International Journal of Forecasting最新文献_第4页

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 : 2025-10-01 Epub 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":"2025-10-01","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

Robust returns ranking prediction and portfolio optimization for M6 M6 的稳健收益排名预测和投资组合优化

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2024-04-25 DOI: 10.1016/j.ijforecast.2024.04.004

Hongfeng Ai , Chenning Liu , Peng Lin

{"title":"Robust returns ranking prediction and portfolio optimization for M6","authors":"Hongfeng Ai , Chenning Liu , Peng Lin","doi":"10.1016/j.ijforecast.2024.04.004","DOIUrl":"10.1016/j.ijforecast.2024.04.004","url":null,"abstract":"<div><div><span>The M6 competition aims to address challenging problems in stock returns ranking prediction and portfolio optimization. To tackle the volatility and low signal-to-noise ratio in the stock market, our team designs the overall solution from the robustness perspective. Regarding returns ranking prediction, we present the MultiTask </span>Deep Neural Network<span><span> with Denoising </span>Autoencoder<span><span> Enhancement (MT-DNN-DAE), which incorporates the self-supervised learning of DAE and jointly optimizes the multi-task loss. We propose Robust Feature Selection (RFS) to identify features with a high signal-to-noise ratio for DAE’s representation learning. We construct a separate branch for important ID features to prevent information loss. Results show our solution can accurately predict returns ranking while maintaining generalization. On the task of portfolio optimization, a </span>Differential Evolution algorithm is presented to optimize asset allocation and maximize returns under risk constraints, demonstrating improved performance over traditional techniques. These methods led to a 4th place global ranking in the M6 competition.</span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1494-1504"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794038","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 the relative performance among financial assets: A comparative analysis of different approaches 预测金融资产的相对表现：不同方法的比较分析

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.008

Panagiotis Samartzis

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 : 2025-10-01 Epub Date: 2025-03-21 DOI: 10.1016/j.ijforecast.2025.02.002

John ‘Jack’ Collins

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 : 2025-10-01 Epub 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

An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition 概率预测的自适应波动率方法及其在M6金融预测竞赛中的应用

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2024-06-22 DOI: 10.1016/j.ijforecast.2024.06.001

Joseph de Vilmarest , Nicklas Werge

{"title":"An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition","authors":"Joseph de Vilmarest , Nicklas Werge","doi":"10.1016/j.ijforecast.2024.06.001","DOIUrl":"10.1016/j.ijforecast.2024.06.001","url":null,"abstract":"<div><div>In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stochastic optimization algorithms. These principles were successfully applied in the M6 forecasting competition under the team named <em>AdaGaussMC</em>. Our approach takes a unique path by embracing the Efficient Market Hypothesis (EMH) instead of trying to beat the market directly. We focus on evaluating the efficient market and emphasize the importance of online forecasting in adapting to the dynamic nature of financial markets. The three key points of our approach are: (a) apply the univariate time-varying volatility model AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We contend that the simplicity of our approach contributes to its robustness and consistency. Our performance in the M6 competition resulted in an overall 7<sup>th</sup> place, with a 5<sup>th</sup> place in the forecasting task. Considering our approach’s perceived simplicity, this achievement underscores the efficacy of our adaptive volatility method in probabilistic forecasting.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1514-1520"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020567","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 : 2025-10-01 Epub Date: 2025-03-18 DOI: 10.1016/j.ijforecast.2025.01.007

Stavros Degiannakis , Eleftheria Kafousaki

引用次数: 0

Single-stage portfolio optimization with automated machine learning for M6 单阶段投资组合优化与自动机器学习的M6

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2024-09-14 DOI: 10.1016/j.ijforecast.2024.08.004

Xinyu Huang , David P. Newton , Emmanouil Platanakis , Charles Sutcliffe

引用次数: 0

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 : 2025-10-01 Epub 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":"2025-10-01","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

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 : 2025-10-01 Epub 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":"2025-10-01","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