Pub Date : 2024-12-01DOI: 10.1016/j.jfds.2025.100152
Yi Huang, Sung Kwan Lee, Bernard Yeung
{"title":"Learning from AI-Finance: A selected synopsis","authors":"Yi Huang, Sung Kwan Lee, Bernard Yeung","doi":"10.1016/j.jfds.2025.100152","DOIUrl":"10.1016/j.jfds.2025.100152","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510739","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.2024.100147
Marzhan Alenova, Assem Utaliyeva, Ki-Joune Li
Hawala, a traditional but informal money transfer system, has been prevalent in many parts of the world, such as money laundering. Despite the regulatory actions taken by financial institutions, Hawala is still a key node in terror financing schemes and its extent of misuse is unknown. Due to the hidden transactions and limited knowledge about the Hawala, it is difficult for legal enforcement authorities such as financial intelligence units (FIU) of each country to detect and investigate the Hawala network. In this paper, we present a novel approach to detect the potential Hawala instances in the stream of financial transaction data by using graph mining techniques. In order to reflect the properties of Hawala, we apply graph mining methods such as graph centrality, Blackhole metric, and Hidden link metric as well as anomaly detection methods using graph convolutional network. Experiments demonstrate that the proposed method gives a meaningful result in detecting Hawala network and can be used as a complementary tool to the existing transactional monitoring tracks.
{"title":"Detecting Hawala network for money laundering by graph mining","authors":"Marzhan Alenova, Assem Utaliyeva, Ki-Joune Li","doi":"10.1016/j.jfds.2024.100147","DOIUrl":"10.1016/j.jfds.2024.100147","url":null,"abstract":"<div><div>Hawala, a traditional but informal money transfer system, has been prevalent in many parts of the world, such as money laundering. Despite the regulatory actions taken by financial institutions, Hawala is still a key node in terror financing schemes and its extent of misuse is unknown. Due to the hidden transactions and limited knowledge about the Hawala, it is difficult for legal enforcement authorities such as financial intelligence units (FIU) of each country to detect and investigate the Hawala network. In this paper, we present a novel approach to detect the potential Hawala instances in the stream of financial transaction data by using graph mining techniques. In order to reflect the properties of Hawala, we apply graph mining methods such as graph centrality, Blackhole metric, and Hidden link metric as well as anomaly detection methods using graph convolutional network. Experiments demonstrate that the proposed method gives a meaningful result in detecting Hawala network and can be used as a complementary tool to the existing transactional monitoring tracks.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175392","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.2025.100154
{"title":"Post notes of 2024 ABFER-JFDS conference on AI and FinTech","authors":"","doi":"10.1016/j.jfds.2025.100154","DOIUrl":"10.1016/j.jfds.2025.100154","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510738","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.2024.100135
Hugo Gobato Souto
{"title":"Corrigendum to “Topological tail dependence: evidence from forecasting realized volatility” [The Journal of Finance and Data Science 9 (2023) 100107]","authors":"Hugo Gobato Souto","doi":"10.1016/j.jfds.2024.100135","DOIUrl":"10.1016/j.jfds.2024.100135","url":null,"abstract":"","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141404142","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.2024.100148
Mustafa Pala, Emre Sefer
Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework.
Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at https://github.com/seferlab/deep_nft.
{"title":"NFT price and sales characteristics prediction by transfer learning of visual attributes","authors":"Mustafa Pala, Emre Sefer","doi":"10.1016/j.jfds.2024.100148","DOIUrl":"10.1016/j.jfds.2024.100148","url":null,"abstract":"<div><div>Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework.</div><div>Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at <span><span>https://github.com/seferlab/deep_nft</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175785","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.2024.100143
Hugo Gobato Souto , Storm Koert Heuvel , Francisco Louzada Neto
This study evaluates the effectiveness of the TSMixer neural network model in forecasting stock realized volatility, comparing it with traditional and contemporary benchmark models. Using data from S&P 100 index stocks and three other datasets containing various financial securities, extensive analyses, including robustness tests, were conducted. Results show that TSMixer outperforms benchmark models in predicting individual stock volatility when applied to datasets with a large number of securities, leveraging its feature-mixing MLP techniques, which can properly model the financial tail dependence phenomenon. However, its superiority diminishes in datasets with fewer securities, such as stock indexes, foreign exchange rates, and commodities, where models like NBEATSx and NHITS often perform better. This indicates that TSMixer's performance is context-dependent, excelling when feature interdependencies can be fully exploited. The findings suggest that simplified neural network architectures like TSMixer can enhance forecasting accuracy in appropriate contexts but may have limitations in datasets with fewer securities.
{"title":"Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model","authors":"Hugo Gobato Souto , Storm Koert Heuvel , Francisco Louzada Neto","doi":"10.1016/j.jfds.2024.100143","DOIUrl":"10.1016/j.jfds.2024.100143","url":null,"abstract":"<div><div>This study evaluates the effectiveness of the TSMixer neural network model in forecasting stock realized volatility, comparing it with traditional and contemporary benchmark models. Using data from S&P 100 index stocks and three other datasets containing various financial securities, extensive analyses, including robustness tests, were conducted. Results show that TSMixer outperforms benchmark models in predicting individual stock volatility when applied to datasets with a large number of securities, leveraging its feature-mixing MLP techniques, which can properly model the financial tail dependence phenomenon. However, its superiority diminishes in datasets with fewer securities, such as stock indexes, foreign exchange rates, and commodities, where models like NBEATSx and NHITS often perform better. This indicates that TSMixer's performance is context-dependent, excelling when feature interdependencies can be fully exploited. The findings suggest that simplified neural network architectures like TSMixer can enhance forecasting accuracy in appropriate contexts but may have limitations in datasets with fewer securities.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175786","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 : 2024-10-22DOI: 10.1016/j.jfds.2024.100142
Tingyu Qu
In financial markets, tail risks, representing the potential for substantial losses, bear significant implications for the formulation of effective risk management strategies. Yet, there exists a notable gap in understanding the interconnectedness within the global market, particularly when analysing time-series tail data. This study introduces a reliable method for identifying events indicative of tail transitions in financial time-series data. The investigation suggests consistent patterns governing extreme events across diverse industries and different time periods, suggestive of the financial contagion in tail risks. Importantly, time-series tail slopes in specific stocks emerge as viable predictors of price fluctuations in others. These findings offer valuable insights for portfolio diversification and risk mitigation in the interconnected financial market.
