Online Non-linear Prediction of Financial Time Series Patterns

Online Non-linear Prediction of Financial Time Series Patterns

We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data...

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Bibliographic Details
Main Author: da Costa, Joel
Other Authors: Gebbie, Timothy
Format: Dissertation
Language:English
Published: Faculty of Science 2020
Subjects:
online learning
feedforward neural network
restricted Boltzmann machine
variance weight initialization
stacked autoencoder
pattern prediction
JSE
non-linear
financial time series
combinatorially symmetrical cross validation
backtest overfitting
deflated Sharpe ratio
probabilistic Sharpe ratio
Online Access:http://hdl.handle.net/11427/32221

Internet

http://hdl.handle.net/11427/32221

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