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Inference and Prediction of Cryptocurrency Market Returns

Alarcon Valdez, Emilia Felicidad LU (2020) NEKN02 20201
Department of Economics
Abstract
The potential for making profits investing in cryptocurrencies, the hedging benefits and the role in global economy, make it relevant to study the determinants of cryptocurrencies and to analyze different returns prediction models. Previous studies have focused one or some cryptocurrencies, this study analyzes the cryptocurrency market as a whole and finds the determinants of the cryptocurrency market and a returns prediction model using a machine learning approach. Evaluating the immediate impact of features, divided in cryptocurrency market data, information demand, financial markets, exchange rates and macroeconomics, it was found that the most important determinants of the cryptocurrency market returns is the cryptocurrency market... (More)
The potential for making profits investing in cryptocurrencies, the hedging benefits and the role in global economy, make it relevant to study the determinants of cryptocurrencies and to analyze different returns prediction models. Previous studies have focused one or some cryptocurrencies, this study analyzes the cryptocurrency market as a whole and finds the determinants of the cryptocurrency market and a returns prediction model using a machine learning approach. Evaluating the immediate impact of features, divided in cryptocurrency market data, information demand, financial markets, exchange rates and macroeconomics, it was found that the most important determinants of the cryptocurrency market returns is the cryptocurrency market data. For prediction of the next-day returns, the USD-CNY exchange rate emerged as the most important determinant. Different returns prediction models are evaluated using Lasso, Regression Tress, Random Forest and Boosting. Random Forest presents the best prediction accuracy and can be used to predict the cryptocurrency market returns. (Less)
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author
Alarcon Valdez, Emilia Felicidad LU
supervisor
organization
course
NEKN02 20201
year
type
H1 - Master's Degree (One Year)
subject
keywords
Cryptocurrency Market, Machine Learning, Prediction, Inference, Determinants
language
English
id
9023391
date added to LUP
2020-08-29 11:11:54
date last changed
2020-08-29 11:11:54
@misc{9023391,
  abstract     = {The potential for making profits investing in cryptocurrencies, the hedging benefits and the role in global economy, make it relevant to study the determinants of cryptocurrencies and to analyze different returns prediction models. Previous studies have focused one or some cryptocurrencies, this study analyzes the cryptocurrency market as a whole and finds the determinants of the cryptocurrency market and a returns prediction model using a machine learning approach. Evaluating the immediate impact of features, divided in cryptocurrency market data, information demand, financial markets, exchange rates and macroeconomics, it was found that the most important determinants of the cryptocurrency market returns is the cryptocurrency market data. For prediction of the next-day returns, the USD-CNY exchange rate emerged as the most important determinant. Different returns prediction models are evaluated using Lasso, Regression Tress, Random Forest and Boosting. Random Forest presents the best prediction accuracy and can be used to predict the cryptocurrency market returns.},
  author       = {Alarcon Valdez, Emilia Felicidad},
  keyword      = {Cryptocurrency Market,Machine Learning,Prediction,Inference,Determinants},
  language     = {eng},
  note         = {Student Paper},
  title        = {Inference and Prediction of Cryptocurrency Market Returns},
  year         = {2020},
}