Note(s): This table reports the results of multivariate DID regressions on the number of M&As for the pilot and the control firms over the periods before, during and after Regulation SHO's pilot program. We categorize all firms into two groups according to firm age. We use the number of years the firm has been on Compustat with a non-missing stock price to measure firm age. We use the age information in 2003, immediately before the start of Regulation SHO's pilot program, to group firms into two subsamples. We define a firm with an age above the median as a mature firm and a firm with an age below the median as a young firm. We estimate the multivariate DID panel regression as follows

Ln_(1 + Count_i,t) = α₀ + β₁Pilot_i×During_t + β₂Pilot_i×After_t + β₃Pilot_i + β₄During_t + β₅After_t + β₆X_i,t + ɛ_i,t

In this model, Ln_(1 + Count_i,t) is the natural logarithm of (1 + Count_i,t) while Count_i,t is the total number of value-destroying M&As, including either cross-industry M&As or those with a negative CAR, announced by firm i in a given year t. Pilot_i equals one if firm i belongs to the pilot group and zero otherwise. After_t is a dummy variable that equals one if the year is during the Post period (2007 to 2010). X_i,t is the set of controlling variables including return on assets (ROA), market-to-book ratio (MB_Ratio), leverage (Leverage), size (Ln_MV), sales (Ln_Sales), capital expenditures (CAPEX), R&D expenditures (RDX), and cash and short-term investment (Cash). If we add a firm fixed effect in the panel regression, we omit Pilot_i in that column to avoid multicollinearity. We cluster the standard errors at the firm level. The Chi-square test statistic (χ²) indicates whether the coefficient of Pilot × During differs significantly between the two subsamples. For simplicity, we do not report the coefficients of the control variables. ^***, ^**, and ^* denote significance at the 1%, 5% and 10% level, respectively