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Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting.

Kerolly Kedma Felix do Nascimento¹, Fábio Sandro Dos Santos¹, Jader Silva Jale¹

¹Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, Pernambuco Brazil.

Computational Economics

|February 23, 2022

Summary

This summary is machine-generated.

Cryptocurrency markets exhibit long-range memory, with Bitcoin, Ethereum, and Ripple showing seven memory steps and Litecoin nine. This analysis uses Markov chain models to predict future returns, aiding traders and policymakers.

Keywords:

Digital market Granularity Long-range memory Markov chains Rule support Time series forecasting

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Area of Science:

Quantitative Finance
Computational Economics
Time Series Analysis

Background:

Digital currencies, or cryptocurrencies, are increasingly popular, necessitating robust analytical models for predicting market returns.
Existing models may not fully capture the complex dynamics and memory effects inherent in cryptocurrency markets.

Purpose of the Study:

To develop and apply Markov chain models for predicting future returns in major cryptocurrency markets (Bitcoin, Ethereum, Litecoin, Ripple).
To analyze the influence of memory depth on the predictive accuracy of these models.
To establish decision rules for future return prediction based on observed state transition probabilities.

Main Methods:

Utilized categorical data analysis and Markov chain models of orders 1 to 10.
Employed accuracy metrics including Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation to select optimal models.
Investigated the dependence of memory on process dynamics to understand long-range dependencies.

Main Results:

Cryptocurrency price series demonstrate significant long-range memory.
Bitcoin, Ethereum, and Ripple exhibited seven steps of memory, while Litecoin showed nine steps.
Identified frequent state transitions to formulate decision rules for predicting future returns.

Conclusions:

The findings confirm the presence of long-range memory in cryptocurrency markets, challenging assumptions of pure randomness.
The developed Markov chain approach provides a novel method for scenario analysis and future return prediction.
Results offer valuable insights for traders, investors, and policymakers navigating the volatile cryptocurrency landscape.