Pub Date : 2025-08-05DOI: 10.1016/j.jfds.2025.100165
Parisa Davar , Frédéric Godin , Jose Garrido
This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented.
{"title":"Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆","authors":"Parisa Davar , Frédéric Godin , Jose Garrido","doi":"10.1016/j.jfds.2025.100165","DOIUrl":"10.1016/j.jfds.2025.100165","url":null,"abstract":"<div><div>This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100165"},"PeriodicalIF":3.9,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.1016/j.jfds.2025.100164
Arefeh Zarifian , Christoph Gallus , Ludger Overbeck , Emmanuel M. Pothos , Pawel Blasiak
The failure to identify and measure financial risk carries significant social and economic consequences. This paper introduces a novel framework for analyzing financial stress and crises, based on the Bell inequalities, a foundational framework in causal analysis, originally developed in quantum mechanics. Traditional approaches to crisis analysis do not, in general, adequately represent event-based dependencies and the distribution of tail risks inherent in complex financial systems. The proposed approach is underwritten by a generic causal framework, which we think is suitable for financial analysis: we offer an index for financial stress and we explore its value in detecting extreme market co-movements, which may serve as an early crisis warning signal.
Our analyses employ a rolling-window approach to analyze financial time series data. We utilize S&P 500 and STOXX Europe 600 stocks and consider three historical crises, namely the 2008 financial crisis, the EU debt crisis and the COVID-19 pandemic, which mark some of the largest downturns of financial markets in the last two decades. The findings demonstrate the framework's ability to align the number of observed Bell inequalities violations with observed peaks in market stress. In particular, the framework shows good performance against CDS spreads as a crisis indicator and is less erratic than the traditional Pearson correlation of price returns. It aligns well with implied equity option volatility as measured by VIX. Overall, we think the present causal framework has promising properties and merits further examination.
{"title":"Using Bell violations as an indicator for financial market crisis","authors":"Arefeh Zarifian , Christoph Gallus , Ludger Overbeck , Emmanuel M. Pothos , Pawel Blasiak","doi":"10.1016/j.jfds.2025.100164","DOIUrl":"10.1016/j.jfds.2025.100164","url":null,"abstract":"<div><div>The failure to identify and measure financial risk carries significant social and economic consequences. This paper introduces a novel framework for analyzing financial stress and crises, based on the Bell inequalities, a foundational framework in causal analysis, originally developed in quantum mechanics. Traditional approaches to crisis analysis do not, in general, adequately represent event-based dependencies and the distribution of tail risks inherent in complex financial systems. The proposed approach is underwritten by a generic causal framework, which we think is suitable for financial analysis: we offer an index for financial stress and we explore its value in detecting extreme market co-movements, which may serve as an early crisis warning signal.</div><div>Our analyses employ a rolling-window approach to analyze financial time series data. We utilize S&P 500 and STOXX Europe 600 stocks and consider three historical crises, namely the 2008 financial crisis, the EU debt crisis and the COVID-19 pandemic, which mark some of the largest downturns of financial markets in the last two decades. The findings demonstrate the framework's ability to align the number of observed Bell inequalities violations with observed peaks in market stress. In particular, the framework shows good performance against CDS spreads as a crisis indicator and is less erratic than the traditional Pearson correlation of price returns. It aligns well with implied equity option volatility as measured by VIX. Overall, we think the present causal framework has promising properties and merits further examination.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100164"},"PeriodicalIF":3.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1016/j.jfds.2025.100163
Ajit Desai , Anneke Kosse , Jacob Sharples
We propose a flexible machine learning (ML) framework for real-time transaction monitoring in high-value payment systems (HVPS), which are central to a country’s financial infrastructure and integral to financial stability. This framework can be used by system operators and overseers to detect anomalous transactions, which—if caused by a cyber attack or an operational outage and left undetected—could have serious implications for the HVPS, its participants and the financial system more broadly. Given the high volume of payments settled each day and the scarcity of actual anomalous transactions in HVPS, detecting anomalies resembles finding a needle in a haystack. Therefore, our framework employs a layered approach to manage the high volume of payments and isolate potential anomalies. In the first layer, a supervised ML algorithm is used to identify and separate ‘typical’ payments from ‘unusual’ payments. In the second layer, only the ‘unusual’ payments are run through an unsupervised ML algorithm for anomaly detection. We test this framework using artificially manipulated transactions and payments data from the Canadian HVPS. The ML algorithm employed in the first layer achieves a detection rate of 93 %, marking a significant improvement over commonly-used econometric models. The ML algorithm used in the second layer marks the artificially manipulated transactions as nearly twice as suspicious as the original transactions, proving its effectiveness.
{"title":"Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems","authors":"Ajit Desai , Anneke Kosse , Jacob Sharples","doi":"10.1016/j.jfds.2025.100163","DOIUrl":"10.1016/j.jfds.2025.100163","url":null,"abstract":"<div><div>We propose a flexible machine learning (ML) framework for real-time transaction monitoring in high-value payment systems (HVPS), which are central to a country’s financial infrastructure and integral to financial stability. This framework can be used by system operators and overseers to detect anomalous transactions, which—if caused by a cyber attack or an operational outage and left undetected—could have serious implications for the HVPS, its participants and the financial system more broadly. Given the high volume of payments settled each day and the scarcity of actual anomalous transactions in HVPS, detecting anomalies resembles finding a needle in a haystack. Therefore, our framework employs a layered approach to manage the high volume of payments and isolate potential anomalies. In the first layer, a supervised ML algorithm is used to identify and separate ‘typical’ payments from ‘unusual’ payments. In the second layer, only the ‘unusual’ payments are run through an unsupervised ML algorithm for anomaly detection. We test this framework using artificially manipulated transactions and payments data from the Canadian HVPS. The ML algorithm employed in the first layer achieves a detection rate of 93 %, marking a significant improvement over commonly-used econometric models. The ML algorithm used in the second layer marks the artificially manipulated transactions as nearly twice as suspicious as the original transactions, proving its effectiveness.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.jfds.2025.100155
Xiaoling Song , Xuan Qin , Wanmeng Wang , Rita Yi Man Li
Using panel data from 144 countries, this study constructed an inclusive financial evaluation index and depicted the inclusive finance development worldwide under digital empowerment through classification. It reviewed the spatial effect of financial inclusion in developed and developing countries by throwing light on demand, supply, and regulatory factors via the spatial Durbin model. The mediating and regulatory effects model examines the transmission mechanism of financial inclusion with a focus on financial literacy, scientific and technological levels, and regulatory quality. The results show that the level of financial inclusion in developed countries is significantly higher than in developing countries. The economic level of developed countries positively impacts financial inclusion in their countries and neighbouring ones. Enhancing financial literacy, science and technological level, and supervision quality improve the development of inclusive finance. While the economic level and urbanization rate in developing countries inhibit the development of financial inclusion, countries with lower economic development and urbanization rates have a greater incentive to develop digital financial inclusion. The improved economic development in developing countries favours financial inclusion in countries nearby. Moreover, financial literacy plays a positive moderating role in the effect of digital finance on financial inclusion. The technology level can exert a transmission effect on financial inclusion through an elevated level of digital finance. The impact of regulatory quality on financial inclusion can be conveyede by creating a stable economic and financial environment and improving economic development levels. This study expands the theoretical research on constructing an inclusive finance evaluation system and its impact mechanism. It provides essential decision-making references for governments, relevant decision-making departments, financial institutions and financial technology enterprises to develop inclusive finance.
{"title":"Financial inclusion, technologies, and worldwide economic development: A spatial Durbin model approach","authors":"Xiaoling Song , Xuan Qin , Wanmeng Wang , Rita Yi Man Li","doi":"10.1016/j.jfds.2025.100155","DOIUrl":"10.1016/j.jfds.2025.100155","url":null,"abstract":"<div><div>Using panel data from 144 countries, this study constructed an inclusive financial evaluation index and depicted the inclusive finance development worldwide under digital empowerment through classification. It reviewed the spatial effect of financial inclusion in developed and developing countries by throwing light on demand, supply, and regulatory factors via the spatial Durbin model. The mediating and regulatory effects model examines the transmission mechanism of financial inclusion with a focus on financial literacy, scientific and technological levels, and regulatory quality. The results show that the level of financial inclusion in developed countries is significantly higher than in developing countries. The economic level of developed countries positively impacts financial inclusion in their countries and neighbouring ones. Enhancing financial literacy, science and technological level, and supervision quality improve the development of inclusive finance. While the economic level and urbanization rate in developing countries inhibit the development of financial inclusion, countries with lower economic development and urbanization rates have a greater incentive to develop digital financial inclusion. The improved economic development in developing countries favours financial inclusion in countries nearby. Moreover, financial literacy plays a positive moderating role in the effect of digital finance on financial inclusion. The technology level can exert a transmission effect on financial inclusion through an elevated level of digital finance. The impact of regulatory quality on financial inclusion can be conveyede by creating a stable economic and financial environment and improving economic development levels. This study expands the theoretical research on constructing an inclusive finance evaluation system and its impact mechanism. It provides essential decision-making references for governments, relevant decision-making departments, financial institutions and financial technology enterprises to develop inclusive finance.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1016/j.jfds.2025.100149
Marcel Lee , Alan Spark
Stock returns are moved by many risk factors. Thematic stock indices try to represent these factors, but are limited by the fact that risk factors are not directly observable. This paper introduces a method to uncover hidden risk factors through text analysis. It applies the dynamic variant of the Latent Dirichlet Allocation (LDA) model to annual and quarterly reports to find a topic distribution for each stock. This is then interpreted as the risk factor partition and transformed into a standard normal basis which corresponds to pure risk factors. The weights indicate the proportions necessary to combine the equities into tradable topic indices. The need for human intervention is minimized by determining the optimal parameters automatically.
