.Multivariate Time Series Bitcoin Price Forecasting based on Social and Economic Factors

Abstract

Bitcoin is a digital asset that forms the foundation for peer-to-peer financial transaction using blockchain technology. Nowadays, its trade is commonly perceived as one of the most attractive and favorable types of lucrative investments. Hovewer, its growing financial market impact is associated with significant volatility, highly competitive consumer market, and strong price fluctuations. Thus, creating a reliable and accurate forecasting model is deemed necessary for portfolio management and optimization. The goal of this research is to propose a multiple-phases hybrid classical time series and deep learning model framework utilizing a fixed set of socio-economic perspective factors to forecast daily bitcoin price. It started with Twitter (X-former) sentiment analysis to preprocess its raw data as Twitter has been found to influence bitcoin price. Afterwards, forecasting methodology was implemented using the combination of time series models (ARIMAX GARCHX) and deep learning models (variants of LSTM). Finally, Shapley additive explanations (SHAP) was proposed to examine the importance of various factors on bitcoin price based on the forecasting result of the best model. The result of this thesis will help investors making better decisions to pay attention on the significant factors to gain better profit and reduce loss in investment. Thus, it will contribute to the novel financial investment decision support literature.

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