- Author:
- Edward Gately
From the Publisher:
When applied to the world of finance, neural networks are automated trading systems, based on mapping inputs and outputs for forecasting probable future values. In Neural Networks for Financial Forecasting - the first book to focus on the role of neural networks specifically in price forecasting - traders are provided with a solid foundation that explains how neural nets work, what they can accomplish, and how to construct, use, and apply them for maximum profit. It is written by an acknowledged authority who is, himself, the developer of several successful networks. Neural Networks for Financial Forecasting enables you to develop a usable, state-of-the-art network from scratch all the way through completion of training. There are spreadsheets and graphs throughout to illustrate key points, and an appendix of valuable information, including neural network software suppliers and related publications.
Cited By
- de Campos Souza P and Torres L (2021). Extreme Wavelet Fast Learning Machine for Evaluation of the Default Profile on Financial Transactions, Computational Economics, 57:4, (1263-1285), Online publication date: 1-Apr-2021.
- Wu M, Syu J, Lin J and Ho J (2021). Portfolio management system in equity market neutral using reinforcement learning, Applied Intelligence, 51:11, (8119-8131), Online publication date: 1-Nov-2021.
- Hoog S (2019). Surrogate Modelling in (and of) Agent-Based Models, Computational Economics, 53:3, (1245-1263), Online publication date: 1-Mar-2019.
Faloutsos C, Gasthaus J, Januschowski T and Wang Y Classical and Contemporary Approaches to Big Time Series Forecasting Proceedings of the 2019 International Conference on Management of Data, (2042-2047)- Faloutsos C, Gasthaus J, Januschowski T and Wang Y (2018). Forecasting big time series, Proceedings of the VLDB Endowment, 11:12, (2102-2105), Online publication date: 1-Aug-2018.
- Harris T (2015). Credit scoring using the clustered support vector machine, Expert Systems with Applications: An International Journal, 42:2, (741-750), Online publication date: 1-Feb-2015.
- Zhang M, He C and Liatsis P (2019). A D-GMDH model for time series forecasting, Expert Systems with Applications: An International Journal, 39:5, (5711-5716), Online publication date: 1-Apr-2012.
- Cheng C, Su C, Chen T and Chiang H Forecasting stock market based on price trend and variation pattern Proceedings of the Second international conference on Intelligent information and database systems: Part I, (455-464)
- Vanstone B and Finnie G (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks, Expert Systems with Applications: An International Journal, 36:3, (6668-6680), Online publication date: 1-Apr-2009.
- Atsalakis G and Valavanis K (2009). Forecasting stock market short-term trends using a neuro-fuzzy based methodology, Expert Systems with Applications: An International Journal, 36:7, (10696-10707), Online publication date: 1-Sep-2009.
- Barnes M and Lee V Feature selection techniques, company wealth assessment and intra-sectoral firm behaviours Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications, (134-146)
- Yu L, Wang S and Lai K (2006). An Integrated Data Preparation Scheme for Neural Network Data Analysis, IEEE Transactions on Knowledge and Data Engineering, 18:2, (217-230), Online publication date: 1-Feb-2006.
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