An Empirical Study in Forecasting Bitcoin Price Using Bayesian Regularization Neural Network

Abstract

In recent years, Bitcoin has attracted a lot of attention because of its nature that supports encryption technology and monetary units. For traders, Bitcoin becomes a promising investment since its fluctuating prices potentially draw high profit (the higher the risk the higher the return). Unlike conventional stock, Bitcoin trades for 24 hours a day without a closing period, so that it escalates the risk. Predicting the value of Bitcoin is expected to minimize the risk by considering some information such as blockchain information, macroeconomic factors, and global currency ratios. However, the multicollinearity among these independent variables causes regression method cannot be used. This research employs Bayesian Regularization Neural Network (BRNN) which is a free assumption. This method is Single Hidden Layer Feed Forward Neural Network (SLNN) that utilize Bayesian concept to optimize weights, biases, and connection strengths. The data is time series data from January 23, 2017, to January 23, 2019. Regression with subset selection is employed to reduce independent variables, from a total of 25 variables to 14 variables. As a result, the predicted value is not much different from the actual data, with an accuracy of 91.1% based on the MAPE value.

Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting

“…Every Bitcoin transaction is carefully recorded in the blockchain, a distributed ledger that a central bank or government cannot control [7,8]. Because a central authority does not run Bitcoin, its price can grow without stopping or slowing down [9,10]. With few and clear controls on inflation, the currency's value is inherently unstable.…”

Forecasting Bitcoin Price Based on Blockchain Information Using Long-Short Term Method

“…Because bitcoin is valued in currency (USD, GBP, JYP, etc.) then the exchange rate of currency give an affect to ups and down of the bitcoin prices (Jang & Lee, 2018) (Sriwiji & Primandari, 2019).…”