Hongjun Zeng, Ran Wu, Mohammad Zoynul Abedin, Abdullahi D. Ahmed
{"title":"应用WTC-DCA-Informer框架预测澳大利亚股市波动","authors":"Hongjun Zeng, Ran Wu, Mohammad Zoynul Abedin, Abdullahi D. Ahmed","doi":"10.1002/for.3264","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This article proposed a novel hybrid framework, the WTC-DCA-Informer, for forecasting volatility in the Australian stock market. The findings indicated that (1) through a comprehensive comparison with various machine learning and deep learning models, the proposed WTC-DCA-Informer framework significantly outperformed traditional methods in terms of predictive performance. (2) Across different training set proportions, the WTC-DCA-Informer model demonstrated exceptional forecasting capabilities, achieving a coefficient of determination (<i>R</i><sup>2</sup>) as high as 0.9216 and a mean absolute percentage error (MAPE) as low as 13.6947%. (3) The model exhibited strong adaptability and robustness in responding to significant market fluctuations and structural changes before and after the outbreak of COVID-19. This study offers a new perspective and tool for forecasting financial market volatility, with substantial theoretical and practical implications for enhancing the efficiency and stability of financial markets.</p>\n </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 6","pages":"1851-1866"},"PeriodicalIF":2.7000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting Volatility of Australian Stock Market Applying WTC-DCA-Informer Framework\",\"authors\":\"Hongjun Zeng, Ran Wu, Mohammad Zoynul Abedin, Abdullahi D. Ahmed\",\"doi\":\"10.1002/for.3264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>This article proposed a novel hybrid framework, the WTC-DCA-Informer, for forecasting volatility in the Australian stock market. The findings indicated that (1) through a comprehensive comparison with various machine learning and deep learning models, the proposed WTC-DCA-Informer framework significantly outperformed traditional methods in terms of predictive performance. (2) Across different training set proportions, the WTC-DCA-Informer model demonstrated exceptional forecasting capabilities, achieving a coefficient of determination (<i>R</i><sup>2</sup>) as high as 0.9216 and a mean absolute percentage error (MAPE) as low as 13.6947%. (3) The model exhibited strong adaptability and robustness in responding to significant market fluctuations and structural changes before and after the outbreak of COVID-19. This study offers a new perspective and tool for forecasting financial market volatility, with substantial theoretical and practical implications for enhancing the efficiency and stability of financial markets.</p>\\n </div>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":\"44 6\",\"pages\":\"1851-1866\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3264\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3264","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Forecasting Volatility of Australian Stock Market Applying WTC-DCA-Informer Framework
This article proposed a novel hybrid framework, the WTC-DCA-Informer, for forecasting volatility in the Australian stock market. The findings indicated that (1) through a comprehensive comparison with various machine learning and deep learning models, the proposed WTC-DCA-Informer framework significantly outperformed traditional methods in terms of predictive performance. (2) Across different training set proportions, the WTC-DCA-Informer model demonstrated exceptional forecasting capabilities, achieving a coefficient of determination (R2) as high as 0.9216 and a mean absolute percentage error (MAPE) as low as 13.6947%. (3) The model exhibited strong adaptability and robustness in responding to significant market fluctuations and structural changes before and after the outbreak of COVID-19. This study offers a new perspective and tool for forecasting financial market volatility, with substantial theoretical and practical implications for enhancing the efficiency and stability of financial markets.
期刊介绍:
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.