Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation

TL;DR

This paper introduces a novel linear law-based feature space transformation (LLT) that significantly improves the accuracy of intraday cryptocurrency price movement predictions using machine learning.

Contribution

The paper presents a new LLT method that enhances classification accuracy in cryptocurrency price movement prediction compared to traditional approaches.

Findings

LLT significantly increases prediction accuracy for Bitcoin, Ethereum, Binance Coin, and Ripple.

The method demonstrates the potential of LLT in financial time series forecasting.

Transforming data with LLT improves machine learning classification performance.

Abstract

The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.