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Abstract
Over the past few years, with the advent of blockchain technology, there has been a massive increase in the usage of Cryptocurrencies. However, Cryptocurrencies are not seen as an investment opportunity due to the market's erratic behavior and high price volatility. Most of the solutions reported in the literature for price forecasting of Cryptocurrencies may not be applicable for real-time price prediction due to their deterministic nature. Motivated by the aforementioned issues, we propose a stochastic neural network model for Cryptocurrency price prediction. The proposed approach is based on the random walk theory, which is widely used in financial markets for modeling stock prices. The proposed model induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility. Moreover, a technique to learn the pattern of the reaction of the market is also included in the prediction model. We trained the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models for Bitcoin, Ethereum, and Litecoin. The results show that the proposed model is superior in comparison to the deterministic models.
Highlights
In a continuously evolving technological landscape, there has been a paradigm shift in the mode of transactions from physical payments like cash and cheques to digital transactions
Jay et al.: Stochastic Neural Networks for Cryptocurrency Price Prediction explored by various authors in the last to predict the value of equity and securities using machine learning and deep learning algorithms [14], [15]
Before we introduce stochasticity into the market scene, we present the types of stochastic processes and how we can relate them to the Cryptocurrency market
Summary
In a continuously evolving technological landscape, there has been a paradigm shift in the mode of transactions from physical payments like cash and cheques to digital transactions. P. Jay et al.: Stochastic Neural Networks for Cryptocurrency Price Prediction explored by various authors in the last to predict the value of equity and securities using machine learning and deep learning algorithms [14], [15]. Having vast amounts of openly available data on the Cryptocurrencies market and social trends information, machine learning algorithms can be used to forecast the prices with Cryptocurrencies [12]. These algorithms are a set of methods for learning mathematical models from data without explicitly programming the computer to do a specific task. We acquaint the reader with a diversified set of machine learning approaches [20] that have been used to predict price trends of various currencies
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