engleski

Exploring the Impact of Alternative Data on Bitcoin Price Volatility Prediction

This thesis explores and defines a new system of preprocessing data for price series prediction tasks. The system is called an event-based system and functions by sampling events from the market and aggregating data according to predefined changes in the market. The impact of many features is explored with a focus on features that are extracted from Twitter. Multiple aggregation functions are tested, and their performance is evaluated. Finally, several preprocessing methods for preprocessing time series are introduced and explained. This thesis indicates that event-based systems have potential and should be explored in more detail. It is also shown that the type of event aggregation can impact model prediction accuracy. Finally, it is shown that features from Twitter are not good indicators of price change in a high-frequency setting.

Bitcoin, machine learning, volatility prediction, feature importance, time series preprocessing

nije evidentirano