کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4960200 | 1445959 | 2017 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500](/preview/png/4960200.png)
چکیده انگلیسی
In recent years, machine learning research has gained momentum: new developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods can be applied to various fields, including finance. This paper implements and analyzes the effectiveness of deep neural networks (DNN), gradient-boosted-trees (GBT), random forests (RAF), and several ensembles of these methods in the context of statistical arbitrage. Each model is trained on lagged returns of all stocks in the S&P 500, after elimination of survivor bias. From 1992 to 2015, daily one-day-ahead trading signals are generated based on the probability forecast of a stock to outperform the general market. The highest k probabilities are converted into long and the lowest k probabilities into short positions, thus censoring the less certain middle part of the ranking. Empirical findings are promising. A simple, equal-weighted ensemble (ENS1) consisting of one deep neural network, one gradient-boosted tree, and one random forest produces out-of-sample returns exceeding 0.45 percent per day for k=10, prior to transaction costs. Irrespective of the fact that profits are declining in recent years, our findings pose a severe challenge to the semi-strong form of market efficiency.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 259, Issue 2, 1 June 2017, Pages 689-702
Journal: European Journal of Operational Research - Volume 259, Issue 2, 1 June 2017, Pages 689-702
نویسندگان
Christopher Krauss, Xuan Anh Do, Nicolas Huck,