Chen Liu, Minh-Ngoc Tran, Chao Wang, Richard Gerlach, Robert Kohn
arXiv 5 Sep 2023 · Econometrics
arXiv:2309.02072 · PDF · DOI · OpenAlex · Extracted main text
Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the conventional practice of estimating models locally in data-scarce environments may underlie the mixed empirical performance observed in prior work. By focusing on volatility forecasting, we employ a dataset comprising over 10,000 global stocks and implement a global estimation strategy that pools information across cross-sections. Our econometric analysis reveals that forecasting accuracy improves markedly as the training dataset becomes larger and more heterogeneous. Notably, even with as little as 12 months of data, globally trained networks deliver robust predictions for individual stocks and portfolios that are not even in the training dataset. Furthermore, our interpretation of the model dynamics shows that these networks not only capture key stylized facts of volatility but also exhibit resilience to outliers and rapid adaptation to market regime changes. These findings underscore the importance of leveraging extensive and diverse datasets in financial forecasting and advocate for a shift from traditional local training approaches to integrated global estimation methods.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kaplan, Jared, McCandlish, Sam, Henighan, Tom, Brown, Tom B., Chess,… (2020) Scaling Laws for Neural Language Models | 0.843 | 4 | 3 | 75% |
| 2 | Zhai, Xiaohua, Kolesnikov, Alexander, Houlsby, Neil, Beyer, Lucas (2022) Scaling Vision Transformers | 0.843 | 4 | 3 | 75% |
| 3 | Makridakis, Spyros, Spiliotis, Evangelos, Assimakopoulos, Vassilios (2018) Statistical and Machine Learning forecasting methods: Concerns and ways forward | 0.737 | 3 | 2 | 100% |
| 4 | Montero-Manso, Pablo, Hyndman, Rob J (2021) Principles and algorithms for forecasting groups of time series: Locality and globality | 0.585 | 3 | 1 | 100% |
| 5 | Chung, Junyoung, Gulcehre, Caglar, Cho, KyungHyun, Bengio, Yoshua (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling | 0.511 | 2 | 2 | 50% |
| 6 | Bollerslev, Tim (1986) Generalized autoregressive conditional heteroskedasticity | 0.511 | 2 | 1 | 100% |
| 7 | Fissler, Tobias, Ziegel, Johanna F (2016) Higher order elicitability and Osband’s principle | 0.511 | 2 | 1 | 100% |
| 8 | Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron (2016) Deep learning | 0.511 | 2 | 1 | 100% |
| 9 | Hansen, Peter R., Lunde, Asger, Nason, James M (2011) The Model Confidence Set | 0.511 | 2 | 1 | 100% |
| 10 | LeCun, Yann, Bengio, Yoshua, Hinton, Geoffrey (2015) Deep learning | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 42 scored citations.