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#5085. An LSTM and GRU based trading strategy adapted to the Moroccan market
| July 2026 | publication date |
| Proposal available till | 17-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Information Systems and Management; Information Systems; Computer Networks and Communications; Hardware and Architecture; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5085.1   | Contract №5085.2 | Contract №5085.3 | Contract №5085.4 |
1 place - free (for sale)
2 place - free (for sale)
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4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). The use of deep learning and more precisely of recurrent neural networks (RNNs) in stock market forecasting is an increasingly common practice in the literature. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are among the most widely used types of RNNs, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the stock market, based on two deep learning models: LSTM and GRU to predict the closing price in the short and medium term respectively. The repetition of this process with a variation of portfolio size makes it possible to select the best possible combination of stock each with the optimized parameter for the decision rules. The proposed strategy produces very promising results and outperforms the performance of indices used as benchmarks in the local market. Indeed, the annualized return of our strategy proposed during the test period is 27.13%, while it is 0.43% for all share Indice (MASI), 15.24% for the distributor sector indices, and 19.94% for the pharmaceutical industry indices.
Keywords:
Deep Learning; Financial times series; Stock market; Trading strategies
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). The use of deep learning and more precisely of recurrent neural networks (RNNs) in stock market forecasting is an increasingly common practice in the literature. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are among the most widely used types of RNNs, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the stock market, based on two deep learning models: LSTM and GRU to predict the closing price in the short and medium term respectively. The repetition of this process with a variation of portfolio size makes it possible to select the best possible combination of stock each with the optimized parameter for the decision rules. The proposed strategy produces very promising results and outperforms the performance of indices used as benchmarks in the local market. Indeed, the annualized return of our strategy proposed during the test period is 27.13%, while it is 0.43% for all share Indice (MASI), 15.24% for the distributor sector indices, and 19.94% for the pharmaceutical industry indices.
Keywords:
Deep Learning; Financial times series; Stock market; Trading strategies
Contacts :