{"title":"Unsupervised generation of tradable topic indices through textual analysis","authors":"Marcel Lee , Alan Spark","doi":"10.1016/j.jfds.2025.100149","DOIUrl":"10.1016/j.jfds.2025.100149","url":null,"abstract":"<div><div>Stock returns are moved by many risk factors. Thematic stock indices try to represent these factors, but are limited by the fact that risk factors are not directly observable. This paper introduces a method to uncover hidden risk factors through text analysis. It applies the dynamic variant of the <em>Latent Dirichlet Allocation</em> (LDA) model to annual and quarterly reports to find a topic distribution for each stock. This is then interpreted as the risk factor partition and transformed into a standard normal basis which corresponds to pure risk factors. The weights indicate the proportions necessary to combine the equities into tradable topic indices. The need for human intervention is minimized by determining the optimal parameters automatically.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1016/j.jfds.2025.100151
Helge Holden , Lars Holden
The increasing fraction of passive funds influences stock market variability since passive investors behave differently than active investors. We demonstrate via simulations how portfolios that rebalance between different classes of assets influence the market variability. We prove that the optimal strategy for such portfolios when we include transaction costs, is only to rebalance when the portfolio leaves a no-trade region in the state space. This is the case also when the expectation and volatility of the prices are inhomogeneous. We show that portfolios that apply an optimal rebalance strategy reduce the variability in the stock market measured in the sum of the distances between local minimum and maximum of the prices in the stock market, also when these portfolios constitute only a small part of the market. However, the more usual rebalance strategies that only consider to rebalance at the end of a month or a quarter, have a much weaker influence on the market variability.
{"title":"Optimal rebalancing strategies reduce market variability","authors":"Helge Holden , Lars Holden","doi":"10.1016/j.jfds.2025.100151","DOIUrl":"10.1016/j.jfds.2025.100151","url":null,"abstract":"<div><div>The increasing fraction of passive funds influences stock market variability since passive investors behave differently than active investors. We demonstrate via simulations how portfolios that rebalance between different classes of assets influence the market variability. We prove that the optimal strategy for such portfolios when we include transaction costs, is only to rebalance when the portfolio leaves a no-trade region in the state space. This is the case also when the expectation and volatility of the prices are inhomogeneous. We show that portfolios that apply an optimal rebalance strategy reduce the variability in the stock market measured in the sum of the distances between local minimum and maximum of the prices in the stock market, also when these portfolios constitute only a small part of the market. However, the more usual rebalance strategies that only consider to rebalance at the end of a month or a quarter, have a much weaker influence on the market variability.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-09DOI: 10.1016/j.jfds.2025.100150
Xiangwu Zuo, Anxiao (Andrew) Jiang
Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression, etc.), Symbolic Regression does not limit the discovered function to specific forms (e.g., linear functions, polynomials, etc.). Its recent developments are enabling its application to various fields, including both scientific study and engineering research. However, in spite of its flexibility, Symbolic Regression still faces one limitation: given datasets from different systems in the same domain, Symbolic Regression needs to find a distinct function for each dataset, instead of finding a more general yet succinct function that can fit all the datasets through the adjustments of its coefficients. The latter approach, which is termed “Symbolic Modeling” in this work, can be seen as a generalization of Symbolic Regression and has important applications to both academia and industry. This work elucidates Symbolic Modeling and unveils a cutting-edge algorithm, deriving its principles from deep learning and genetic programming. This algorithm is implemented into an application, showcasing its practical utility in the field of financial asset pricing, an integral facet of finance that concentrates on asset valuation. It is shown that Symbolic Modeling compares favorably to existing asset pricing models in multiple aspects.
