TL;DR
Results show that the evolved saBiLSTM models by the proposed method achieve the lowest prediction error and highest fitting degree.
Accurate prediction of stock market prices plays a vital role in financial analysis and investment planning due to the highly dynamic and nonlinear nature of market behavior. This paper presents a comparative framework for stock price prediction using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) mo...
The findings indicate that the BiLSTM architecture has strong potential for financial time-series forecasting and can effectively capture important sequential patterns in stock market data.
Stock price prediction remains challenging due to market noise, non-stationarity, and complex temporal dependencies. This study proposes a robust hybrid framework to enhance predictive performance, stability, and interpretability in financial time series forecasting. The framework integrates BorutaSHAP-based feature se...
Accurate stock price prediction is vital for investors and analysts to make informed decisions and mitigate risks. Traditional methods like ARIMA struggle with the complex, non-linear patterns of stock market data, leading to less precise forecasts. This study addresses the gap by leveraging advanced deep learning meth...
View the results as a methodological contribution rather than direct evidence of practical investment value, given the modest trend-classification accuracy and the lack of trading back testing, transaction costs, or risk-adjusted performance measures.
The foreign exchange market is highly volatile and complex, making accurate price prediction challenging. This study aims to develop an optimized deep learning framework for predicting daily closing prices of seven major currency pairs (AUDUSD, EURUSD, GBPUSD, USDCAD, USDCHF, USDCNY, and USDJPY) by integrating Bidirect...
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