[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123642-en":3,"doc-seo-123642-105":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},123642,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Average variance portfolio optimization using machine learning-based stock price prediction case of renewable energy investments","Research focuses on improving renewable energy investment decisions by integrating time-series stock prediction into portfolio selection. A hybrid approach is presented that merges a convolutional neural network (CNN) and long-term bidirectional memory (BiLSTM) for closing-price prediction, using robust input features derived from Huber location characteristics. Predicted stock outcomes are then fed into the mean-variance (MV) Markowitz framework to construct optimal portfolios. Experiments use historical data from the SET50 index (Jan 2016–Dec 2021) and benchmark against MV and equal-weight (1/N) portfolios with LSTM, BiLSTM, and CNN-BiLSTM.","Average variance portfolio optimization using machine learningbased stock price prediction case of renewable energy investments  \nKarima Sabbar, Moad El Kharrim  \nFaculty of Economic and Social Legal Sciences ofTetouan, Abdemalek Esaadi University, Morocco  \nAbstract: With the progress of time series prediction, several recent developments in machine learning  \nhave shown that the integration of prediction methods into portfolio selection is a great opportunity to  \nstructure investment decisions in the renewable energy industry. In this paper, we propose a novel  \napproach to portfolio formation strategy based on a hybrid machine learning model that combines a  \nconvolutional neural network (CNN) and long-term bidirectional memory (BiLSTM) with robust input  \ncharacteristics obtained from Huber's location for stock prediction and the mean-variance (MV)  \nMarkowitz model for optimal portfolio construction. Specifically, this study first applies a prediction  \nmethod for stock pre-selection to ensure high-quality stock inflows for portfolio formation. Then, the  \npredicted results are integrated into the MV model. To comprehensively demonstrate the superiority of the  \nproposed model, we used two portfolio models, the MV model and the equal-weighted (1/N) portfolio  \nmodel, with LSTM, BiLSTM and CNN-BiLSTM, and used them as references. Between January 2016 and  \nDecember 2021, historical data from the Stock Exchange of Thailand 50 Index (SET50) was collected for  \nthe study. Experience shows that integrating stock pre-selection can improve VM performance, and the  \nresults of the proposed method show that they outperform comparison models in terms of Sharpe ratio,  \naverage return and risk.  \nKeywords : portfolio optimization; mean-variance model; inventory forecasting; stock selection; machine  \nlearning; convolutional neural network; short-term long memory.  \n1 Introduction  \nIn recent years, there has been a reluctance on the part of investors to invest in renewable energy technologies.  \nTherefore, the most important prerequisite for assessing investment risks and creating a better situation to attract the tendency of investors to invest in this area is the optimization of portfolio selection to structure investment decisions in this area.  \nPortfolio optimization is one of the most interesting issues, the MV model relies on historical data to generate the optimal portfolio and can only display the optimal portfolio as far as data entry is concerned. Therefore, a number of researchers have applied machine learning to predict return and volatility in the future (Henrique et al. 2019) . Investors in the financial market need to evaluate a variety of factors and perspectives to maximize their investment income (Rahiminezhad Galankashi et al. 2020) . In this regard, including stock price prediction methods in portfolio optimization would be beneficial and cost-effective for investors (Kolm et al. 2014) . Financial time series forecasting has long been a challenging area of study, as financial market fluctuations are inherently volatile, complex and dynamic (Paiva et al. 2019) . However, several related studies claim that there is a pattern of asset price movement in financial time series data and that this pattern can be used to predict financial time series data to some extent (Wan et al. 2020; Wang et al. 2020) .  \nPortfolio management is an analytical process of selecting and allocating a group of investment assets in which the allocated portion of the investment is constantly modified to optimize expected return and risk tolerance (Markowitz 1952) . Markowitz's mean variance (MV) model, first developed in 1952, is the foundation of portfolio theory, which is widely used and recognized in portfolio management (Sharpe and Markowitz 1989) .  \nHowever, based on the classical MV model, there are two main problems of practical application. The first is that the MV relies on the expected return and risk of asset inflows to produce","cbCaijfANfUEvDaO","https://ap.wps.com/l/cbCaijfANfUEvDaO","pdf",2184463,1,12,"English","en",105,"# Introduction\n## Mean-variance portfolio optimization\n## Portfolio management challenges and motivation\n# Basic knowledge\n## Mean-variance optimization\n## Hybrid prediction and portfolio construction","[{\"question\":\"How does the proposed method combine stock prediction with portfolio optimization?\",\"answer\":\"It first applies a hybrid CNN-BiLSTM model to pre-select stocks by predicting future closing prices, and then integrates the predictions into the mean-variance (MV) Markowitz model to build the optimal portfolio.\"},{\"question\":\"What data and market universe are used for the experiments?\",\"answer\":\"Historical data from the Stock Exchange of Thailand 50 Index (SET50) are collected for the period January 2016 to December 2021.\"},{\"question\":\"Which portfolio models are used for comparison in the study?\",\"answer\":\"The study compares the proposed approach with two baseline portfolio constructions: the MV model and the equal-weighted (1/N) portfolio, using LSTM, BiLSTM, and CNN-BiLSTM as reference predictors.\"}]","Average variance portfolio optimization using machine learning-based stock price prediction case of renewable energy investments | 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does the proposed method combine stock prediction with portfolio optimization?","Question",{"text":75,"@type":76},"It first applies a hybrid CNN-BiLSTM model to pre-select stocks by predicting future closing prices, and then integrates the predictions into the mean-variance (MV) Markowitz model to build the optimal portfolio.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and market universe are used for the experiments?",{"text":80,"@type":76},"Historical data from the Stock Exchange of Thailand 50 Index (SET50) are collected for the period January 2016 to December 2021.",{"name":82,"@type":73,"acceptedAnswer":83},"Which portfolio models are used for comparison in the study?",{"text":84,"@type":76},"The study compares the proposed approach with two baseline portfolio constructions: the MV model and the equal-weighted (1/N) portfolio, using LSTM, BiLSTM, and CNN-BiLSTM as reference 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