Lifeng Gu is Assistant Professor in Finance at The University of Hong Kong and Dirk Hackbarth is Professor of Finance at Boston University Questrom School of Business. This post is based on a recent paper by Professor Gu and Professor Hackbarth.

Takeovers and especially models predicting takeovers have been of interest to academics and practitioners. Our paper titled Does Transparency Increase Takeover Vulnerability? studies how transparency affects takeover probability and stock returns over 25 years of takeover data.

Economic intuition suggests that if higher firm-level transparency lowers uncertainty with respect to synergies and valuations of potential target firms, then it should facilitate takeovers. We argue therefore that a better information environment increases takeover vulnerability, such that it has an incrementally important impact on estimates of takeover likelihood, which is consistent with recent research. To the best of our knowledge, however, this is the first paper to examine empirically whether transparency affects takeover vulnerability and potentially stock returns too.

In essence, our paper augments a baseline logit model, which contains known variables to proxy a firm’s takeover likelihood, with variables that measure a firm’s transparency level. For example, accrual quality is one proxy for transparency; a lower value of accrual quality indicates a more transparent information environment. To ensure our results are robust to different measures of transparency, we consider two alternative proxies: namely, forecast error and forecast dispersion that are based on analysts’ earnings forecasts. Our analysis largely proceeds in two steps.

As a first step, we establish that our augmented model with transparency variables improves the predictive power of a firm’s takeover likelihood relative to the baseline logit model without transparency variables in various ways. First, when a transparency proxy is added, the logit estimation coefficient on this variable is positive and highly statistically significant without posing a significant impact on other variables’ effects. Second, we find that the Pseudo-*R*^{2} of the regression increases about 20%, providing supportive evidence that the augmented model fits the takeover data better. Third, we then construct predicted takeover probabilities over the next year based on the logit estimation coefficients, and we compare the time-series of the average predicted takeover probability among firms in the top takeover probability decile with the time-series of the real takeover occurrence rate for the top decile. The curve for the predicted takeover probability matches the real takeover rate quite well, much more so than the curve for the predicted takeover probability that we form using the logit estimation results with the baseline model, because the correlation between the time-series of the predicted takeover likelihood and the real takeover rate are higher when we use our model.

As a second step, we study the relation between takeover probability and stock returns. We find that firms with higher takeover likelihood are generally associated with higher stock returns over the sample period of 1991 to 2016. According to the predicted takeover likelihood, we sort firms into quintile or decile portfolios. The long-short portfolio that buys firms in the top takeover probability quintile and sells firms in the bottom quintile earns a monthly equal-weighted abnormal return of 86 basis points after we adjust for common risk factors. This monthly abnormal return increases to 134 basis points for the decile sorted long-short portfolio. Interestingly, the long-short portfolio that we form using the logit estimation results from the augmented model generates higher average returns and abnormal returns than the long-short portfolio that we construct using the logit estimation results from our baseline model. For example, the mean return to the decile spread portfolio formed using the baseline model and our model is 118 basis points and 130 basis points, respectively. Although the difference is not remarkable, this pattern is true for all cases including the equal-weighted return, the value-weighted return, the decile sorted portfolio, and the quintile sorted portfolio. Our results confirm that takeover exposure is not idiosyncratic (i.e., carries a premium) and reveal that our model better captures a firm’s real takeover exposure.

To differentiate more the pricing ability of our augmented takeover factor from the baseline takeover factor, we construct our takeover factor as the monthly return spread between the top quintile takeover likelihood portfolio and the bottom quintile takeover likelihood portfolio. For comparison’s sake, we also construct the baseline takeover factor in similar fashion, but instead with portfolio sorts using the takeover probability constructed based on the baseline logit estimation results. Both takeover factors are able to bring the abnormal returns to the Fama-French 25 size and book-to-market sorted portfolios to a lower level after we account for only the market factor or all four common factors, which suggests a good pricing ability of both factors. Including our takeover factor further reduces abnormal returns of the 25 portfolios in terms of magnitude and statistical significance.

Finally, to assess further the takeover premium from the augmented model quantitatively with an additional test, we use 100 Fama-French size and book-to-market sorted portfolios to compute the takeover premium in two steps. We first calculate the portfolio beta on a specific factor as the loading on a particular factor in a multivariate regression of the excess return of each of the 100 portfolios on risk factors. We then calculate the premium associated with different factors as the coefficients from the multivariate regression of the mean excess return of each portfolio on all portfolio betas. Notably, the premium associated with the augmented takeover factor is higher than the premium associated with the baseline takeover factor when the Carhart four-factor model, the Fama-French three-factor model, or the capital asset pricing model (CAPM) are employed as benchmark models. This reinforces the result that our logit model better captures potential takeover vulnerability. In other words, our augmented takeover factor performs better than the baseline factor in terms of pricing the cross-section of stock returns. Moreover, the premium associated with the augmented takeover factor is higher than that for the baseline factor.

Overall, our results reveal transparency is crucial for external governance mechanisms, such as takeovers. Future researchers could consider its relation to internal governance mechanisms, such as activist investors or institutional ownership. A few questions that could emerge from such research are whether more transparent firms are more likely to have institutional shareholders (e.g., pension funds), and whether they are more likely to have activist campaigns.

The full paper is available for download here.