Detecting Regime Change in Computational Finance

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A01=Edward P K Tsang
A01=Jun Chen
Abnormal Regimes
Actual Regime Changes
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algorithmic trading
Alpha Engine
Author_Edward P K Tsang
Author_Jun Chen
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Category1=Non-Fiction
Category=KFF
Category=TJFM
Category=UMB
Category=UN
COP=United Kingdom
data science
Data Set
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DJIA Index
Emission Probabilities
eq_business-finance-law
eq_computing
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financial market monitoring
High Volatility Regime
HMM
Indicator Space
Language_English
machine learning
Market Regime
Maximum Drawdown
Normal Regime
Oil Crash
Os Event
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Price Movements
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Regime Changes
softlaunch
Test Data Sets
Time Series Approach
Time Series Indicator
Trading Algorithms
Trading Threshold
UK Referendum
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Product details

  • ISBN 9780367536282
  • Weight: 400g
  • Dimensions: 156 x 234mm
  • Publication Date: 15 Sep 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
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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.

About the Authors

Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002. He is a Visiting Professor at University of Hong Kong.