Crypto Currency Price Analysis With Artificial Intelligence

Abstract

Crypto currency is playing an
increasingly important role in reshaping the financial
system due to its growing popular appeal and
mechant acceptance. While many people are making
investments in Cryptocurrency, the dynamical
features, uncertainty, the predictability of
Cryptocurrency are still mostly unknown, which
dramatically risk the investments. It is a matter to try
to understand the factors that infiuence the value
formation. In this study, we use advanced artificial
intelligence frameworks of fully connected Artificial
Neural Network (ANN) and Long Short-Term
Memory (LSTM) Recurrent Neural Network to
analyse the price dynamics of Bitcoin, Etherum, and
Ripple. We find that ANN tends to rely more on
long-term history while LSTM tends to rely more on
short-term dynamics, which indicate the efficiency of
LSTM to utilise useful information hidden in
historical memory is stronger than ANN. However,
given enough historical information ANN can
achieve a similar accuracy, compared with LSTM.
This study provides a unique demonstration that
Cryptocurrency market price is predictable. However,
the explanation of the predictability could vary
depending on the nature of the involved machinelearning
model.