International Journal of Forecasting最新文献

Benchmarking M6 competitors: An analysis of financial metrics and discussion of incentives M6竞争对手的基准：财务指标的分析和激励的讨论

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-08-29 DOI: 10.1016/j.ijforecast.2025.03.008

Matthew J. Schneider , Rufus Rankin , Prabir Burman , Alexander Aue

{"title":"Benchmarking M6 competitors: An analysis of financial metrics and discussion of incentives","authors":"Matthew J. Schneider , Rufus Rankin , Prabir Burman , Alexander Aue","doi":"10.1016/j.ijforecast.2025.03.008","DOIUrl":"10.1016/j.ijforecast.2025.03.008","url":null,"abstract":"<div><div>The M6 Competition assessed the performance of competitors using a ranked probability score and an information ratio. While these metrics do well at picking the winners in the competition, crucial questions remain for investors with longer-term incentives. To address these questions, we compare the competitors’ performance with a number of conventional (long-only) and alternative indices using industry-relevant metrics. We apply factor models to measure the competitors’ value-adds above industry-standard benchmarks and find that competitors with more extreme performance are less dependent on the benchmarks. We further introduce two new strategies by picking the competitors with the best (Superstars) and worst (Superlosers) recent performance and show that it is challenging to identify skill amongst investment managers. Finally, we discuss the incentives of winning the competition compared with professional investors, where investors wish to maximize fees over an extended period of time, and provide suggestions for future competition improvements.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1383-1394"},"PeriodicalIF":7.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition 动态市场中的自适应预测：AutoTS在M6竞争中的评价

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-08-28 DOI: 10.1016/j.ijforecast.2025.08.004

Colin Catlin

{"title":"Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition","authors":"Colin Catlin","doi":"10.1016/j.ijforecast.2025.08.004","DOIUrl":"10.1016/j.ijforecast.2025.08.004","url":null,"abstract":"<div><div>In contemporary forecasting, the challenges of navigating the intricacies of erratic human-induced patterns combine with the challenges of navigating the overwhelming number of methods and models available to manage these data. The M6 Competition, which emphasized repeated, real-time monthly forecasting of stock markets, featured many of these difficulties. Here, AutoTS, an open-source Python package designed specifically for probabilistic time series predictions, is evaluated within the context of this competition. AutoTS includes an extensive repertoire of models, augmented by robust data preprocessing utilities, and employs genetic algorithms to fine-tune model parameters, contingent upon user-delineated evaluation metrics. This study describes the deployment of AutoTS in the M6 Competition, which won the investment decision challenge, and outlines the model selection pipeline and the process of converting forecasts into decisions which produced this result. Although a single definitive model remains elusive, these findings underscore the potential value of methodologies that are dynamic and largely autonomous.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1485-1493"},"PeriodicalIF":7.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introduction to the M6 forecasting competition Special Issue M6预报比赛特刊简介

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-08-27 DOI: 10.1016/j.ijforecast.2025.07.006

Spyros Makridakis, Fotios Petropoulos, Evangelos Spiliotis, Norman R. Swanson

引用次数: 0

Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition 在M6预测竞赛结果中揭示参与和一致性的影响

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-05-14 DOI: 10.1016/j.ijforecast.2025.04.002

Anastasios Kaltsounis, Evangelos Theodorou, Evangelos Spiliotis, Vassilios Assimakopoulos

引用次数: 0

M6 investment challenge: The role of luck and strategic considerations M6投资挑战：运气的作用和战略考虑

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-04-30 DOI: 10.1016/j.ijforecast.2025.03.005

