Detail práce

Predictive Power of Machine Learning in Cryptoassets

Autor: Mgr. Miroslav Duda
Rok: 2021 - letní
Vedoucí: prof. PhDr. Ladislav Krištoufek Ph.D.
Konzultant:
Typ práce: Diplomová
Finance, finanční trhy a bankovnictví
Jazyk: Anglicky
Stránky: 74
Ocenění:
Odkaz:
Abstrakt: 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.

02

Prosinec

Prosinec 2021
poútstčtsone
  12345
6789101112
13141516171819
20212223242526
2728293031  

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance