Tim Simin graduated Summa Cum Laude from the University of Texas at Dallas with a Bachelor of Science in Economics and Finance in 1992. Between 1992 and 1994 he worked in the Division of Monetary Affairs at the Federal Reserve Board of Governors in Washington, D.C.. He earned a Ph.D. in Finance from the University of Washington. Tim has taught finance and economics at the University of Washington, held a visiting position at the University of North Carolina at Chapel Hill, and is currently an Associate Professor in the Finance Department at the Smeal College of Business at the Pennsylvania State University. He is also the director of the PhD program in the department of finance.
Tim is married to Dr. Denine Simin, has a daughter Sydnie, two younger sons Alexander and Noah, and two step-sons Owen and Holden
Here are some of my current research projects
We combine classic corporate finance theory of capital structure with an asset pricing theory of leverage-constrained investors to explain why CAPM beta is negatively related to abnormal stock returns. Current theory explaining this anomaly suggests that leverage constrained investors tilt portfolios towards high-beta stocks. With a stylized analytical model and simulation, we show leverage-constrained investors rationally tilt investment, not towards high-beta firms generally but specifically towards those with high financial leverage. The advantage to adding levered firms, rather than an unlevered firms with comparably high betas, comes through lower covariance of the levered assets with the market portfolio. Informed by a continuous-time capital structure model, we estimate the varying impact of firm-level financial leverage on market risk measures and document two novel contributions. First, we find no remaining evidence of the anomalous low returns to high beta stocks. Second, we formally test an adjusted model of leverage-constrained investors and conclude that such constraints have practical implications for investors and for asset pricing models.
Abstract We analyze a multi-period partial equilibrium model with capital gains taxation. Relative to an economy without taxation, a capital gains tax tends to lower prices and raise expected returns, but it has little effect on volatility. Abstracting from tax redistribution policies, we find that a taxable investor's welfare falls, a nontaxable investor's welfare rises, and, depending on the tax rate, social welfare may either rise or fall under a capital gains tax. Furthermore, the taxable investor's tax-timing option may either increase or decrease tax revenue. Implications for empirical asset pricing are identifed.Download working paper
Abstract This paper is an empirical investigation of the predictive ability of the call-put implied volatility spread (CPIVS). In out-of-sample tests, the CPIVS outperforms well-known predictors of equity premium such as the dividend yield, stock return variance, term spread, and Cay. Specifically, we find that (1) the CPIVS attracts most investors’ attention according to willing-to-pay for predictive information based on utility gains; (2) the prediction is robust and consistent with common risk factors and different portfolios, and (3) the CPIVS can survive in various specifications and is unbiased in finite samples. Furthermore, we attribute the powerful prediction to the forward-looking information within the CPIVS, in the channel of discount rate and cash flow, as well as the moment risk in the market returns.Download Working Paper
There currently exists no accepted optical coherence tomography severity staging system in glaucoma management. It is important to determine which optical coherence tomography metrics, or which combination of metrics, may best be used to predict visual field severity. The purpose of this study is to evaluate different optical coherence tomography nerve fiber layer and ganglion cell complex parameters, specifically the focal loss volume percent and the global loss volume percent in different visual field groupings to determine which optical coherence tomography metrics are the most significant predictors of visual field defect severity. Applying a machine learning algorithm that combines variates and their interactions produces an area under the curve of 0.92. Focal loss volume and global loss volume and other optical coherence tomography parameters may be useful to incorporate into a multivariate model that also takes interactions into account to help clinicians determine the severity of their patient's glaucoma.
We investigate how information choices impact equity returns and risk. Building upon the theory of Van Nieuwerburgh and Veldkamp (2010), we estimate a learning index that reflects the expected benefits of learning about an asset. High learning index stocks have 6.2% lower returns per year and an order of magnitude lower abnormal volatilities compared to low learning index stocks. Long run patterns in returns and volatilities, other measures of information flow, and the information environment surrounding earnings announcements confirm our interpretation of the learning index. Our findings support the model’s predictions and illustrate a novel empirical measure of investor learning.