[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119793-en":3,"doc-seo-119793-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119793,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning and Portfolio Optimization - an application to Italian FTSE-MIB Stocks","The work presents a data-driven framework for portfolio optimization applied to Italian FTSE-MIB stocks, combining forecasting methods with machine learning and risk-return portfolio construction. It starts from Markowitz’s portfolio theory and the modern approach to diversification, then defines stock indicators and develops an ARMA-based forecasting component. A machine learning model is introduced to support predictive performance, followed by portfolio optimization and evaluation on a selected dataset. The document also details the dataset preparation, implementation steps, and forecasting and SVM-related procedures, concluding with overall findings and methodological limitations.","UNIVERSITA’ DEGLI STUDI DI PADOVA  \nDIPARTIMENTO DI SCIENZE ECONOMICHE ED AZIENDALI  \n“M.FANNO”  \nDIPARTIMENTO DI MATEMATICA “TULLIO LEVI-CIVITA”  \nCORSO DI LAUREA MAGISTRALE IN  \nECONOMICS AND FINANCE  \nTESI DI LAUREA  \nMachine Learning and Portfolio Optimization: an application to Italian FTSE-MIB Stocks  \nRELATORE:  \nCH.MO PROF. CLAUDIO FONTANA  \nLAUREANDO: ANDREA MASIERO  \nMATRICOLA N. 2004261  \nANNO ACCADEMICO 2022 – 2023  \nDichiaro di aver preso visione del “Regolamento antiplagio” approvato dal Consiglio del Dipartimento di Scienze Economiche e Aziendali e, consapevole delle conseguenze derivantida dichiarazioni mendaci, dichiaro che il presente lavoro non è già stato sottoposto, in tutto o in parte, per il conseguimento di un titolo accademico in altre Università italiane o straniere. Dichiaro inoltre che tutte le fonti utilizzate per la realizzazione del presente lavoro, inclusi imateriali digitali, sono state correttamente citate nel corpo del testo e nella sezione‘Riferimenti bibliografici’.  \nI hereby declare that I have read and understood the “Anti-plagiarism rules and regulations”approved by the Council of the Department of Economics and Management and I am aware of the consequences of making false statements. I declare that this piece of work has not been previously submitted – either fully or partially –for fulfilling the requirements of an academic degree, whether in Italy or abroad. Furthermore, I declare that the references used for this work – including the digital materials – have been appropriately cited and acknowledged in the text and in the section ‘References’.  \nFirma (signature)…….…………………………  \nTable of Contents  \nINTRODUCTION .................................................................................................................. 5  \nCHAPTER ONE: LITERATURE REVIEW................................................................................. 12  \nCHAPTER TWO: THE METHODOLOGY................................................................................ 20  \n2.1 Stock Indicators ................................................................................................................... 20  \n2.2 Developing the ARMA Model............................................................................................... 26  \n2.3 Developing the Machine Learning Model............................................................................. 32  \n2.4 Portfolio Optimization ......................................................................................................... 37  \nCHAPTER 3: APPLICATION OF THE MODEL TO THE DATA ................................................... 44  \n3.1 The Dataset ......................................................................................................................... 44  \n3.2 Forecast method applied to a single stock ........................................................................... 47  \n3.3 Portfolio optimization.......................................................................................................... 55  \n3.4 Portfolio evaluation ............................................................................................................. 61  \nCONCLUSION .................................................................................................................... 65  \nAPPENDIX A ..................................................................................................................... 67  \nA.1 Retrieving financial data...................................................................................................... 67  \nA.2 Indicators calculation .......................................................................................................... 67  \nA.3 ARMA model implementation ............................................................................................. 68  \nA.4 ARMA Model fitting .....................................................................................","cbCaigMZNSNVmkKm","https://ap.wps.com/l/cbCaigMZNSNVmkKm","pdf",5910087,1,86,"English","en",105,"# Introduction\n# Chapter One: Literature Review\n# Chapter Two: The Methodology\n## Stock Indicators\n## Developing the ARMA Model\n## Developing the Machine Learning Model\n## Portfolio Optimization\n# Chapter 3: Application of the Model to the Data\n## The Dataset\n## Forecast method applied to a single stock\n## Portfolio optimization\n## Portfolio evaluation\n# Conclusion\n# Appendix A\n## Retrieving financial data\n## Indicators calculation\n## ARMA model implementation\n## ARMA Model fitting\n## ARMA Model forecasting\n## Preparing the data to the SVM implementation\n## SVM implementation\n## Present the data\n# Appendix B\n# References","[{\"question\":\"What core portfolio concept guides the study?\",\"answer\":\"The study is grounded in Markowitz’s portfolio theory, using diversification to balance expected returns and risk.\"},{\"question\":\"How is forecasting handled before portfolio optimization?\",\"answer\":\"The methodology includes developing an ARMA model and building a machine learning model, then applying forecasting to individual stocks as inputs to portfolio optimization.\"},{\"question\":\"What steps are used to evaluate the optimized portfolios?\",\"answer\":\"Portfolio evaluation is performed after optimization, using the dataset prepared for forecasting and the derived portfolio construction outputs.\"}]","Machine Learning and Portfolio Optimization - 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