BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching

TL;DR

This paper introduces BIRP, a multimodal pattern matching model that ranks past Bitcoin chart patterns to enhance directional prediction, aiming to improve investment decision-making in volatile markets.

Contribution

The paper proposes a novel ranking-based approach for pattern matching that improves Bitcoin price movement prediction by leveraging multimodal features.

Findings

Ranking similar past patterns enhances prediction accuracy.

Multimodal features improve directional prediction performance.

Application to Bitcoin demonstrates effectiveness in volatile markets.

Abstract

Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements.