Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets

ArXiv ID: 2506.09851 “View on arXiv”

Authors: Md. Yeasin Rahat, Rajan Das Gupta, Nur Raisa Rahman, Sudipto Roy Pritom, Samiur Rahman Shakir, Md Imrul Hasan Showmick, Md. Jakir Hossen

Abstract

The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD/BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short-Term Memory (LSTM) neural network is employed, achieving an exceptional accuracy of 99.449%, a Root Mean Square Error (RMSE) of 0.9858, and a test loss of 0.8523, significantly outperforming traditional methods like ARIMA (RMSE 1.342). Additionally, a Gradient Boosting Classifier (GBC) is applied for directional prediction, with backtesting on a $10,000 initial capital revealing a 40.82% profitable trade rate, though resulting in a net loss of $20,653.25 over 49 trades. The study analyzes historical trends, showing a decline in BDT/USD rates from 0.012 to 0.009, and incorporates normalized daily returns to capture volatility. These findings highlight the potential of deep learning in forex forecasting, offering traders and policymakers robust tools to mitigate risks. Future work could integrate sentiment analysis and real-time economic indicators to further enhance model adaptability in volatile markets.

Keywords: Long Short-Term Memory (LSTM), Gradient Boosting, Foreign Exchange (Forex) Prediction, Time Series Analysis, Risk Management, Foreign Exchange

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (LSTM) and specific loss functions (MSE, exponential loss) with some LaTeX notation, indicating moderate mathematical density. It demonstrates high empirical rigor through specific backtesting results on real financial data ($10,000 capital, 49 trades, net loss calculation), use of technical indicators, and comparative performance metrics (RMSE, accuracy) against traditional models.

  flowchart TD
    A["Research Goal<br>Forecast USD/BDT Exchange Rates<br>using ML/AI"] --> B{"Data Preparation"}
    
    B --> C["Historical Data<br>2018-2023 (Yahoo Finance)<br>Normalized Daily Returns"]
    
    C --> D{"Model Development"}
    
    D --> E["LSTM Neural Network<br>Time Series Forecasting"]
    D --> F["Gradient Boosting Classifier<br>Directional Prediction"]
    
    E --> G{"Performance Evaluation"}
    F --> G
    
    G --> H["Key Findings & Outcomes"]
    
    H --> I["LSTM Results<br>Accuracy: 99.449%<br>RMSE: 0.9858<br>Outperforms ARIMA (1.342)"]
    H --> J["GBC Backtesting<br>$10,000 Initial Capital<br>40.82% Win Rate<br>Net Loss: -$20,653.25"]
    H --> K["Market Analysis<br>BDT/USD declined 0.012 → 0.009<br>Volatility captured via normalization"]