Journal of Business & Financial Affairs

The copula theory is a fundamental instrument used in modeling multivariate distributions. It defines the joint distribution via the marginal distributions together with the dependence between variables. Copulas can also model dynamic structures. This paper offers a brief description of the copulas’ statistical procedures implemented on real market data. A direct application of the Gaussian copula to the assessment of a portfolio of loans belonging to one of the banks operating in Lebanon is illustrated in order to make the implementation of the copula simple and straightforward.

The purpose of this paper is two-fold. First, to determine whether there is value premium in our sample of Canadian non-interlisted and interlisted stocks for the period May 1, 1985-April 30, 2010. Second, to examine whether an additional screening to the first step of the value investing process can be employed to separate the good value stocks from the bad ones. For both non-interlisted and interlisted stocks, we document a consistently strong value premium over the sample period, which persists in both bull and bear markets, as well as in recessions and recoveries for noninterlisted stocks, but less so for interlisted stocks. We show that the value premium is not driven by a few outliers, but it is pervasive. Interlisted stocks have a higher value premium than non-interlisted stocks. The other difference between interlisted and non-interlisted firms is with regards to stock liquidity, debt to equity and market cap metrics, as well as to the fact that a typical size effect does not exist for interlisted stocks. We are able to construct a composite score indicator (SCORE), combining various fundamental and market metrics, which enables us to predict future stock returns and separate the winners from the losers among value stocks. Results are stronger for interlisted than noninterlisted stocks. It is not clear, however, whether the SCORE indicator performance is linked to risk as evidence is inconclusive.