[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122297-en":3,"doc-seo-122297-105":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},122297,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning Applications in Fixed Income Markets and Correlation Forecasting - Master of Philosophy Thesis","This MPhil thesis presents two applications of classic machine learning methods to fixed income markets and correlation forecasting. It first develops a supervised learning approach for selecting bonds using relative value signals from cross-sectional static data. It then extends the analysis to dynamic time series by introducing a directional change/directional forecasting (DC/DF) framework for rolling correlations across asset-class pairs at intrinsic time intervals. The work grounds model design in statistical theory, validates with large-scale benchmarks, and provides a confidence-interval and non-parametric testing methodology for comparing models, enabling bottom-up security selection and top-down allocation.","Machine Learning Applications in Fixed Income Markets and Correlation Forecasting  \nViktor Kazakov  \nA declaration submitted in partial fulfillment of the requirements for the  \ndegree of  \nMaster of Philosophy  \nof  \nUniversity College London  \nInstitute of Finance and Technology  \nDepartment of Civil, Environmental and Geomatic Engineering  \n14 April 2025  \nI, Viktor Kazakov, confirm that the work presented in my thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.  \nAbstract  \nIn this work, we present two applications of classic machine learning algorithms in fixed income marketsand correlation forecasting. We begin by developing a supervised learning model for the selection of bonds based on their relative value, trained on cross-sectional “static” data. We then expand this analysis to the dynamic setting by presenting a time series directional change/directional forecast framework (DC/DF) . We apply the DC/DF framework to paired time series of rolling correlation between asset classes, with the objective of forecasting changes in the correlation of the asset classes at “intrinsic time” intervals. Forecasting changes in the correlation between asset classes can be applied in contexts such as portfolio optimization, risk management, and trading.  \nThe proposed frameworks are built on solid statistical fundamentals. We justify the choice of models by conducting two large-scale benchmarking studies to determine which algorithms tend to be most accurate, on average. We then propose a statistical framework for comparing the results of the machine learning models. This framework rests on a methodology for building confidence intervals around point estimates and non-parametric statistical tests for model comparison.  \nWhen combined, the two models form a comprehensive framework for bottom-up securities selection and top-down allocation between different asset classes. The two frameworks are complementary and have direct practical use cases for value investing and portfolio diversification strategies. To the best of our knowledge, the proposed model validation methodology has not been previously applied to specifically solve financial problems. Furthermore, this is the first work that applies the proposed directional change/directional forecasting framework to time series of rolling correlation.  \nImpact Statement  \nThis MPhil research bridges gaps in financial and computational sciences by introducing two innovative machine-learning methodologies with broad-reaching implications in academia and industry.  \nAcademic Impact  \nThe proposed methodologies contribute to advancing the state-of-the-art in time series forecasting and cross-sectional investment analysis. First, the non-parametric supervised learning framework for bond valuation introduces a novel application of machine learning in fixed-income relative value analysis. It not only improves upon existing methods by addressing key limitations in the assumptions underpinning cash flow stripping techniques but also establishes a robust validation methodology through extensive benchmarking. This contributes to the broader research community by providing insights into machine learning’s predictive capabilities across 121 datasets and 27 algorithms.  \nSecond, the introduction of the directional change/directional forecasting (DC/DF) framework represents a significant advance in the analysis of dynamic financial time series, particularly for forecasting rolling correlations. By integrating machine learning techniques and novel event-driven methods, this work provides a foundational approach for further academic exploration in dynamic correlation modelling and its applications in financial research.  \nIndustrial Impact  \nIn industry, the practical applications of this research are multifaceted. The proposed bond investment strategy demonstrates significant potential in relative value investing by providing act","cbCaiqneHi3Gj3tk","https://ap.wps.com/l/cbCaiqneHi3Gj3tk","pdf",1196798,1,77,"English","en",105,"# Introduction\n## Background and Motivation\n## Research Objectives and Contributions\n## Relative Value Analysis for Fixed Income Securities\n## Directional Change and Directional Forecasting for Time Series of Rolling Correlation\n## Thesis Outline\n# Relative Value Investment Strategies for Fixed Income Securities\n## Introduction\n## Literature Review\n## Background and Terminology","[{\"question\":\"What are the two main machine learning applications proposed in the thesis?\",\"answer\":\"The thesis presents (1) a supervised model for bond selection based on relative value and (2) a DC/DF time-series framework for forecasting changes in rolling correlations between asset classes.\"},{\"question\":\"How does the thesis handle the shift from static data to dynamic forecasting?\",\"answer\":\"It starts with cross-sectional static data for supervised learning, then moves to dynamic paired time series using the directional change/directional forecasting framework to forecast correlation changes at intrinsic time intervals.\"},{\"question\":\"How are models validated and compared in the proposed methodology?\",\"answer\":\"The thesis uses two large-scale benchmarking studies to assess average accuracy, and introduces a statistical framework that builds confidence intervals for point estimates and applies non-parametric tests for model comparison.\"}]","Machine Learning Applications in Fixed Income Markets and Correlation Forecasting - Master of Philosophy Thesis | PDF",1785809882,194,{"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},"machine-learning-applications-in-fixed-income-markets-and-correlation-forecasting-master-of-philosophy-thesis","",{"@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/machine-learning-applications-in-fixed-income-markets-and-correlation-forecasting-master-of-philosophy-thesis/122297/",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","2026-08-04",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},"What are the two main machine learning applications proposed in the thesis?","Question",{"text":75,"@type":76},"The thesis presents (1) a supervised model for bond selection based on relative value and (2) a DC/DF time-series framework for forecasting changes in rolling correlations between asset classes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis handle the shift from static data to dynamic forecasting?",{"text":80,"@type":76},"It starts with cross-sectional static data for supervised learning, then moves to dynamic paired time series using the directional change/directional forecasting framework to forecast correlation changes at intrinsic time intervals.",{"name":82,"@type":73,"acceptedAnswer":83},"How are models validated and compared in the proposed methodology?",{"text":84,"@type":76},"The thesis uses two large-scale benchmarking studies to assess average accuracy, and introduces a statistical framework that builds confidence intervals for point estimates and applies non-parametric tests for model comparison.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]