Predicting stock price and spread movements from news
Vozian Katia; Rönnqvist Samuel; Wistbacka Pontus; Sagade Satchit
Predicting stock price and spread movements from news
Vozian Katia
Rönnqvist Samuel
Wistbacka Pontus
Sagade Satchit
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021093048397
https://urn.fi/URN:NBN:fi-fe2021093048397
Tiivistelmä
We explore several ways of using news articles and financial data to train neural network machine learning models to predict shock events in high-frequency market data, and aggregated shock episodes. We investigate the use of price movements in this context, and separately at a daily interval as well. We describe in detail how training sets are created from our data sources and how our machine learning models are trained. We find that pairing company-related news text with events or movements in financial time series proves less straight-forward than the literature would indicate. We discuss possible reasons for negative results, especially relating to the combination of minute-level news and millisecond-level market data.
Kokoelmat
- Rinnakkaistallenteet [19207]