Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Entire DC Network
Developing High Frequency Foreign Exchange Trading Systems, Bruce Vanstone, Tobias Hahn, Gavin Finnie
Developing High Frequency Foreign Exchange Trading Systems, Bruce Vanstone, Tobias Hahn, Gavin Finnie
Bruce Vanstone
The foreign exchange (FX) spot markets are well suited to high frequency trading. They are highly liquid, allow leverage, and trade 24 hours a day, 5 days a week. This paper documents and tests the stylized facts known about high-frequency FX markets. It then postulates a high frequency trading system on the basis of these stylized facts. Benchmarking confirms the robustness of the approach, demonstrating the role algorithmic trading has to play in higher frequency trading environments.
Stockmarket Trading Using Fundamental Variables And Neural Networks, Bruce Vanstone, Gavin Finnie, Tobias Hahn
Stockmarket Trading Using Fundamental Variables And Neural Networks, Bruce Vanstone, Gavin Finnie, Tobias Hahn
Bruce Vanstone
This paper uses a neural network methodology developed by Vanstone & Finnie[1] to develop a successful stockmarket trading system. The approach is based on these same 4 fundamental variables used within the Aby et al. fundamental trading strategies [2, 3], and demonstrates the important role neural networks have to play within complex and noisy environments, such as that provided by the stockmarket.
Enhancing Stockmarket Trading Performance With Anns, Bruce Vanstone, Gavin Finnie
Enhancing Stockmarket Trading Performance With Anns, Bruce Vanstone, Gavin Finnie
Bruce Vanstone
Artificial Neural Networks (ANNs) have been repeatedly and consistently applied to the domain of trading financial time series, with mixed results. Many researchers have developed their own techniques for both building and testing such ANNs, and this presents a difficulty when trying to learn lessons and compare results. In a previous paper, Vanstone and Finnie have outlined an empirical methodology for creating and testing ANNs for use within stockmarket trading systems. This paper demonstrates the use of their methodology, and creates and benchmarks a financially viable ANN-based trading system. Many researchers appear to fail at the final hurdles in their …
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Financial Time Series Forecasting With Machine Learning Techniques: A Survey, Bjoern Krollner, Bruce Vanstone, Gavin Finnie
Bruce Vanstone
Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in this area. We conclude with possible future research directions.
Designing Short Term Trading Systems With Artificial Neural Networks, Bruce Vanstone, Gavin Finnie, Tobias Hahn
Designing Short Term Trading Systems With Artificial Neural Networks, Bruce Vanstone, Gavin Finnie, Tobias Hahn
Bruce Vanstone
There is a long established history of applying Artificial Neural Networks (ANNs) to financial data sets. In this paper, the authors demonstrate the use of this methodology to develop a financially viable, short-term trading system. When developing short-term systems, the authors typically site the neural network within an already existing non-neural trading system. This paper briefly reviews an existing medium-term long-only trading system, and then works through the Vanstone and Finnie methodology to create a short-term focused ANN which will enhance this trading strategy. The initial trading strategy and the ANN enhanced trading strategy are comprehensively benchmarked both in-sample and …