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Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach

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Abstract

The purpose of the paper is to predict Bitcoin prices using various machine learning techniques. Due to its high volatility attribute, accurate price prediction is the need of the hour for sound investment decision-making. At the offset, this study categorizes Bitcoin price by daily and high-frequency price (5-min interval price). For its daily and 5-min interval price prediction, a set of high-dimensional features and fundamental trading features are employed, respectively. Thereafter, we find that statistical methods like Logistic Regression predict daily price with 64.84% accuracy while complex machine learning algorithms like XGBoost predict 5-min interval price with an accuracy level of 59.4%. This work on Bitcoin price prediction recognizes the significance of sample dimensions in machine learning algorithms.

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Authors and Affiliations

  1. Madras School of Economics, Chennai, Tamil Nadu, India

    Sumit Ranjan, Parthajit Kayal & Malvika Saraf

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  1. Sumit Ranjan
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  2. Parthajit Kayal
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  3. Malvika Saraf
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Correspondence to Parthajit Kayal.

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Appendices

Appendix 1: Machine Learning Models on Bitcoin Daily Data

See Tables 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and 21.

Model: Logistic Regression

Accuracy Score: 0.6484018264840182

Table 6 Confusion matrix on Bitcoin daily price using Logistic Regression
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Table 7 Classification report on Bitcoin daily price using Logistic Regression
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Model: Linear Discriminant Analysis

Accuracy Score: 0.5981735159817352

Table 8 Confusion matrix on Bitcoin daily price using linear Discriminant Analysis
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Table 9 Classification report on Bitcoin daily price using Linear Discriminant Analysis
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Model: Random Forest

Accuracy Score: 0.5114155251141552

Table 10 Confusion matrix on Bitcoin daily price using Random Forest
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Table 11 Classification report on Bitcoin daily price Using Random Forest
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Model: XGBoost

Accuracy Score: 0.4748858447488584

Table 12 Confusion matrix on Bitcoin daily price using XGBoost
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Model: Quadratic Discriminant Analysis

Accuracy Score: 0.4474885844748858

Table 13 Classification report on Bitcoin daily price using XGBoost
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Table 14 Confusion matrix on Bitcoin daily price using Quadratic Discriminant Analysis
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Table 15 Classification report on Bitcoin daily price using Quadratic Discriminant Analysis
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Model: K-Nearest Neighbors

Accuracy Score: 0.4703196347031963

Table 16 Confusion matrix on Bitcoin daily price using K-Nearest Neighbors
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Table 17 Classification report on Bitcoin daily price using K-Nearest Neighbors
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Model: Decision Tree

Accuracy Score: 0.5616438356164384

Table 18 Confusion matrix on Bitcoin daily price using Decision Tree
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Model: Support Vector Machine

Accuracy Score: 0.4611872146118721

Table 19 Classification report on Bitcoin daily price using Decision Tree
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Table 20 Confusion matrix on Bitcoin daily price using Support Vector Machine
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Table 21 Classification report on Bitcoin daily price using Support Vector Machine
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Appendix 2: Machine Learning Models On Bitcoin 5-Minutes Interval Data

See Tables 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, and 37.

Model: Logistic Regression

Accuracy Score: 0.5939269171384457

Table 22 Confusion matrix on Bitcoin 5-min interval price using Logistic Regression
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Table 23 Classification report on Bitcoin 5-min interval price using Logistic Regression
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Model: Linear Discriminant Analysis

Accuracy Score: 0.593798250128667

Table 24 Confusion matrix on Bitcoin 5-min interval price using Linear Discriminant Analysis
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Table 25 Classification report on Bitcoin 5-min interval price using Linear Discriminant Analysis
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Model: Random Forest

Accuracy Score: 0.5432321152856407

Table 26 Confusion matrix on Bitcoin 5-min interval price using Random Forest
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Table 27 Classification report on Bitcoin 5-min interval price using Random Forest
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Model: XGBoost

Accuracy Score: 0.5941842511580031

Table 28 Confusion matrix on Bitcoin 5-min interval price using XGBoost
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Table 29 Classification report on Bitcoin 5-min interval price using XGBoost
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Model: Quadratic Discriminant Analysis

Accuracy Score: 0.5110653628409676

Table 30 Confusion matrix on Bitcoin 5-min interval price using Quadratic Discriminant Analysis
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Table 31 Classification report on Bitcoin 5-min interval price using Quadratic Discriminant Analysis
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Model: K-Nearest Neighbors

Accuracy Score: 0.5400154400411734

Table 32 Confusion matrix on Bitcoin 5-min interval price using K-Nearest Neighbors
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Table 33 Classification report on Bitcoin 5-min interval price using K-Nearest Neighbors
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Model: Decision Tree

Accuracy Score: 0.5317807514153371

Table 34 Confusion matrix on Bitcoin 5-min interval price using Decision Tree
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Table 35 Classification report on Bitcoin 5-min interval price using Decision Tree
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Model: Support Vector Machine

Accuracy Score: 0.5654915079773546

Table 36 Confusion matrix on Bitcoin 5-min interval price using Support Vector Machine
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Table 37 Classification report on Bitcoin 5-min interval price using Support Vector Machine
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Ranjan, S., Kayal, P. & Saraf, M. Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach. Comput Econ 61, 1617–1636 (2023). https://doi.org/10.1007/s10614-022-10262-6

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  • DOI: https://doi.org/10.1007/s10614-022-10262-6

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