[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121833-en":3,"doc-seo-121833-105":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},121833,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Machine Learning Approach to Stock Price Prediction","Stock market forecasting remains challenging because investors must anticipate future price movements amid uncertainty and market regime changes. This thesis applies a Support Vector Regression (SVR) machine learning model to predict directionally oriented Dow Jones Industrial Average (DJIA) stock prices using six technical indicators, then converts forecasts into trading signals for portfolio construction and rebalancing. Weekly, monthly, and quarterly rebalancing strategies are benchmarked against a passive equal-weighted portfolio, with weekly rebalancing achieving the strongest results across return and risk metrics.","ZHAW Zurich University of Applied Sciences School ofManagement and Law Department of Banking, Finance, Insurance  \nMaster of Science in Banking & Finance  \nA Machine Learning Approach to Stock Price  \nPrediction  \nSylvio Rhyner  \nSupervisor:  \nDr. Marc Weibel  \nZurich, June 15th 2023  \nManagement Summary  \nInvesting in the stock market is a complex task that relies heavily on the ability to predict future price movements. For decades, investors and financial analysts have sought ways to forecast these trends using a multitude of methodologies, ranging from fundamental to technical analysis.  \nThis research implemented a Support Vector Regression (SVR) model, a powerful machine learning technique used for predicting real-valued outputs, to forecast the direction of Dow Jones Industrial Average (DJIA) stocks. The model used several input features, including six technical indicators, to predict future stock prices. The generated predictions were then used to produce trading signals that guided the construction andrebalancing of portfolios.  \nThree different portfolio strategies-weekly, monthly, and quarterly rebalancing-were considered, all of which were compared to a passive equal-weighted (EW) benchmark portfolio. Each strategy relied on the SVR model's trading signals for stock selection, thereby encapsulating a Machine Learning-driven approach to investment management.  \nThe weekly rebalancing strategy emerged as the top performer across multiple metrics, including net cumulative return, annualized return, and the Sharpe ratio, thus outperforming the monthly, quarterly, and even the EW benchmark portfolio. This illustrates the potential benefits of frequent portfolio rebalancing in optimizing returns, despite the associated transaction costs. The results also show the influence of major global events, such as the Global Financial Crisis and the COVID-19 pandemic, as well as the rate of transaction costs on the portfolio performance.  \nFuture research should be directed towards the optimization of time windows for technical indicators and the weights of selected stocks.  \nStatement of Authorship  \n“\"I hereby declare that this thesis is my own work, that it has been created by me without the help of others, using only the sources referenced, and that I will not supply any copies of this thesis to any third parties without written permission by the head of this degree program.\"  \nAt the same time, all rights to this thesis are hereby assigned to ZHAW Zurich University of Applied Sciences, except for the right to be identified as its author.  \nSylvio Rhyner  \nTable of contents  \nManagement Summary ...................................................................................................... I  \nStatement of Authorship ................................................................................................... II  \nTable of contents ............................................................................................................ III  \nList of Tables ....................................................................................................................V  \nTable of Figures .............................................................................................................. VI  \nList of Abbreviations ..................................................................................................... VII  \nIntroduction ...................................................................................................................... 1  \nResearch Question ......................................................................................................... 1  \nObjective and Significance............................................................................................ 1  \nStructure of the Thesis................................................................................................... 2  \nLiterature Review ...........................................................","cbCaicK1VwmEoWAR","https://ap.wps.com/l/cbCaicK1VwmEoWAR","pdf",1055881,1,63,"English","en",105,"# Management Summary\n# Statement of Authorship\n# List of Tables\n# List of Figures\n# List of Abbreviations\n# Introduction\n## Research Question\n## Objective and Significance\n## Structure of the Thesis\n# Literature Review\n# Methodology\n## Data\n## Technical Indicators\n## Support Vector Machines\n## Support Vector Regression Model\n## Hyperparameter Tuning\n## Performance Metrics\n## Price prediction and trading signals\n## Portfolio Stock Selection and Performance Metrics\n# Results","[{\"question\":\"What machine learning method is used to predict stock prices in this thesis?\",\"answer\":\"The thesis uses Support Vector Regression (SVR), a machine learning technique designed for predicting real-valued outputs, to forecast DJIA stock price movements.\"},{\"question\":\"Which inputs and indicators feed the prediction model?\",\"answer\":\"The SVR model uses several input features, including six technical indicators, to forecast future stock prices and generate trading signals.\"},{\"question\":\"How do different portfolio rebalancing frequencies affect performance?\",\"answer\":\"Weekly rebalancing outperforms monthly, quarterly, and the passive equal-weighted benchmark across key metrics such as net cumulative return, annualized return, and the Sharpe ratio, while transaction costs influence overall performance.\"}]","A Machine Learning Approach to Stock Price Prediction | 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machine learning method is used to predict stock prices in this thesis?","Question",{"text":75,"@type":76},"The thesis uses Support Vector Regression (SVR), a machine learning technique designed for predicting real-valued outputs, to forecast DJIA stock price movements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which inputs and indicators feed the prediction model?",{"text":80,"@type":76},"The SVR model uses several input features, including six technical indicators, to forecast future stock prices and generate trading signals.",{"name":82,"@type":73,"acceptedAnswer":83},"How do different portfolio rebalancing frequencies affect performance?",{"text":84,"@type":76},"Weekly rebalancing outperforms monthly, quarterly, and the passive equal-weighted benchmark across key metrics such as net cumulative return, annualized return, and the Sharpe ratio, while transaction costs influence overall 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