Real Time Distributed Stock Market Forecasting using Feed-Forward Neural Networks, Market Orders, and Financial indicators
Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
Carlsson, Oscar
Rudnick, Kevin
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Machine learning and mathematical models are two tools used in prior research of
stock predictions. However, the stock market provides enormous data sets, making
machine learning an expensive and slow task, and a solution to this is to distribute
the computations. The input to the machine learning in this thesis uses market
orders, which is a different way to make short-term predictions than previous work.
Distributing machine learning in a modular configuration is also implemented in
this thesis, showing a new way to combine predictions from multiple models. The
models are tested with different parameters, with an input base consisting of a list of
the latest market orders for a stock. The distributed system is divided into so-called
node-boxes and tested based on latency. The distributed system works well and has
the potential to be used in large systems. Unfortunately, making predictions with
market orders in neural networks does not provide good enough performance to be
viable. Using a combination of predictions and financial indicators, however, shows
better results.
Beskrivning
Ämne/nyckelord
Machine learning , deep neural network , distributed systems , stock market prediction , market orders