Filip Staněk

{"title":"M6 investment challenge: The role of luck and strategic considerations","authors":"Filip Staněk","doi":"10.1016/j.ijforecast.2025.03.005","DOIUrl":"10.1016/j.ijforecast.2025.03.005","url":null,"abstract":"<div><div>This article investigates the influence of luck and strategic considerations on the performance of teams participating in the M6 investment challenge. We find that there is insufficient evidence to suggest that the extreme Sharpe ratios observed are beyond what one would expect by chance, given the number of teams, and thus not necessarily indicative of the possibility of consistently attaining abnormal returns. These findings are consistent with the efficient-market hypothesis, reinforcing the notion that any apparent outperformance is indistinguishable from statistical noise. Furthermore, we introduce a stylized model of the competition to derive and analyze a portfolio strategy optimized for attaining the top rank. The results demonstrate that the task of achieving the top rank is not necessarily identical to that of attaining the best investment returns in expectation. It is possible to improve one’s chances of winning, even without the ability to attain abnormal returns, by constructing a portfolio that deviates from the strategies of competitors. Empirical analysis of submitted portfolios shows that teams that differentiated themselves from competitors by holding a higher proportion of short positions were more than eight times as likely to secure a top rank, aligning with findings from the stylized model.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1413-1427"},"PeriodicalIF":7.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of models and methods used for forecasting when investing in financial markets 对金融市场投资时用于预测的模型和方法的调查

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-04-11 DOI: 10.1016/j.ijforecast.2025.03.002

Kenwin Maung, Norman R. Swanson

{"title":"A survey of models and methods used for forecasting when investing in financial markets","authors":"Kenwin Maung, Norman R. Swanson","doi":"10.1016/j.ijforecast.2025.03.002","DOIUrl":"10.1016/j.ijforecast.2025.03.002","url":null,"abstract":"<div><div>The <em>Makridakis M6 Financial Duathalon</em><span><span> competition builds on prior M-competitions that focus on the properties of point and probabilistic forecasts of random variables<span> by also evaluating investment decisions in financial markets. In particular, the M6 competition evaluates both forecasts and investment outcomes associated with the analysis of a large group of financial time series<span> variables. Given the importance of return and risk forecasting when making investment decisions, a natural question in this context concerns what sorts of methods and models are available for said forecasting and were used by participants of the competition. In this survey, we discuss such methods and models, with a specific focus on the construction of financial time series forecasts using approaches designed for both discrete and continuous time setups and using both small and large (high dimensional and/or high frequency) datasets. Examples covered range from simple random walk-type models of returns to parametric </span></span></span>GARCH<span> and nonparametric integrated volatility methods for forecasting volatility (risk). We also present the results of a novel empirical illustration that underscores the difficulty in forecasting financial returns, even when using so-called big data.</span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1355-1382"},"PeriodicalIF":7.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Australian federal electoral seats with machine learning 用机器学习预测澳大利亚联邦选举席位

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-03-21 DOI: 10.1016/j.ijforecast.2025.02.002

John ‘Jack’ Collins

引用次数: 0

Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data 深度学习和NLP在加密货币预测中的应用：整合金融、区块链和社交媒体数据

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-03-18 DOI: 10.1016/j.ijforecast.2025.02.007

Vincent Gurgul , Stefan Lessmann , Wolfgang Karl Härdle

{"title":"Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data","authors":"Vincent Gurgul , Stefan Lessmann , Wolfgang Karl Härdle","doi":"10.1016/j.ijforecast.2025.02.007","DOIUrl":"10.1016/j.ijforecast.2025.02.007","url":null,"abstract":"<div><div>We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: <span><span>https://anonymous.4open.science/r/crypto-forecasting-public</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1666-1695"},"PeriodicalIF":7.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disaggregating VIX 将波动率指数

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-03-18 DOI: 10.1016/j.ijforecast.2025.01.007

Stavros Degiannakis , Eleftheria Kafousaki

引用次数: 0

Election forecasting: Political economy models 选举预测：政治经济模型

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-03-18 DOI: 10.1016/j.ijforecast.2025.02.006

Michael S. Lewis-Beck , John Kenny , Debra Leiter , Andreas Erwin Murr , Onyinye B. Ogili , Mary Stegmaier , Charles Tien

引用次数: 0