Journal of Forecasting最新文献_第8页

Harnessing volatility cascades with ensemble learning 通过集合学习驾驭波动级联

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-06-09 DOI: 10.1002/for.3166

Mingmian Cheng

{"title":"Harnessing volatility cascades with ensemble learning","authors":"Mingmian Cheng","doi":"10.1002/for.3166","DOIUrl":"https://doi.org/10.1002/for.3166","url":null,"abstract":"This paper introduces a simple yet effective modification to bootstrap aggregation (bagging) and boosting techniques, aimed at addressing substantial errors arising from parameter estimation, particularly prevalent in macroeconomic and financial forecasting. We propose “egalitarian” bagging and boosting algorithms, where forecasts are derived through an equally weighted combination scheme following variable selection procedures, rather than relying on estimated model parameters. Our empirical work focuses on volatility forecasting, where our approach is applied to a hierarchical model that aggregates a diverse array of volatility components over different time intervals. Significant improvements in predictive accuracy are observed when conventional bagging and boosting approaches are replaced by their “egalitarian” counterparts, across a range of assets and forecast horizons. Notably, these improvements are most pronounced during periods of financial market turmoil, particularly for medium- to long-term predictions. In contrast to boosting, which often yields a sparse model specification, bagging effectively leverages a diverse range of volatility cascades to capture rich information without succumbing to increasing estimation errors. The proposed “egalitarian” algorithm plays a crucial role in facilitating this process, contributing to the superior performance of bagging over other competing approaches.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"2954-2981"},"PeriodicalIF":3.4,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Forecasting interval-valued returns of crude oil: A novel kernel-based approach 预测原油的区间值回报：基于核的新方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-06-04 DOI: 10.1002/for.3167

Kun Yang, Xueqing Xu, Yunjie Wei, Shouyang Wang

{"title":"Forecasting interval-valued returns of crude oil: A novel kernel-based approach","authors":"Kun Yang, Xueqing Xu, Yunjie Wei, Shouyang Wang","doi":"10.1002/for.3167","DOIUrl":"10.1002/for.3167","url":null,"abstract":"This paper proposes a novel kernel-based generalized random interval multilayer perceptron (KG-iMLP) method for predicting high-volatility interval-valued returns of crude oil. The KG-iMLP model is constructed by utilizing the \u0000<math>\u0000 <msub>\u0000 <mi>D</mi>\u0000 <mi>K</mi>\u0000 </msub></math> distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval-valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward \u0000<math>\u0000 <msub>\u0000 <mi>D</mi>\u0000 <mi>K</mi>\u0000 </msub></math> distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG-iMLP for crude oil price forecasting.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"2937-2953"},"PeriodicalIF":3.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Structured multifractal scaling of the principal cryptocurrencies: Examination using a self-explainable machine learning 主要加密货币的结构多分形缩放：使用可自我解释的机器学习进行检验

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-06-01 DOI: 10.1002/for.3168

Foued Saâdaoui, Hana Rabbouch

{"title":"Structured multifractal scaling of the principal cryptocurrencies: Examination using a self-explainable machine learning","authors":"Foued Saâdaoui, Hana Rabbouch","doi":"10.1002/for.3168","DOIUrl":"10.1002/for.3168","url":null,"abstract":"This paper introduces a novel statistical testing technique known as segmented detrended multifractal fluctuation analysis (SMF-DFA) to analyze the structured scaling properties of financial returns and predict the long-term memory of financial markets. The proposed methodology is applied to assess the efficiency of major cryptocurrencies, expanding upon conventional approaches by incorporating different fluctuation regimes identified through a change-point detection test. A single-factor model is employed to characterize the endogenous factors influencing scaling behavior, leading to the development of a self-explanatory machine learning approach for price forecasting. The proposed method is evaluated using daily data from three major cryptocurrencies spanning from April 2017 to December 2022. The analysis aims to determine whether the digital market has experienced significant changes in recent years and assess whether this has resulted in structured multifractal behavior. The study identifies common periods of local scaling among the three prices, with a noticeable decrease in multifractality observed after 2018. Furthermore, complementary tests on shuffled and surrogate data are conducted to explore the distribution, linear correlation, and nonlinear structure, shedding light on the explanation of structured multifractality to some extent. Additionally, prediction experiments based on neural networks fed with multi-fractionally differentiated data demonstrate the utility of this new self-explanatory algorithm for decision-makers and investors seeking more accurate and interpretable forecasts.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2917-2934"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning 预测比特币收益：计量经济学时间序列分析与机器学习

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-31 DOI: 10.1002/for.3165

Theo Berger, Jana Koubová

引用次数: 0

Measuring persistent global economic factors with output, commodity price, and commodity currency data 用产出、商品价格和商品货币数据衡量持续存在的全球经济因素

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-27 DOI: 10.1002/for.3139

Arabinda Basistha, Richard Startz

{"title":"Measuring persistent global economic factors with output, commodity price, and commodity currency data","authors":"Arabinda Basistha, Richard Startz","doi":"10.1002/for.3139","DOIUrl":"10.1002/for.3139","url":null,"abstract":"In this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non-persistent factor, both as a single global factor using all data and as factors for each category of data. The in-sample predictive performances of the three persistent factors together are better than the non-persistent factors and the single global factors. Out-of-sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub-categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1- to 6-month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12-month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate-based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2860-2885"},"PeriodicalIF":3.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Splitting long-term and short-term financial ratios for improved financial distress prediction: Evidence from Taiwanese public companies 拆分长期和短期财务比率，改进财务困境预测：来自台湾上市公司的证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-27 DOI: 10.1002/for.3143

Asyrofa Rahmi, Chia-chi Lu, Deron Liang, Ayu Nur Fadilah

{"title":"Splitting long-term and short-term financial ratios for improved financial distress prediction: Evidence from Taiwanese public companies","authors":"Asyrofa Rahmi, Chia-chi Lu, Deron Liang, Ayu Nur Fadilah","doi":"10.1002/for.3143","DOIUrl":"https://doi.org/10.1002/for.3143","url":null,"abstract":"Financial distress occurs when a company cannot meet its financial obligations within a specified timeframe, often owing to prolonged poor operational performance. While studies on financial distress prediction (FDP) use financial ratios (FRs) to forecast distress, they neglect to differentiate long-term (LT) attributes from FRs. To address this gap, our study introduces a novel model that distinguishes between LT and short-term (ST) accounting attributes in FRs. Using data from Taiwanese public companies (1991–2018), our proposed model employs a stacking ensemble classifier to split LT and ST Altman's ratios. This study addresses three key questions: (1) Do models involving split of LT and ST ratios outperform those that combine them? (2) How reliable and robust are these proposed models? (3) What is the proposed model's impact on distress prediction? The results show a significant outperformance of the existing solution, with higher accuracy, lower Type I and Type II errors, and reduced misclassification costs. These models are reliable in handling imbalanced data, proving suitable for real-market investigations. Diverse FR contexts from previous Taiwanese studies validate the distinction between LT and ST features, representing robust performance. This model identifies characteristics of correctly and incorrectly predicted distress in companies, providing nuanced insights into complex distress attributes. This study introduces a pioneering model demonstrating superior predictive accuracy, reliability, and robustness by considering the split between LT and ST accounting attributes. It lays a foundation for future studies to extend and refine the proposed model, offering valuable insights into the complex dynamics of FDP.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2886-2903"},"PeriodicalIF":3.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Forecasting the direction of the Fed's monetary policy decisions using random forest 利用随机森林预测美联储货币政策决策的方向

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-23 DOI: 10.1002/for.3144

Jungyeon Yoon, Juanjuan Fan

引用次数: 0

Regime-dependent commodity price dynamics: A predictive analysis 与时间相关的商品价格动态：预测分析

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-20 DOI: 10.1002/for.3152

Jesus Crespo Cuaresma, Ines Fortin, Jaroslava Hlouskova, Michael Obersteiner

{"title":"Regime-dependent commodity price dynamics: A predictive analysis","authors":"Jesus Crespo Cuaresma, Ines Fortin, Jaroslava Hlouskova, Michael Obersteiner","doi":"10.1002/for.3152","DOIUrl":"10.1002/for.3152","url":null,"abstract":"We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime-dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime-dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub-indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade-off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2822-2847"},"PeriodicalIF":3.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seeing is believing: Forecasting crude oil price trend from the perspective of images 眼见为实：从图像角度预测原油价格走势

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-19 DOI: 10.1002/for.3149

Xiaohang Ren, Wenting Jiang, Qiang Ji, Pengxiang Zhai

引用次数: 0

Portfolio management based on a reinforcement learning framework 基于强化学习框架的投资组合管理

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-19 DOI: 10.1002/for.3155

Wu Junfeng, Li Yaoming, Tan Wenqing, Chen Yun

引用次数: 0