction accuracy and does so even with only 33 data observations, while avoiding overfitting.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]