Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction
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| Format: | Preprint |
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2024
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| author | Finnegan, CJ McCann, James F. Moutari, Salissou |
| author_facet | Finnegan, CJ McCann, James F. Moutari, Salissou |
| contents | In this paper we introduce a multi-agent deep-learning method which trades in the Futures markets based on the US S&P 500 index. The method (referred to as Model A) is an innovation founded on existing well-established machine-learning models which sample market prices and associated derivatives in order to decide whether the investment should be long/short or closed (zero exposure), on a day-to-day decision. We compare the predictions with some conventional machine-learning methods namely, Long Short-Term Memory, Random Forest and Gradient-Boosted-Trees. Results are benchmarked against a passive model in which the Futures contracts are held (long) continuously with the same exposure (level of investment). Historical tests are based on daily daytime trading carried out over a period of 6 calendar years (2018-23). We find that Model A outperforms the passive investment in key performance metrics, placing it within the top quartile performance of US Large Cap active fund managers. Model A also outperforms the three machine-learning classification comparators over this period. We observe that Model A is extremely efficient (doing less and getting more) with an exposure to the market of only 41.95% compared to the 100% market exposure of the passive investment, and thus provides increased profitability with reduced risk. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_11740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction Finnegan, CJ McCann, James F. Moutari, Salissou Trading and Market Microstructure Portfolio Management I.2.1; I.6.4; I.6.5 In this paper we introduce a multi-agent deep-learning method which trades in the Futures markets based on the US S&P 500 index. The method (referred to as Model A) is an innovation founded on existing well-established machine-learning models which sample market prices and associated derivatives in order to decide whether the investment should be long/short or closed (zero exposure), on a day-to-day decision. We compare the predictions with some conventional machine-learning methods namely, Long Short-Term Memory, Random Forest and Gradient-Boosted-Trees. Results are benchmarked against a passive model in which the Futures contracts are held (long) continuously with the same exposure (level of investment). Historical tests are based on daily daytime trading carried out over a period of 6 calendar years (2018-23). We find that Model A outperforms the passive investment in key performance metrics, placing it within the top quartile performance of US Large Cap active fund managers. Model A also outperforms the three machine-learning classification comparators over this period. We observe that Model A is extremely efficient (doing less and getting more) with an exposure to the market of only 41.95% compared to the 100% market exposure of the passive investment, and thus provides increased profitability with reduced risk. |
| title | Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction |
| topic | Trading and Market Microstructure Portfolio Management I.2.1; I.6.4; I.6.5 |
| url | https://arxiv.org/abs/2408.11740 |