[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126752-en":3,"doc-seo-126752-105":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},126752,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting risk/reward ratio in financial markets for asset management using machine learning","Financial market forecasting remains a difficult task despite advances in computation and machine learning. Many prior approaches achieve accurate predicted market movements but still fail to produce profitable trades, largely because profit and loss depend on whether predictions succeed. This study proposes an algorithm to forecast trading profit/loss outcomes and integrates them with previous market trend predictions. The method targets algorithmic trading, enabling trade-by-trade profitability assessment and optimal trade sizing, improving both traditional and algorithmic strategies.","arXiv :2311 .09148v1 [ q-fin .CP] 15 Nov 2023  \nCopyright Information  \nThis work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 4.0 International  \nLicense.  \nTo view a copy of this license, visit  \n[https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPredicting risk/reward ratio in financial markets for asset management using machine learning  \nReza Yarbakhsha,b,∗, Mahdieh Soleymani Baghshahb , Hamidreza  \nKarimaghaiea,1  \na Department of Business Management, University of Tehran, Tehran, Iran b Department of Computer Science and Engineering, Sharif University of Technology,  \nTehran, Iran  \nAbstract  \nFinancial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions.  \nKeywords: Risk/Reward Ratio, Applied ML, stock market prediction, algorithmic trading, asset management  \n1. Introduction  \nFinancial markets are complex and ever-changing, rendering market forecasting a challenging task (Maasoumi & Racine, 2002; Raubitzek & Neubauer, 2022) . The issues arise primarily because financial markets are driven by a multitude of external factors such as political turmoil (Hillier & Loncan, 2019), global economy (Matkovskyy & Jalan, 2019), investor sentiments (Li et al. , 2014; Broadstock & Zhang, 2019), and the fundamental characteristics of assets (Wafi et al., 2015) . While these variables are mostly unpredictable, and  \n∗ Corresponding author  \nEmail addresses: [yarbakhsh@ut.ac.ir](yarbakhsh@ut.ac.ir) (Reza Yarbakhsh ), [soleymani@sharif.edu](soleymani@sharif.edu)[ ](soleymani@sharif.edu)(Mahdieh Soleymani Baghshah), [karimaghaie@ut.ac.ir](karimaghaie@ut.ac.ir) (Hamidreza Karimaghaie)  \ntheir information is not readily available to the public, they have a direct and strong correlation with asset prices. Despite this, with markets not being fully efficient due to information disparities (Barr Rosenberg & Lanstein, 1998), information relevant to the market’s future is reflected in its pricing (Dias et al. , 2020; Ozkan, 2021) . Technical analysis approaches were previously used to forecast markets using historical pricing data (Park & Irwin, 2007) . Nowadays, machine learning methods have significantly improved these predictions and increased their accuracy substantially (Stankovi´c et al., 2015; Ayala et al., 2021) . However, most machine learning algorithms still cannot provide usable trading recommendations (Buczynski et al., 2021) . This is primarily because they do not consider the potential profit and loss for every trade and the possibility of incurring great losses in wrong predictions even if there are few of them, We will delve into and address this issue in greater detail in later sections. In the following three sections of this article, we will explore three widely used models for predicting financial markets, examining their strengths and weaknesses to gain a deeper understanding of their utility in the complex world of financial f","cbCaij2T7NXXfMTe","https://ap.wps.com/l/cbCaij2T7NXXfMTe","pdf",2226693,1,29,"English","en",105,"# Introduction\n## Regression Models for Price Forecasting in Financial Markets\n## Classification Models for Market Direction Prediction\n## Triple-barrier labeling","[{\"question\":\"Why do accurate machine learning market forecasts often fail to generate profitable trading results?\",\"answer\":\"Because trading outcomes depend on unpredictable profit/loss ratios in successful versus unsuccessful predictions, not only on prediction accuracy.\"},{\"question\":\"What does the proposed approach add beyond typical prediction models?\",\"answer\":\"It forecasts profit and loss outcomes for trading activities and integrates these forecasts with prior market trend predictions to support algorithmic trading decisions.\"},{\"question\":\"How does the method support position sizing in algorithmic trading?\",\"answer\":\"Traders can assess profitability of each trade and calibrate the optimal trade size based on the forecasted outcomes.\"}]","Predicting risk/reward ratio in financial markets for asset management using machine learning | 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do accurate machine learning market forecasts often fail to generate profitable trading results?","Question",{"text":75,"@type":76},"Because trading outcomes depend on unpredictable profit/loss ratios in successful versus unsuccessful predictions, not only on prediction accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed approach add beyond typical prediction models?",{"text":80,"@type":76},"It forecasts profit and loss outcomes for trading activities and integrates these forecasts with prior market trend predictions to support algorithmic trading decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the method support position sizing in algorithmic trading?",{"text":84,"@type":76},"Traders can assess profitability of each trade and calibrate the optimal trade size based on the forecasted 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