1st Edition
by Jun Chen (Author), Edward P K Tsang (Author)
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.
