arXiv 31 Mar 2020 · Finance — Statistical Finance · 1 citations (OpenAlex)
arXiv:2004.01504 · PDF · DOI · OpenAlex · Extracted main text
This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, Luyang, Pelger, Markus, Zhu, Jason (2019) Deep Learning in Asset Pricing | 0.843 | 3 | 3 | 100% |
| 2 | Duan, Tony, Avati, Anand, Ding, Daisy Yi, Basu, Sanjay, Ng, Andrew Y… (2019) NGBoost: Natural Gradient Boosting for Probabilistic Prediction | 0.737 | 3 | 2 | 100% |
| 3 | Bergstra, J.S., Yamins, D., Cox, D.D (2013) Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms | 0.693 | 5 | 1 | 100% |
| 4 | Lundberg, Scott, Lee, Su-In (2017) A Unified Approach to Interpreting Model Predictions | 0.644 | 2 | 2 | 100% |
| 5 | Henrique, Bruno, Sobreiro, Vinicius, Kimura, Herbert (2019) Literature Review: Machine Learning Techniques Applied to Financial Market Prediction | 0.644 | 2 | 2 | 100% |
| 6 | Kelly, Bryan, Pruitt, Seth, Su, Yinan (2017) Some Characteristics Are Risk Exposures, and the Rest Are Irrelevant | 0.644 | 2 | 2 | 100% |
| 7 | Krollner, Bjoern, Vanstone, Bruce, Finnie, Gavin (2010) Financial Time Series Forecasting with Machine Learning Techniques: A Survey | 0.644 | 2 | 2 | 100% |
| 8 | Sheridan, Iain (2017) MiFID II in the context of Financial Technology and Regulatory Technology | 0.585 | 3 | 1 | 100% |
| 9 | (2015) Keras | 0.585 | 3 | 1 | 100% |
| 10 | Heaton, J.B., Polson, Nick (2016) Deep Learning for Finance: Deep Portfolios | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 126 scored citations.