{"title":"Tail-driven portfolios: Unveiling financial contagion and enhancing risk management","authors":"Tingyu Qu","doi":"10.1016/j.jfds.2024.100142","DOIUrl":"10.1016/j.jfds.2024.100142","url":null,"abstract":"<div><div>In financial markets, tail risks, representing the potential for substantial losses, bear significant implications for the formulation of effective risk management strategies. Yet, there exists a notable gap in understanding the interconnectedness within the global market, particularly when analysing time-series tail data. This study introduces a reliable method for identifying events indicative of tail transitions in financial time-series data. The investigation suggests consistent patterns governing extreme events across diverse industries and different time periods, suggestive of the financial contagion in tail risks. Importantly, time-series tail slopes in specific stocks emerge as viable predictors of price fluctuations in others. These findings offer valuable insights for portfolio diversification and risk mitigation in the interconnected financial market.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659614","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 : 2024-10-22DOI: 10.1016/j.jfds.2024.100144
Mikhail Stolbov , Maria Shchepeleva
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.
{"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":"2024-10-22","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}
Pub Date : 2024-10-18DOI: 10.1016/j.jfds.2024.100141
Christian Tausch, Marcus Pietz
In this paper, we use two machine learning techniques to learn the aggregated return time series of complete private capital fund segments. First, we propose Stochastic Discount Factor (SDF) model combination to determine the public factor exposure of private equity. Here, we describe our theoretical motivation to favor model combination over model selection. This entails that we apply simple coefficient averaging to obtain multivariate SDF models that mimic the factor exposure of all major private capital fund types. As a second step, we suggest componentwise L2 boosting to estimate the error-term time series associated with our factor models. The simple addition of the public factor model returns and the error terms then yields the total return time series. These return time series can be applied for proper integrated public and private risk management or benchmarking.
{"title":"Machine learning private equity returns","authors":"Christian Tausch, Marcus Pietz","doi":"10.1016/j.jfds.2024.100141","DOIUrl":"10.1016/j.jfds.2024.100141","url":null,"abstract":"<div><div>In this paper, we use two machine learning techniques to learn the aggregated return time series of complete private capital fund segments. First, we propose Stochastic Discount Factor (SDF) model combination to determine the public factor exposure of private equity. Here, we describe our theoretical motivation to favor model combination over model selection. This entails that we apply simple coefficient averaging to obtain multivariate SDF models that mimic the factor exposure of all major private capital fund types. As a second step, we suggest componentwise <em>L</em><sub>2</sub> boosting to estimate the error-term time series associated with our factor models. The simple addition of the public factor model returns and the error terms then yields the total return time series. These return time series can be applied for proper integrated public and private risk management or benchmarking.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538739","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 : 2024-10-10DOI: 10.1016/j.jfds.2024.100140
Peipei Li , Yuan Wang , Licheng Zhang , Xueying Zhang
This paper studies the impact of house prices on the distribution of GDP growth in China (the 5th, median, and 95th percentiles). We show that house price appre-ciation positively affects future GDP growth, with a more significant impact on the tail outcomes - GDP at risk. Moreover, we find that housing bust is associated with GDP growth vulnerability; a sharp decline in house prices generates severe economic downturns. Our finding is supported by the investment channel, a housing boom stim-ulates investment, which boosts GDP growth. However, the subsequent housing bust suppresses investment, leading to increased downside risks to GDP growth.
本文研究了房价对中国 GDP 增长分布（第 5、中位数和第 95 百分位数）的影响。我们的研究表明，房价预期对未来 GDP 增长有积极影响，对尾部结果--风险 GDP--的影响更为显著。此外，我们还发现住房萧条与 GDP 增长的脆弱性相关联；房价急剧下降会导致严重的经济衰退。我们的发现得到了投资渠道的支持，房地产繁荣刺激了投资，从而推动了国内生产总值的增长。然而，随后的房地产萧条抑制了投资，导致 GDP 增长的下行风险增加。
{"title":"China's GDP at risk: The role of housing prices","authors":"Peipei Li , Yuan Wang , Licheng Zhang , Xueying Zhang","doi":"10.1016/j.jfds.2024.100140","DOIUrl":"10.1016/j.jfds.2024.100140","url":null,"abstract":"<div><div>This paper studies the impact of house prices on the distribution of GDP growth in China (the 5th, median, and 95th percentiles). We show that house price appre-ciation positively affects future GDP growth, with a more significant impact on the tail outcomes - GDP at risk. Moreover, we find that housing bust is associated with GDP growth vulnerability; a sharp decline in house prices generates severe economic downturns. Our finding is supported by the investment channel, a housing boom stim-ulates investment, which boosts GDP growth. However, the subsequent housing bust suppresses investment, leading to increased downside risks to GDP growth.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527028","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号