Articlegold2021

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

56 citations0/0 files storedJournal Of Big Data

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Financial Markets and Investment Strategies Financial Analysis and Corporate Governance

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 56 citations exist upstream.

References

1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*1970Efficient Capital Markets: A Review of Theory and Empirical Work 2020A CNN–LSTM model for gold price time-series forecasting 2018Financial time series forecasting model based on CEEMDAN and LSTM 2020Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong 2020Optimizing LSTM for time series prediction in Indian stock market 2020A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions 2012The Supraview of Return Predictive Signals

Cited by

2025Helformer: an attention-based deep learning model for cryptocurrency price forecasting 2023Machine learning techniques for stock price prediction and graphic signal recognition 2024A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting 2022Demand Forecasting Model using Deep Learning Methods for Supply Chain Management 4.0 2022An XGBoost-based multivariate deep learning framework for stock index futures price forecasting

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.