Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique

• URL: http://arxiv.org/abs/2507.01964v1
• Date: Tue, 27 May 2025 11:34:07 GMT
• Title: Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
• Authors: Adebola K. Ojo, Ifechukwude Jude Okafor,
• Abstract summary: The predictability of equity stock returns can boost investor confidence, but it remains a difficult task.<n>To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements.<n>The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.

Related papers

• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [55.328782443604986]
  Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data.<n>This paper offers the first quantitative analysis of confirmation bias in LLM-based investment analysis.<n>We observe a consistent preference for large-cap stocks and contrarian strategies across most models.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• Latent Thought Models with Variational Bayes Inference-Time Computation [52.63299874322121]
  Latent Thought Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.<n>LTMs demonstrate superior sample and parameter efficiency compared to autoregressive models and discrete diffusion models.
  arXiv  Detail & Related papers  (2025-02-03T17:50:34Z)
• Forecasting S&P 500 Using LSTM Models [0.0]
  This report compares ARIMA and LSTM models in predicting the S&P 500 index.<n>We evaluate these models using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)<n>The LSTM model, leveraging sequential processing capabilities, outperformed ARIMA with an MAE of 369.32, RMSE of 412.84, and 92.46 percent accuracy.
  arXiv  Detail & Related papers  (2025-01-29T01:31:56Z)
• CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction [28.15955243872829]
  We propose CryptoMamba, a novel Mamba-based State Space Model (SSM) architecture designed to capture long-range dependencies in financial time-series data.<n>Our experiments show that CryptoMamba not only provides more accurate predictions but also offers enhanced generalizability across different market conditions.<n>Our findings signal a huge advantage for SSMs in stock and cryptocurrency price forecasting tasks.
  arXiv  Detail & Related papers  (2025-01-02T02:16:56Z)
• Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals [0.0]
  A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies.
  arXiv  Detail & Related papers  (2024-09-29T11:20:20Z)
• An Evaluation of Deep Learning Models for Stock Market Trend Prediction [0.3277163122167433]
  This study investigates the efficacy of advanced deep learning models for short-term trend forecasting using daily and hourly closing prices from the S&P 500 index and the Brazilian ETF EWZ. We introduce the Extended Long Short-Term Memory for Time Series (xLSTM-TS) model, an xLSTM adaptation optimised for time series prediction. Among the models tested, xLSTM-TS consistently outperformed others. For example, it achieved a test accuracy of 72.82% and an F1 score of 73.16% on the EWZ daily dataset.
  arXiv  Detail & Related papers  (2024-08-22T13:58:55Z)
• Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning [0.0]
  The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions.
  arXiv  Detail & Related papers  (2024-05-28T17:55:54Z)
• ResNLS: An Improved Model for Stock Price Forecasting [1.0712226955584796]
  We introduce a hybrid model that improves the prediction of stock prices by emphasizing the dependencies between adjacent stock prices.<n>The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM.<n>Our experiment reveals that when the closing price data for the previous 5 consecutive trading days is used as input, the performance of the model (ResNLS-5) is optimal compared to those with other inputs.
  arXiv  Detail & Related papers  (2023-12-02T03:55:37Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Long Short-Term Memory Neural Network for Financial Time Series [0.0]
  We present an ensemble of independent and parallel long short-term memory neural networks for the prediction of stock price movement. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time.
  arXiv  Detail & Related papers  (2022-01-20T15:17:26Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)
• A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting [26.834110647177965]
  Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. Recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. We develop a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES.
  arXiv  Detail & Related papers  (2020-01-23T05:13:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.