{"title":"Symbolic Modeling for financial asset pricing","authors":"Xiangwu Zuo, Anxiao (Andrew) Jiang","doi":"10.1016/j.jfds.2025.100150","DOIUrl":"10.1016/j.jfds.2025.100150","url":null,"abstract":"<div><div>Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression, <em>etc.</em>), Symbolic Regression does not limit the discovered function to specific forms (e.g., linear functions, polynomials, <em>etc.</em>). Its recent developments are enabling its application to various fields, including both scientific study and engineering research. However, in spite of its flexibility, Symbolic Regression still faces one limitation: given datasets from different systems in the same domain, Symbolic Regression needs to find a distinct function for each dataset, instead of finding a more general yet succinct function that can fit all the datasets through the adjustments of its coefficients. The latter approach, which is termed “Symbolic Modeling” in this work, can be seen as a generalization of Symbolic Regression and has important applications to both academia and industry. This work elucidates Symbolic Modeling and unveils a cutting-edge algorithm, deriving its principles from deep learning and genetic programming. This algorithm is implemented into an application, showcasing its practical utility in the field of financial asset pricing, an integral facet of finance that concentrates on asset valuation. It is shown that Symbolic Modeling compares favorably to existing asset pricing models in multiple aspects.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"11 ","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.jfds.2025.100153
{"title":"Paper discussion at the 2024 ABFER-JFDS Conference on AI and FinTech","authors":"","doi":"10.1016/j.jfds.2025.100153","DOIUrl":"10.1016/j.jfds.2025.100153","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.jfds.2024.100146
Minwu Kim, Sidahmend Benahderrahmane, Talal Rahwan
This research presents a predictive model to identify potential targets of activist investment funds—entities that acquire significant corporate stakes to influence strategic and operational decisions, ultimately enhancing shareholder value. Predicting such targets is crucial for companies aiming to mitigate intervention risks, activist funds seeking optimal investments, and investors looking to leverage potential stock price gains. Using data from the Russell 3000 index from 2016 to 2022, we evaluated 123 model configurations incorporating diverse imputation, oversampling, and machine learning techniques. Our best model achieved an AUC-ROC of 0.782, demonstrating its capability to effectively predict activist fund targets. To enhance interpretability, we employed the Shapley value method to identify key factors influencing a company’s likelihood of being targeted, highlighting the dynamic mechanisms underlying activist fund target selection. These insights offer a powerful tool for proactive corporate governance and informed investment strategies, advancing understanding of the mechanisms driving activist investment decisions.
{"title":"Interpretable machine learning model for predicting activist investment targets","authors":"Minwu Kim, Sidahmend Benahderrahmane, Talal Rahwan","doi":"10.1016/j.jfds.2024.100146","DOIUrl":"10.1016/j.jfds.2024.100146","url":null,"abstract":"<div><div>This research presents a predictive model to identify potential targets of activist investment funds—entities that acquire significant corporate stakes to influence strategic and operational decisions, ultimately enhancing shareholder value. Predicting such targets is crucial for companies aiming to mitigate intervention risks, activist funds seeking optimal investments, and investors looking to leverage potential stock price gains. Using data from the Russell 3000 index from 2016 to 2022, we evaluated 123 model configurations incorporating diverse imputation, oversampling, and machine learning techniques. Our best model achieved an AUC-ROC of 0.782, demonstrating its capability to effectively predict activist fund targets. To enhance interpretability, we employed the Shapley value method to identify key factors influencing a company’s likelihood of being targeted, highlighting the dynamic mechanisms underlying activist fund target selection. These insights offer a powerful tool for proactive corporate governance and informed investment strategies, advancing understanding of the mechanisms driving activist investment decisions.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.jfds.2024.100145
Markus Leippold , Qian Wang , Min Yang
This paper explores the effectiveness of technical patterns in predicting asset prices and market movements, emphasizing the role of news sentiment. We employ an image recognition method to detect technical patterns in price images and assess whether this approach provides more information than traditional rule-based methods. Our findings indicate that many model-based patterns yield significant returns in the US market, whereas top-type patterns are less effective in the Chinese market. The model demonstrates high accuracy in training samples and strong out-of-sample performance. Our empirical analysis concludes that technical patterns remain effective in recent stock markets when combined with news sentiment, offering a profitable portfolio strategy. Moreover, we find patterns better predict returns for firms with high momentum, institutional ownership, and prior patterns in US, while in China, they are more effective for small firms with high momentum and institutional ownership. This study highlights the potential of image recognition methods in market data analysis and underscores the importance of sentiment in technical analysis.
{"title":"Technical patterns and news sentiment in stock markets","authors":"Markus Leippold , Qian Wang , Min Yang","doi":"10.1016/j.jfds.2024.100145","DOIUrl":"10.1016/j.jfds.2024.100145","url":null,"abstract":"<div><div>This paper explores the effectiveness of technical patterns in predicting asset prices and market movements, emphasizing the role of news sentiment. We employ an image recognition method to detect technical patterns in price images and assess whether this approach provides more information than traditional rule-based methods. Our findings indicate that many model-based patterns yield significant returns in the US market, whereas top-type patterns are less effective in the Chinese market. The model demonstrates high accuracy in training samples and strong out-of-sample performance. Our empirical analysis concludes that technical patterns remain effective in recent stock markets when combined with news sentiment, offering a profitable portfolio strategy. Moreover, we find patterns better predict returns for firms with high momentum, institutional ownership, and prior patterns in US, while in China, they are more effective for small firms with high momentum and institutional ownership. This study highlights the potential of image recognition methods in market data analysis and underscores the importance of sentiment in technical analysis.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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