An LSTM and GRU based trading strategy adapted to the Moroccan market

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.). Predicting the closing price provides useful information and helps the investor make the right decision. 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 Moroccan stock market, based on two deep learning models: LSTM and GRU to predict the closing price in the short and medium term respectively. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four 3-year periods, we simulate transactions using these decision rules with different settings for each stock. The returns obtained will be used to estimate an expected return. We only hold stocks that outperform a benchmark index (expected return > threshold). The random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. 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 Moroccan all share Indice (MASI), 15.24% for the distributor sector indices, and 19.94% for the pharmaceutical industry indices. Noted that brokerage fees are estimated and subtracted for each transaction. which makes the performance found even more realistic.

Modeling and forecasting of stock market processes

“…This is especially true for investors in the stock market, since the amount of their profits or losses depends on the accuracy of forecasts of stock market development processes. In particular, the use of regression analysis and extrapolation methods is a classic approach to modeling and forecasting stock market development processes [3,4], [5]. To predict these processes, methods are used that allow identifying functional dependencies in these processes and making forecasts of their future development.…”

Journal Of Big Data: A Bibliometric Analysis

“…BUDIHARTO W has been ranked seventh his top work includes Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis [49], Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM) [50], GNSS-based navigation systems of autonomous drone for delivering items [51]. DOUZI K has been ranked eighth with 6 articles his top work includes GNSS-based navigation systems of autonomous drone for delivering items [52], An LSTM and GRU based trading strategy adapted to the Moroccan market [53] IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism [54], FURHT B has been ranked ninth also with 6m articles in total , his top work includes Deep Learning applications for COVID-19 [55], Text Data Augmentation for Deep Learning [56], Random forest implementation and optimization for Big Data analytics on LexisNexis's high performance computing cluster platform [57]', and VILLANUSTRE F has been ranked tenth with 6 articles which includes Deep learning applications and challenges in big data analytics [58], Random forest implementation and optimization for Big…”

Моделювання Системи Інтелектуального Прогнозування Торгів На Фондових Біржах

“…DEVELOPMENT The process of designing algorithms and system behaviour is defined at the stage of determining what algorithmic structure and function the programme code will perform [18][19]. This approach differs from the simple function of an algorithm, as the architecture provides an overall understanding of where specific algorithms are located in the system.…”

“…he active use of information technology in the activities of various enterprises, regardless of their form of ownership, is becoming a standard. The classic way of conducting trading operations between brokers on a trading floor is gradually losing its relevance in favour of electronic trading via the network [1][2][3], which is carried out by people or automated trading systems. Thus, any individual can invest in shares traded on a stock exchange using his or her own computer.…”