Tacking Regime Changes in the Markets

Abstract

In our previous work, we showed that regime changes in the market are retrospectively detectable using historic data in directional changes (DC). In this paper, we build on such results and show that DC indicators can be used for market tracking - using data up to the present - to understand what is going on in the market. In particular, we wanted to track the market to see whether the market is entering an abnormally volatile regime. The proposed approach used DC indicator values observed in the past to model the normal regime of a market (in which volatility is normal) or an abnormal regime (in which volatility is abnormally high). Given a particular value observed in the current market, we used a naive Bayes model to calculate independently two probabilities: one for the market being in the normal regime and one for it being in the abnormal regime. These two probabilities were combined to decide which regime the market was in, two decision rules were examined: a Simple Rule and a Stricter Rule. We used DJIA, FTSE 100 and S&P 500 data from 2007 to 2010 to build the Bayes model. The model was used to track the S&P 500 market from 2010 to 2012, which saw two spells of abnormal regimes, as identified by our previous work, with the benefit of hindsight. The tracking method presented in this paper, with either decision rule, managed to pick up both spells of regime changes accurately. The tracking signals could be useful to market participants. This study potentially lays the foundation of a practical financial early warning system.