The liquidity effects of revisions to the CAC40 stock index

Abstract

This article explores liquidity effects following CAC40 index revisions over the time period 1997 to 2001. We find evidence of a sustained increase (decrease) in the liquidity of the added (deleted) stocks. Furthermore, the improvement (reduction) in the liquidity of the stocks is due to a decrease (increase) in the direct cost of trading as opposed to a reduction (enhancement) in the asymmetric information cost of transacting. The empirical findings support the information cost, liquidity explanation. This is because investors demand a smaller (larger) risk premium for investing in stocks with more (less) available information.

Notes

¹ Atkins and Dyl (1997) report a similar relationship for the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotations (NASDAQ).

² We could also include the number of analysts as an additional explanatory variable in Equation 1. This variable has not been included in Equation 1 because analysts’ forecasts are only available on a monthly frequency, whereas all other explanatory variables are available on a daily basis. The small number of observations on the number of analysts’ forecasts may result in large jumps in the data causing inaccurate results for this variable.

³ We perform a Hausman (1978) test for the hypothesis that the explanatory variables are strictly exogenous. In our empirical estimates, the Hausman (1978) test rejects the null hypothesis at all conventional significance levels. This leads to the conclusion that we have to tackle the econometric issue of endogeneity for our explanatory variables. The result of the Hausman (1978) test is not reported by the authors but is available upon request.

⁴ The serial correlation test on the GMM is undertaken on the first difference of the residuals due to the transformations involved.

⁵ We re-estimated Equation 1 with the GMM panel estimator using 2, 3 and 4 lags of the endogenous variables as instruments. The results given in Table 2 do not change and are available upon request.

⁶ It can be argued that all the endogenous variables displayed in Equation 1 are jointly determined. If this was the case, we should have estimated the panel in the context of the GMM system estimator established in Blundell and Bond (1998). For robustness, we estimated Equation 1 using a GMM system. The results given in Table 2 remain quantitatively similar when we apply the system estimator. These results are not reported but are available upon request.

⁷ Two further robustness tests were conducted when we decomposed the bid–ask spread into relative direct costs and relative information asymmetric costs of trading. First, following Hegde and McDermott (2003), we also analysed the direct costs of transacting and information asymmetry in order to encapsulate any trends in stock prices subsequent to the index revisions. Second, we decomposed the bid–ask spread into relative direct costs of trading and relative asymmetric costs of trading with the use of the Huang and Stoll (1997) spread decomposition model. The results do not change and are available upon request. One possible limitation of this study is that there are various other spread decomposition models that were not considered such as the Glosten and Harris (1988), George et al. (1991) and the Lin et al. (1995) model. However, as pointed out by Van Ness et al. (2001), all spread decomposition models yield very similar results.