Next Article in Journal
Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
Next Article in Special Issue
The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach
Previous Article in Journal
A Fast Approach to Texture-Less Object Detection Based on Orientation Compressing Map and Discriminative Regional Weight
Previous Article in Special Issue
An Algorithmic Look at Financial Volatility
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(12), 202; https://doi.org/10.3390/a11120202

Classification of Normal and Abnormal Regimes in Financial Markets

Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, Colchester CO4 3SQ, UK
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 14 September 2018 / Revised: 23 November 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
(This article belongs to the Special Issue Algorithms in Computational Finance)
Full-Text   |   PDF [833 KB, uploaded 12 December 2018]   |  

Abstract

When financial market conditions change, traders adopt different strategies. The traders’ collective behaviour may cause significant changes in the statistical properties of price movements. When this happens, the market is said to have gone through “regime changes”. The purpose of this paper is to characterise what is a “normal market regime” as well as what is an “abnormal market regime”, under observations in Directional Changes (DC). Our study starts with historical data from 10 financial markets. For each market, we focus on a period of time in which significant events could have triggered regime changes. The observations of regime changes in these markets are then positioned in a designed two-dimensional indicator space based on DC. Our results suggest that the normal regimes from different markets share similar statistical characteristics. In other words, with our observations, it is possible to distinguish normal regimes from abnormal regimes. This is significant, because, for the first time, we can tell whether a market is in a normal regime by observing the DC indicators in the market. This opens the door for future work to be able to dynamically monitor the market for regime change. View Full-Text
Keywords: regime change; directional change; machine learning regime change; directional change; machine learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, J.; Tsang, E.P.K. Classification of Normal and Abnormal Regimes in Financial Markets. Algorithms 2018, 11, 202.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top