Articlegold2018

Anticipating Cryptocurrency Prices Using Machine Learning

Laura Alessandretti · Abeer ElBahrawy · Luca Maria Aiello · Andrea Baronchelli

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Blockchain Technology Applications and Security

Abstract

Machine learning and AI‐assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state‐of‐the‐art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short‐term evolution of the cryptocurrency market.

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References

1992Simple Technical Trading Rules and the Stochastic Properties of Stock Returns 2004Forecasting stock market movement direction with support vector machine 2015What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis 2013BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era 2018Predicting the Price of Bitcoin Using Machine Learning 2014The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy 2005The use of data mining and neural networks for forecasting stock market returns 2017An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information

Cited by

2019A Comparative Study of Bitcoin Price Prediction Using Deep Learning 2020Stochastic Neural Networks for Cryptocurrency Price Prediction 2021Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey 2020An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning

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