Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecasting

TL;DR

This study analyzes the unpredictability of cryptocurrency prices by examining their complexity and forecasting accuracy, revealing that they resemble Brownian noise and are difficult to predict, with simple models often outperforming complex ones.

Contribution

It provides a comprehensive analysis combining complexity measures and forecasting models, highlighting the low predictability and inherent randomness of cryptocurrency time-series.

Findings

Cryptocurrency time-series resemble Brownian noise in complexity.

Simple models outperform complex machine learning models in forecasting.

Cryptocurrencies exhibit low predictability and high randomness.

Abstract

This paper offers a thorough examination of the univariate predictability in cryptocurrency time-series. By exploiting a combination of complexity measure and model predictions we explore the cryptocurrencies time-series forecasting task focusing on the exchange rate in USD of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP. On one hand, to assess the complexity and the randomness of these time-series, a comparative analysis has been performed using Brownian and colored noises as a benchmark. The results obtained from the Complexity-Entropy causality plane and power density spectrum analysis reveal that cryptocurrency time-series exhibit characteristics closely resembling those of Brownian noise when analyzed in a univariate context. On the other hand, the application of a wide range of statistical, machine and deep learning models for time-series forecasting demonstrates the low predictability of cryptocurrencies. Notably, our analysis reveals that simpler models such as Naive models consistently outperform the more complex machine and deep learning ones in terms of forecasting accuracy across different forecast horizons and time windows. The combined study of complexity and forecasting accuracies highlights the difficulty of predicting the cryptocurrency market. These findings provide valuable insights into the inherent characteristics of the cryptocurrency data and highlight the need to reassess the challenges associated with predicting cryptocurrency's price movements.