Journal of Finance and Data Science最新文献_第3页

Explicit formulae for the valuation of European options with price impacts 具有价格影响的欧式期权估值的明确公式

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-05-24 DOI: 10.1016/j.jfds.2024.100133

Gerardo Hernández-del-Valle , Julio César Rodríguez-Burgos , Héctor Jasso-Fuentes

{"title":"Explicit formulae for the valuation of European options with price impacts","authors":"Gerardo Hernández-del-Valle , Julio César Rodríguez-Burgos , Héctor Jasso-Fuentes","doi":"10.1016/j.jfds.2024.100133","DOIUrl":"10.1016/j.jfds.2024.100133","url":null,"abstract":"<div><p>In this work, we examine the consequences of trading a large position in vanilla European options within a multi-period binomial model framework for the underlying asset price, <em>S</em>. Given the significant size of the transaction, we expect both the derivative's price and the underlying asset's price to be affected by market impacts. Consequently, derivative valuation should incorporate these effects. To address this, we not only utilize a multi-period binomial model to represent the price process <em>S</em> but also incorporate trading impacts in a multiplicative manner.</p><p>Moreover, we conduct our analysis in discrete time to better capture the influence of price impacts. Our findings suggest, for instance, that the strike price should be determined by both the trade's magnitude and parameterized market impacts. We present explicit formulas for European option prices under market impacts and offer numerical examples to elucidate our findings. Upon request, we can provide code implemented in the statistical package <em>R</em>.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000187/pdfft?md5=e4f7c9fff11deba41d42f03de17167a5&pid=1-s2.0-S2405918824000187-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141134202","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}

引用次数: 0

Paper discussion at the 2024 ABFER-JFDS Conference on AI and FinTech 在2024 ABFER-JFDS人工智能与金融科技会议上的论文讨论

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2025-02-14 DOI: 10.1016/j.jfds.2025.100153

引用次数: 0

Interpretable machine learning model for predicting activist investment targets 预测激进投资目标的可解释机器学习模型

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-11-19 DOI: 10.1016/j.jfds.2024.100146

Minwu Kim, Sidahmend Benahderrahmane, Talal Rahwan

{"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}

引用次数: 0

Tail-driven portfolios: Unveiling financial contagion and enhancing risk management 尾部驱动的投资组合：揭示金融传染和加强风险管理

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-10-22 DOI: 10.1016/j.jfds.2024.100142

Tingyu Qu

引用次数: 0

Technical patterns and news sentiment in stock markets 股票市场的技术形态和新闻情绪

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-11-16 DOI: 10.1016/j.jfds.2024.100145

Markus Leippold , Qian Wang , Min Yang

{"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}

引用次数: 0

Learning from AI-Finance: A selected synopsis 向人工智能金融学习：节选摘要

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2025-02-14 DOI: 10.1016/j.jfds.2025.100152

Yi Huang, Sung Kwan Lee, Bernard Yeung

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Post notes of 2024 ABFER-JFDS conference on AI and FinTech 2024 ABFER-JFDS人工智能与金融科技会议纪要

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2025-02-14 DOI: 10.1016/j.jfds.2025.100154

引用次数: 0

Do commodity prices matter for global systemic risk? Evidence from ML variable selection 商品价格对全球系统性风险重要吗？多变量选择的证据

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-10-22 DOI: 10.1016/j.jfds.2024.100144

Mikhail Stolbov , Maria Shchepeleva

{"title":"Do commodity prices matter for global systemic risk? Evidence from ML variable selection","authors":"Mikhail Stolbov , Maria Shchepeleva","doi":"10.1016/j.jfds.2024.100144","DOIUrl":"10.1016/j.jfds.2024.100144","url":null,"abstract":"<div><div>We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December 2021. The search is based on a battery of ML variable selection algorithms which apply both to price levels and price shocks in the presence of control variables, including the first lag of SRISK, world industrial production, global economic policy uncertainty, geopolitical risk as well as the global stance of monetary and macroprudential policies. We find that these controls outweigh commodity prices as the predictors of global systemic risk. Of the commodities themselves, the prices for agricultural commodities, including food, e.g. chicken, bananas, beef, tea, cocoa, are more important predictors of global systemic risk than the prices for energy commodities, e.g. natural gas and oil prices. The financialization of agricultural commodities, bio-energy expansion as well as commodity-specific dependence of the major economies contributing to global systemic risk, e.g. China, account for our main finding. We also document the positive linkage between commodity prices and systemic risk for the majority of commodities. Thus, monitoring commodity prices to avoid their unbalanced growth is of vast importance to curb global systemic financial risk.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571479","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}

引用次数: 0

Detecting Hawala network for money laundering by graph mining 利用图挖掘检测Hawala网络的洗钱行为

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-11-16 DOI: 10.1016/j.jfds.2024.100147

Marzhan Alenova, Assem Utaliyeva, Ki-Joune Li

引用次数: 0

Cluster-based regression using variational inference and applications in financial forecasting 使用变异推理的聚类回归及其在金融预测中的应用

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-05-08 DOI: 10.1016/j.jfds.2024.100130

Udai Nagpal , Krishan Nagpal

{"title":"Cluster-based regression using variational inference and applications in financial forecasting","authors":"Udai Nagpal , Krishan Nagpal","doi":"10.1016/j.jfds.2024.100130","DOIUrl":"https://doi.org/10.1016/j.jfds.2024.100130","url":null,"abstract":"<div><p>This paper describes an approach to simultaneously identify clusters and estimate cluster-specific regression parameters from the given data. Such an approach can be useful in learning the relationship between input and output when the regression parameters for estimating output are different in different regions of the input space. Variational Inference (VI), a machine learning approach to obtain posterior probability densities using optimization techniques, is used to identify clusters of explanatory variables and regression parameters for each cluster. From these results, one can obtain both the expected value and the full distribution of predicted output. Other advantages of the proposed approach include the elegant theoretical solution and clear interpretability of results. The proposed approach is well-suited for financial forecasting where markets have different regimes (or clusters) with different patterns and correlations of market changes in each regime. In financial applications, knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. An illustrative example of predicting one-day S&P change is considered to illustrate the approach and compare the performance of the proposed approach with standard regression without clusters. Due to the broad applicability of the problem, its elegant theoretical solution, and the computational efficiency of the proposed algorithm, the approach may be useful in a number of areas extending beyond the financial domain.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000151/pdfft?md5=d14569ce823f6d454daa2b2a1c4bdb82&pid=1-s2.0-S2405918824000151-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141163946","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}

引用次数: 0