Journal of Finance and Data Science最新文献_第2页

NFT price and sales characteristics prediction by transfer learning of visual attributes

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100148

Mustafa Pala, Emre Sefer

{"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}

引用次数: 0

Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100143

Hugo Gobato Souto , Storm Koert Heuvel , Francisco Louzada Neto

{"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}

引用次数: 0

Corrigendum to “Topological tail dependence: evidence from forecasting realized volatility” [The Journal of Finance and Data Science 9 (2023) 100107] 拓扑尾部依赖性：预测实现波动率的证据》[《金融与数据科学杂志》9 (2023) 100107] 更正

Journal of Finance and Data Science Pub Date : 2024-12-01 DOI: 10.1016/j.jfds.2024.100135

Hugo Gobato Souto

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 : 2024-10-22 DOI: 10.1016/j.jfds.2024.100142

Tingyu Qu

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 : 2024-10-22 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":"2024-10-22","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}

引用次数: 0

Machine learning private equity returns 机器学习私募股权投资回报

Journal of Finance and Data Science Pub Date : 2024-10-18 DOI: 10.1016/j.jfds.2024.100141

Christian Tausch, Marcus Pietz

引用次数: 0

China's GDP at risk: The role of housing prices 中国的 GDP 面临风险：房价的作用

Journal of Finance and Data Science Pub Date : 2024-10-10 DOI: 10.1016/j.jfds.2024.100140

Peipei Li , Yuan Wang , Licheng Zhang , Xueying Zhang

引用次数: 0

What drives liquidity in the Chinese credit bond markets? 是什么推动了中国信用债券市场的流动性？

Journal of Finance and Data Science Pub Date : 2024-10-09 DOI: 10.1016/j.jfds.2024.100139

Jingyuan Mo , Marti G. Subrahmanyam

引用次数: 0

Liquidity risk analysis via drawdown-based measures 以缩减为基础的流动性风险分析

Journal of Finance and Data Science Pub Date : 2024-09-24 DOI: 10.1016/j.jfds.2024.100138

Guglielmo D'Amico, Bice Di Basilio, Filippo Petroni

引用次数: 0

Reinforcement prompting for financial synthetic data generation 金融合成数据生成的强化提示

Journal of Finance and Data Science Pub Date : 2024-08-03 DOI: 10.1016/j.jfds.2024.100137

Xiangwu Zuo , Anxiao (Andrew) Jiang , Kaixiong Zhou

{"title":"Reinforcement prompting for financial synthetic data generation","authors":"Xiangwu Zuo , Anxiao (Andrew) Jiang , Kaixiong Zhou","doi":"10.1016/j.jfds.2024.100137","DOIUrl":"10.1016/j.jfds.2024.100137","url":null,"abstract":"<div><p>The emergence of Large Language Models (LLMs) has unlocked unprecedented potential for comprehending and generating human-like text, fueling advances in the finance domain – a tool that can shape investment strategies and market predictions. Nevertheless, challenges stemming from the necessity for extensive labeled data and the imperative for data privacy remain. The generation of high-quality synthetic data emerges as a promising avenue to circumvent these issues. In this paper, we introduce a novel methodology, named “Reinforcement Prompting”, to address these challenges. Our strategy employs a policy network as a Selector to generate prompts, and an LLM as an Executor to produce financial synthetic data. This synthetic data generation process preserves data privacy and mitigates the dependency on real-world labeled datasets. We validate the effectiveness of our approach through experimental evaluations. Our results indicate that models trained on synthetic data generated via our approach exhibit competitive performance when compared to those trained on actual financial data, thereby bridging the performance gap. This research provides a novel solution to the challenges of data privacy and labeled data scarcity in financial sentiment analysis, offering considerable advancement in the field of financial machine learning.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000229/pdfft?md5=00bc590d50782ff3979a1146c9c7d2aa&pid=1-s2.0-S2405918824000229-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992690","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