Stock Market Stationarity
Posted: 10 Sep 2015
Date Written: Sep 9, 2015
Stationarity tests are used to detect mean reversion in a certain dataset. Mean Reversion processes suggest a non-random behavior in a time series (Lo and MacKinley, 1988). Previous research has focused on studying mean reversion at stock price level (Debondt and Thaler, 1985; Lindemann et al., 2004) and considers stationary assumptions to be restrictive for a financial time series (Lo and MacKinley, 1988). The authors look at the concept of stationarity at group specific level as previously defined by the ‘Mean Reversion Framework’. The group approach allows for a different interpretation of stationarity, as it overcomes limitations of stationary tests on time series and the problems regarding trend and difference stationarity when it comes to finite data (Cochrane, 1987). The groups approach to look at stationarity also offers an easy way to prove the co-existence of non-random and random behavior in a group (stock market). The stationarity trends are defined as the percentage number of components that exhibit stationarity at the Value, Core or Growth bin levels in the ‘Framework’. The trends observed were consistent and showcase a duration dependency. More than 50% of all the components in the three bins exhibit stationarity, suggesting that the ‘Framework’ is a good proxy for complex and natural systems, which express both random and non-random behavior. The authors combine the absolute trends of bin transformation with the stationarity trends to draw parallels to understand how reversion and divergence co-exists in the ‘Framework’. Stationarity at a group level strengthens the case of markets as a complex system and ‘Framework’ as a good proxy to understand behavior of such systems.
Keywords: stationarity, stock market systems
JEL Classification: A00, A10
Suggested Citation: Suggested Citation