# Classification of Normal and Abnormal Regimes in Financial Markets
## 2 Regime Change Detection  
In this section, we will recapitulate our previous work on regime change recognition [5]. Given a series of transaction prices over a period of time, the task is to classify them into the two regimes which are based on market volatilities.  
The raw data of a market records every single transaction; this is called the tick data. Transactions take place in irregular time, which sometimes can inconvenience analysis. Most people prefer to sample data at fixed intervals: one data point is taken at every interval, e.g., minute by minute, hourly or daily. This is what forms the makeup of time-series that researchers are familiar with.  
Thus, given the way that data is sampled in time-series, changes between two sample points are not recorded. This means some significant price movements may not be recorded. By contrast, Directional Change (DC) is an alternative way to sample data [2]. While a time-series is time-driven, DC is data-driven: This is where a data point is sampled retrospectively when the price has changed in the opposite direction of the current trend and beyond a pre-defined threshold [7]. The market is thus partitioned into uptrands and downtrends, which correspond to bull and bear markets that traders are familiar with. Thus, through its sampling method, extreme points (the highest and lowest points before the direction changes) are always sampled in a DC series, so that no market fluctuation is missed.  
However, the majority of the studies on regime changes are based on time-series analysis. Various regime-switching models have been developed for modelling regime changes. The idea of these models was to track the behaviour of asset prices. For example, if the mean, volatility and correlation patterns in asset prices change dramatically, a regime change is concluded to have occurred [1]. Empirical studies demonstrate that these models are capable of capturing sudden changes of market behaviour [1]. However, due to the way that data is sampled in time-series, these regime-switching models run the risk of missing important transaction price changes between sample points. This motivated us to use DC series, in which all extreme points are sampled.  
In our pioneering work, we have demonstrated that regime changes can be recognised using DC indicators [5]. The Hidden Markov Model was employed to detect non-stationary markets--it attempted to recognise a hidden state, which tends to persist over time (which may be observed as volatility clustering under time-series). We compared the time-series approach with the DC approach in effective regime change detection. The two approaches were both used to examine data from the time period over the UK's Brexit referendum in June 2016. The results demonstrated that the financial market did experience regime change. The evidence showed that: firstly, the DC approach can be used to detect regime change just as effectively as the time-series approach.  
Secondly, the DC approach managed to pick up some signals of regime change that were missed under the time-series approach. Therefore, the DC approach offered a different but useful perspective to regime change detection. Encouraged by this research, this paper will take the research further, and go on to study what is the mechanism of regime change, and will attempt to classify what are the different types of market regimes revealed by the data.