Detecting regime change in computational finance: data science, machine learning and algorithmic trading

By:

Chen, Jun

Contributor(s):

Tsang, Edward P K

Material type: Text

TextPublication details: CRC Press Boco Raton 2021Description: xxvi, 138 pISBN:

9780367540951

Subject(s):

DDC classification:

332.01511352 CHE

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.

List(s) this item appears in: Finance & Accounting | Non Fiction

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Finance & Accounting	332.01511352 CHE (Browse shelf(Opens below))	1	Available		004213

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Previous	332.0028 MOH Financial analytics	332.0151 DAV Quantitative finance: a simulation-based introduction using excel	332.0151 TRE Introductory course on financial mathematics	332.01511352 CHE Detecting regime change in computational finance:	332.015118 BEL Quantitative finance for dummies	332.015118 BEN Financial modeling	332.015118 JAC Advanced modelling in finance using excel and VBA	Next

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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