The Effect of Data Types’ on the Performance of Machine Learning Algorithms for Financial Prediction

ArXiv ID: 2404.19324 “View on arXiv”

Authors: Unknown

Abstract

Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, allowing them to make more profits. The most important thing in this process is to produce accurate forecasts suitable for real-life applications. Bitcoin, frequently mentioned recently due to its volatility and chaotic behavior, has begun to pay great attention and has become an investment tool, especially during and after the COVID-19 pandemic. This study provided a comprehensive methodology, including constructing continuous and trend data using one and seven years periods of data as inputs and applying machine learning (ML) algorithms to forecast Bitcoin price movement. A binarization procedure was applied using continuous data to construct the trend data representing each input feature trend. Following the related literature, the input features are determined as technical indicators, google trends, and the number of tweets. Random forest (RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB), Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected features for prediction purposes. This work investigates two main research questions: i. How does the sample size affect the prediction performance of ML algorithms? ii. How does the data type affect the prediction performance of ML algorithms? Accuracy and area under the ROC curve (AUC) values were used to compare the model performance. A t-test was performed to test the statistical significance of the prediction results.

Keywords: Bitcoin Price Movement, Machine Learning Algorithms, Technical Indicators, Sentiment Analysis, Random Forest, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper employs standard machine learning algorithms and basic statistical tests (t-test) without advanced mathematical derivations, focusing on empirical evaluation of cryptocurrency prediction using real-world data sources like Google trends and tweets.

  flowchart TD
    A["Research Goal:<br>How do data types & sample sizes<br>affect ML performance in<br>Bitcoin price prediction?"] --> B["Data Preparation"]
    B --> C["Input Features:<br>Technical Indicators, Google Trends, Tweets"]
    C --> D1["Data Type 1: Continuous<br>(1 & 7 Year Periods)"]
    C --> D2["Data Type 2: Trend<br>(Binarized Continuous Data)"]
    D1 & D2 --> E["ML Algorithms:<br>RF, KNN, XGBoost, SVM, NB, ANN, LSTM"]
    E --> F["Evaluation:<br>Accuracy & AUC Scores<br>+ Statistical Significance (t-test)"]
    F --> G["Key Outcomes:<br>1. Sample size significantly impacts performance<br>2. Trend data often outperforms continuous data<br>3. Tree-based models (RF, XGBoost) show high robustness"]