Incorporating causal notions to forecasting time series: a case study
Abstract Financial time series have been analyzed with a wide variety of models and approaches, some of which can forecast with great accuracy. However, most of these models, especially the machine learning ones, cannot show additional information for the decision maker or the financial analyst. The...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Financial Innovation |
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| Online Access: | https://doi.org/10.1186/s40854-024-00681-9 |
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| _version_ | 1841544387475013632 |
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| author | Werner Kristjanpoller Kevin Michell Cristian Llanos Marcel C. Minutolo |
| author_facet | Werner Kristjanpoller Kevin Michell Cristian Llanos Marcel C. Minutolo |
| author_sort | Werner Kristjanpoller |
| collection | DOAJ |
| description | Abstract Financial time series have been analyzed with a wide variety of models and approaches, some of which can forecast with great accuracy. However, most of these models, especially the machine learning ones, cannot show additional information for the decision maker or the financial analyst. The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts. In this study, we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series. Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk, as well as machine learning approaches without the proposed methodology. This improvement is statistically significant, as indicated by the Model Confidence Set test in the complete test set and quarterly analysis. |
| format | Article |
| id | doaj-art-c766f24abde04b73bfd662b726bbc0d7 |
| institution | Kabale University |
| issn | 2199-4730 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj-art-c766f24abde04b73bfd662b726bbc0d72025-01-12T12:36:19ZengSpringerOpenFinancial Innovation2199-47302025-01-0111112210.1186/s40854-024-00681-9Incorporating causal notions to forecasting time series: a case studyWerner Kristjanpoller0Kevin Michell1Cristian Llanos2Marcel C. Minutolo3Departamento de Industrias, Universidad Técnica Federico Santa MaríaDepartamento de Industrias, Universidad Técnica Federico Santa MaríaDepartamento de Industrias, Universidad Técnica Federico Santa MaríaSchool of Business, Robert Morris UniversityAbstract Financial time series have been analyzed with a wide variety of models and approaches, some of which can forecast with great accuracy. However, most of these models, especially the machine learning ones, cannot show additional information for the decision maker or the financial analyst. The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts. In this study, we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series. Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk, as well as machine learning approaches without the proposed methodology. This improvement is statistically significant, as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.https://doi.org/10.1186/s40854-024-00681-9EconometricsMachine learningForecastCausal notions |
| spellingShingle | Werner Kristjanpoller Kevin Michell Cristian Llanos Marcel C. Minutolo Incorporating causal notions to forecasting time series: a case study Financial Innovation Econometrics Machine learning Forecast Causal notions |
| title | Incorporating causal notions to forecasting time series: a case study |
| title_full | Incorporating causal notions to forecasting time series: a case study |
| title_fullStr | Incorporating causal notions to forecasting time series: a case study |
| title_full_unstemmed | Incorporating causal notions to forecasting time series: a case study |
| title_short | Incorporating causal notions to forecasting time series: a case study |
| title_sort | incorporating causal notions to forecasting time series a case study |
| topic | Econometrics Machine learning Forecast Causal notions |
| url | https://doi.org/10.1186/s40854-024-00681-9 |
| work_keys_str_mv | AT wernerkristjanpoller incorporatingcausalnotionstoforecastingtimeseriesacasestudy AT kevinmichell incorporatingcausalnotionstoforecastingtimeseriesacasestudy AT cristianllanos incorporatingcausalnotionstoforecastingtimeseriesacasestudy AT marcelcminutolo incorporatingcausalnotionstoforecastingtimeseriesacasestudy |