Stochastic Modelling of the Banking Sector of the Nigerian Stock Market

We investigate empirical finance issues: stylized facts, market efficiency, anomaly, bubble and volatility, characterizing stock prices of sixteen (16) Nigerian banks in the Nigerian stock exchange (NSE) from June 1999 to December 2014, encompassing periods of financial and banking reforms by the Central Bank of Nigeria (CBN) and the 2007-2009 global financial crisis witnessed by the Nigerian financial system. Both daily and monthly returns are examined and compared. Various financial and stochastic time series methods are applied to these series. These include a variety of initial plots, tests and models. The tests include: Jarque-Bera and a host of other normality tests; Ljung-Box (Q) test of autocorrelation; Augmented Dickey Fuller (ADF), Phillip-Peron, and KPSS tests; variance ratio test, BDS tests, runs test for Random Walk, unit root and market efficiency tests; Duration dependent test and appropriate GARCH families of models. The results are compared to the existing literature for other countries and also other studies in Nigeria but at the market index level. The results largely reveal that while in some cases about 90% of the banks behave uniformly with respect to some of the concepts, in most other cases their behaviour differs significantly depending on the concepts investigated. Also, it is found that while the results of this study agree in a few cases with some of the outcomes of the overall market level - for example, the banking industry is largely weak-form inefficient in most other circumstances, there are marked differences. Specifically, unlike at the overall market level, bubbles were identified in some of the banks and only two anomalies such as January-holiday and turn-of the-month were found with most of the banks. Therefore, a good understanding of how each bank reacted to different scenarios is identified. This should form a basis upon which good investment decisions could be made. This also provides a good understanding of which bank is performing well or at risk, so that appropriate decisions that would enhance the performance of the banking market are made by market regulators.