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A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics
Mahtab Mohtasham Khani1, Sahand Vahidnia2, Alireza Abbasi2
1Istanbul Technical University, Istanbul, Turkey.
SN Computer Science
|June 21, 2021
Summary
This study uses long short-term memory networks (LSTM) to forecast stock market fluctuations, incorporating COVID-19 data. The vector sequence output LSTM model shows superior performance in predicting market changes, aiding economic stability during pandemics.
Area of Science:
- * Computational finance and econometrics.
- * Machine learning applications in financial forecasting.
- * Pandemic impact analysis on global markets.
Background:
- * The COVID-19 pandemic significantly disrupted global economies, trade, and business operations, necessitating new analytical approaches.
- * Traditional stock market prediction models often rely solely on historical market data, which may be insufficient during unprecedented global events.
- * Understanding and adapting to pandemic-induced market shifts is crucial for international economic stability and crisis management.
Purpose of the Study:
- * To develop and evaluate a novel model for stock market value estimation incorporating COVID-19 data.
- * To identify and extract key features influencing market fluctuations during a pandemic.
- * To enhance forecasting model performance by integrating real-world pandemic data alongside traditional market indicators.
Main Methods:
- * Utilized Long Short-Term Memory (LSTM) networks, including Vector Sequence Output LSTM, Convolutional Neural Networks (CNN) LSTM, Bidirectional LSTM, and encoder-decoder LSTM.
- * Developed a framework for feature selection, emphasizing the importance of pandemic-specific data (e.g., COVID-19 cases) alongside market tickers.
- * Applied the models to predict Gold price fluctuations using daily time-series data for COVID-19 cases and market values.
Main Results:
- * The Vector Sequence Output LSTM model demonstrated superior performance compared to other methods.
- * Achieved Mean Squared Error (MSE) of , , and on the validation set for 1-day, 2-day, and 30-day ahead predictions, respectively.
- * Integration of COVID-19 case data significantly improved forecasting accuracy in real-world scenarios.
Conclusions:
- * Pandemic-specific features are critical for accurate financial forecasting during global health crises.
- * LSTM-based models, particularly Vector Sequence Output LSTM, offer a robust framework for predicting market behavior amidst unprecedented events.
- * The developed approach provides a valuable tool for economic decision-making and managing market volatility during future pandemics.
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