Predictive Power of Machine Learning in Cryptoassets
|Author:||Mgr. Miroslav Duda|
|Year:||2021 - summer|
|Leaders:|| prof. PhDr. Ladislav Krištoufek Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||The work attempts to forecast the sign of the price change for cryptoasset time
series through classification. The main purpose is to find evidence concerning
market efficiency of the cryptoasset markets, potential trading strategies, and
differences between the modelled assets. Supporting vector machines, random
forests, and multilayer perceptron models are used. An additional model aggregates the results of the previous three. Bitcoin, Ether, XRP, and Binance Coin
are the modelled cryptoassets. The input variables include transformed daily
closing prices up to five lags, trading volumes, volatility, and moving averages.
Random forest models perform the best, followed by supporting vector machines, and multilayer perceptrons. Aggregation does not produce improved
forecasting performance. The two older assets, Bitcoin and Ethereum, are
found to be less forecastable than the newer, Binance Coin and XRP. Differences between the assets exist as exhibited through forecastability. Higher
classification accuracies are not found to imply better trading performance.