Related Experiment Videos
Deep learning-based exchange rate prediction during the COVID-19 pandemic
Mohammad Zoynul Abedin1,2, Mahmudul Hasan Moon3, M Kabir Hassan4
1Department of Finance, Performance & Marketing, Teesside University International Business School, Teesside University, Middlesbrough, TS1 3BX Tees Valley UK.
Annals of Operations Research
|December 1, 2021
Summary
This study introduces a novel ensemble deep learning model for currency exchange rate prediction. The Bi-LSTM BR approach offers improved forecasting accuracy, especially during volatile periods like the COVID-19 pandemic.
Area of Science:
- Computational finance
- Machine learning applications in economics
- Deep learning for time series forecasting
Background:
- Foreign exchange markets are susceptible to significant volatility, particularly during global events like pandemics.
- Accurate exchange rate prediction is crucial for financial traders and risk management.
- Existing machine learning and deep learning models show limitations in forecasting during high-volatility periods.
Purpose of the Study:
- To propose and evaluate a novel ensemble deep learning model, Bi-LSTM BR, for predicting currency exchange rates.
- To compare the performance of the Bi-LSTM BR model against traditional and deep learning benchmarks.
- To analyze the model's effectiveness during pre-COVID-19 and COVID-19 periods across various currencies.
Main Methods:
- Developed an ensemble deep learning approach integrating Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks.
- Utilized the Bi-LSTM BR model to forecast exchange rates for 21 currencies against the USD.
- Compared prediction performance against regression tree, support vector regression, random forest regression, LSTM, and Bi-LSTM models.
Main Results:
- The proposed Bi-LSTM BR ensemble deep learning approach demonstrated superior performance in forecasting exchange rates compared to all benchmark models.
- Model performance varied significantly between non-COVID-19 and COVID-19 periods, highlighting the impact of market volatility.
- The study identified currencies most affected by pandemic-related volatility.
Conclusions:
- The Bi-LSTM BR model offers enhanced prediction accuracy for exchange rates, particularly in volatile market conditions.
- The findings underscore the importance of adaptive prediction models for navigating unpredictable foreign currency markets.
- This research provides valuable insights for foreign exchange traders to enhance profitability and mitigate currency risk during crises.
Related Concept Videos
01:03Prediction Intervals
2.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.4K
01:18Steps in Outbreak Investigation
253
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
253
02:07Improving Translational Accuracy
12.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.0K
01:11Residuals and Least-Squares Property
8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
01:07End Point Prediction: Gran Plot
689
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
689
02:24Predicting Reaction Outcomes
8.8K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.8K