A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics.

Abstract

The spread of COVID-19 has had a devastating impact on the world economy, international trade relations, and globalization. As this pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory networks (LSTM). The goal of this study is to establish a model that can predict near future regarding the variable set of features. The nature of the features in each pandemic is completely different; therefore, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognizing and extracting the features which affect the pandemic is pivotal. In this study, we develop a framework that provides a better understanding of the features and feature selection process. Although the global impacts of COVID-19 are complicated, we are trying to show how additional features like COVID-19 cases can help to forecast in a real-world scenario, rather than relying solely on the history of tickers, which is used conventionally for prediction. This study is based on a preliminary analysis of features such as COVID-19 cases and other market tickers for enhancing forecasting models’ performance against fluctuations in the market. Our predictors are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, vector sequence output LSTM, Bidirectional LSTM, and encoder–decoder LSTM on the dataset. The results of the vector sequence output LSTM achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for 1 day, 2 days, and 30 days predictions in advance which are outperforming other proposed method in the literature.