Detecting regime change in computational finance: data science, machine learning and algorithmic trading
Material type:
TextPublication details: Boco Raton CRC Press 2021 Description: xxvi, 138 pISBN: 9780367540951Subject(s): Stocks--Prices--Mathematical models | Hidden Markov models | Expectation-maximization algorithms | Finance--Mathematical modelsDDC classification: 332.01511352 Summary: Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:
Data science: as an alternative to time series, price movements in a market can be summarised as directional changes
Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model
Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change
Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed
Algorithmic trading: regime tracking information can help us to design trading algorithms
It will be of great interest to researchers in computational finance, machine learning and data science.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Book
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Indian Institute of Management LRC General Stacks | Finance & Accounting | 332.01511352 CHE (Browse shelf(Opens below)) | 1 | Available | 004213 |
Table of Contents
1. Introduction. 2. Background and Literature Survey. 3. Regime Change Detection using Directional Change Indicators. 4. Classification of Normal and Abnormal Regimes in Financial Markets. 5. Tracking Regime Changes using Directional Change Indicators. 6. Algorithmic Trading based on Regime Change Tracking. 7. Conclusion. Appendix A. A Formal Definition of Directional Change. Appendix B. Extended Results of Chapter. 3 Appendix C. Experiment Summary of Chapter. 4 Appendix D. Detected Regime Changes in Chapter.
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:
Data science: as an alternative to time series, price movements in a market can be summarised as directional changes
Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model
Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change
Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed
Algorithmic trading: regime tracking information can help us to design trading algorithms
It will be of great interest to researchers in computational finance, machine learning and data science.
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