Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models

• URL: http://arxiv.org/abs/2410.19241v1
• Date: Fri, 25 Oct 2024 01:29:54 GMT
• Title: Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
• Authors: Shuchen Meng, Andi Chen, Chihang Wang, Mengyao Zheng, Fangyu Wu, Xupeng Chen, Haowei Ni, Panfeng Li,
• Abstract summary: Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate.
• Score: 1.5474412217744966
• License:
• Abstract: Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate. Utilizing 40 features across 6 categories, the analysis identifies TSMixer as the most effective model for this task. A rigorous feature selection process emphasizes the inclusion of key economic indicators, such as China-U.S. trade volumes and exchange rates of other major currencies like the euro-RMB and yen-dollar pairs. The integration of grad-CAM visualization techniques further enhances model interpretability, allowing for clearer identification of the most influential features and bolstering the credibility of the predictions. These findings underscore the pivotal role of fundamental economic data in exchange rate forecasting and highlight the substantial potential of machine learning models to deliver more accurate and reliable predictions, thereby serving as a valuable tool for financial analysis and decision-making.

Related papers

• Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods [45.30624270004584]
  Energy price forecasting plays an important role in supporting decision-making at various levels. Given the evolving landscape of forecasting techniques, the literature lacks a thorough empirical comparison. This paper provides an in-depth review of the evolution of forecasting modeling frameworks.
  arXiv  Detail & Related papers  (2024-11-05T11:45:00Z)
• EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods [0.0]
  This paper proposes a novel framework, IUS, that integrates unstructured textual data from news and analysis with structured data on exchange rates and financial indicators. An Optuna-optimized Bi-LSTM model is then used to forecast the EUR/USD exchange rate. Experiments demonstrate that the proposed method outperforms benchmark models, reducing MAE by 10.69% and RMSE by 9.56% compared to the best performing baseline.
  arXiv  Detail & Related papers  (2024-08-23T16:46:36Z)
• Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis [46.26988706979189]
  Cross-border commodity pricing determines competitiveness and market share of businesses. Time series data is of great significance in commodity pricing and can reveal market dynamics and trends. We propose a new method based on the hybrid neural network model CNN-BiGRU-SSA.
  arXiv  Detail & Related papers  (2024-08-22T03:59:52Z)
• Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach [6.112119533910774]
  This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.
  arXiv  Detail & Related papers  (2024-08-13T04:53:31Z)
• Machine learning and economic forecasting: the role of international trade networks [0.0]
  This study examines the effects of de-globalization trends on international trade networks and their role in improving forecasts for economic growth. Using section-level trade data from nearly 200 countries from 2010 to 2022, we identify significant shifts in the network topology driven by rising trade policy uncertainty. We find that network descriptors evaluated from section-specific trade networks substantially enhance the quality of a country's GDP growth forecast.
  arXiv  Detail & Related papers  (2024-04-11T21:04:56Z)
• 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)
• Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning [1.1602089225841632]
  We develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables.
  arXiv  Detail & Related papers  (2023-03-23T04:40:23Z)
• 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)
• A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data [20.392440676633573]
  This paper presents a framework that predicts price movement of cryptocurrencies from trade-by-trade data. The model is trained to achieve high performance on nearly a year of trade-by-trade data. In a realistic trading simulation setting, the prediction made by the model could be easily monetized.
  arXiv  Detail & Related papers  (2020-10-11T10:42:02Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

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.