Not logged in.

Quick Search - Contribution

Contribution Details

Type	Master's Thesis
Scope	Discipline-based scholarship
Title	Applications of Machine Learning in High-Frequency Financial Time Series Prediction
Organization Unit	Empirical Finance (Marc Paolella)
Authors	Rino Beeli
Supervisors	Marc Paolella Pawel Polak
Language	English
Institution	University of Zurich
Faculty	Faculty of Business, Economics and Informatics
Number of Pages	113
Date	2021
Abstract Text	Interest in the use of machine learning methods continues unabated, notably in empiri-cal ﬁnance and ﬁnancial time series prediction. The recent advent of powerful machine learning methods combined with the availability of vast amounts of computational re-sources form an attractive basis for researchers and practitioners. This thesis applies four methods for stock price movement prediction and algorithmic trading, with the fourth method belonging to the ﬁeld of deep learning. Concretely, a simple momen-tum model, an autoregressive AR(1) model combined with prediction smoothing, a linear 1 trend ﬁlter based model with adaptive hyperparameter optimization, and a Long Short-Term Memory neural network are employed on 1 min high-frequency price data. The objective is to attain high accuracies for price movement predictions and to ﬁnd proﬁtable trading strategies net of transaction costs. The LSTM-based pre-diction model utilizes 1 min price data and extracts features based on handcrafted basis functions ﬁtted to tick price data using the least-squares method, which are then further processed using a LSTM neural network for prediction. The model delivered the best performance with a prediction accuracy of 72% based on average prices for the considered stocks and period, and outperformed the other approaches net of trans-actions costs, although the performance was still negative overall. These ﬁndings are encouraging and support further research using modern machine learning methods for high-frequency ﬁnancial time series prediction and algorithmic trading applications. It is expected that a bet sizing mechanism on top of price movement predictions would signiﬁcantly improve strategy performance net of transaction costs.
PDF File	Download
Export	BibTeX