| Abstract: | Recent research has shown that thin trading can seriously bias beta estimates. Present techniques for controlling this bias in research designs involve the adjustment of OLS betas. This paper presents a new methodology that controls for this bias by forming portfolios where the level of thin trading is held constant, while the difference of another variable, pertinent to a specific research design, is maximized across the portfolios. Directly controlling the level of thin trading avoids reliance on beta adjustment techniques. Further, the linear programming model permits the control of the mean and higher moments of additional variables across portfolios. |
