Anthony Sanford is a postdoctoral fellow at the University of Maryland. He holds a Ph.D. and an M.A. in Economics from the University of Washington, an M.S. in Finance from Seattle University, and a B.Comm. in Finance and Economics from Concordia University. He currently works primarily on forecasting asset returns using options, portfolio construction using options, and determining firm responses to uncertainty and news shocks.
At the University of Washington, he has taught and assisted in teaching graduate and undergraduate courses in statistics (quantitative methods), microeconomics, computational finance, ethics, fixed income securities, American foreign policy, and political economy.
Dissertation Committee: Mu-Jeung Yang (co-chair), Eric Zivot (co-chair), Yu-Chin Chen, Thomas Gilbert, and Matthew Lorig
"Empirical finance people typically come from finance departments and econometricians typically come from economics departments, and each sees the other as relatively unsophisticated. Empirical finance people see the econometricians as tremendously unsophisticated people, because they don’t know how the markets work and how the data is constructed and what are the important questions. I think cross-fertilization is tremendously valuable."
- Robert F. Engle -
Working Paper = A paper that is finished but not necessarily polished. These papers are generally submitted to conferences or I have started circulating them for comments.
Work in Progress = A paper for which data has been collected, analysis conducted, and draft (usually) has been started.
Recovery Theorem with a Multivariate Markov Chain
(Under Review. Paper. Online Appendix. Code.)
In this paper, I redefine the prices derived in Ross’ Recovery Theorem (Ross, 2015) using a multivariate Markov chain rather than a univariate one. I employ a mixture transition distribution where the proposed states depend on the level of the S&P 500 index and its options’ implied volatilities. I include volatility because the transition path between states depends on the propensity of an underlying asset to vary. An asset that is highly volatile is more likely to transition to a far-away state. These higher transition probabilities should lead to higher state prices. The multivariate method improves upon the univariate RT because the latter does not include the volatility inherent in the state transition, which makes its derived prices less precise. The multivariate RT produces forecast results far superior to the univariate RT. Using quarterly forecasts for the 1996-2015 period, the out-of-sample R-square of the RT increases from around 12% to 30%. Moreover, using simulated data, I show that including the implied volatility in the multivariate Markov chain more closely captures the inherent risk in business cycles.
State Price Density Estimation with an Application to the Recovery Theorem
(Revise and Resubmit. Paper. Code.)
In this paper, I introduce a new model to estimate the risk-neutral density. Current estimation techniques use one mathematical model to interpolate option prices on two of an option’s dimensions: strike price and time-to-maturity (TTM). I argue that the methodology to interpolate option prices should be dependent on which dimension we are interpolating. This paper demonstrates that using two different models allows us to better extract market information. I propose to use B-splines with at-the-money knots for the strike price interpolation and a function that depends on the option expiration horizon for the TTM interpolation. This paper shows that the TTM dimension does not include enough observations to produce a reliable result from splines. The results of this “hybrid” interpolation technique are particularly striking when compared to the common Aït-Sahalia and Lo benchmark applied to the Recovery Theorem. The accuracy of the density estimation is critical because different risk neutral density estimation techniques reveal different market information and risk preferences.
Does Perception Matter in Asset Pricing? Modeling Volatility Jumps and Returns Using Twitter-Based Sentiment Indices
(Under Review. Paper. Onilne Appendix)
Do consumer and investor perceptions matter in asset pricing? I find that it is possible to forecast high-frequency stock returns and volatility jumps using consumer and investor sentiment indicators. Using tweets that I scraped from Twitter, I perform textual analysis to construct daily sentiment indices. While other scholars have relied on third-party companies like Stocktwits to complete these tasks, doing so reduces transparency and limits the potential for customization. The sentiment indices I constructed are numerical scores, not dichotomous variables, which allows me to control for sentiment strength (e.g., good vs. great) and not just positive/negative overall feelings. Results indicate that sentiment indices can not only be used to obtain out-of-sample forecasts of daily returns, but can also forecast volatility jumps. Using a simple Markov-switching framework, I find that, as overall sentiments shift from positive to negative (or vice versa), volatility jumps occur.
Information Content of Option Prices: Comparing Analyst Forecasts to Option-Based Forecasts
(Reject and Resubmit. Paper)
Finance researchers keep producing increasingly complex and computationally-intensive models of stock returns. Separately, professional analysts forecast stock returns daily for their clients. Are the sophisticated methods of researchers achieving better forecasts or are we better off relying on the expertise of analysts on the ground? Do the two sets of actors even capture the same information? In this paper, I hypothesize that analyst forecasts and forecasts constructed using option prices will be different because they draw on different information sets. Using hypothesis tests and quantile regressions, I find that option-based forecasts are statistically significantly different from analyst forecasts at every level of the forecast distribution. Then, using cross-sectional regressions, I show that this difference originates in the distinct information sets used to create the forecasts: option-based forecasts incorporate information about the probability of extreme events while analyst forecasts focus on information about firm and macroeconomic fundamentals.
Discrimination in the Stock Market: Board Gender and Stock Performance
with Joannie Tremblay-Boire
(Under Review. Paper)
In this paper, we use event studies to estimate the effects of changes to a public firm's board of trustees on stock returns. The goal is to determine whether the gender of an incoming board member is perceived differently by investors. Scholarly findings on gender and leadership have been mixed at best. Overall, the evidence seems to indicate that women and men in comparable leadership positions are much more alike than different. Yet, the number of women in leadership positions in the United States (and globally) is still disproportionately low-a phenomenon known as the "glass ceiling." Our study shows that women and men, at least in the United States, are still not created equal in the eyes of investors. Using BoardEx data on the composition of U.S. public firm boards for 1992-2017, we find that changes to a firm's board are consistently perceived as a negative information shock by investors, but the effect of incoming female board members is more than twice as negative as than of male counterparts.
Optimized Portfolio Using a Forward-Looking Expected Tail Loss
(Under Review. Paper)
In this paper, I construct an optimal portfolio by minimizing the expected tail loss (ETL) derived from the forward-looking natural distribution of the Recovery Theorem (RT). The RT is one of the first successful attempts at deriving an unparameterized natural distribution of future asset returns. This distribution can be used as the criterion function in an expected tail loss (ETL) portfolio optimization problem. I find that the portfolio constructed using the RT outperforms both the equally-weighted portfolio and a portfolio constructed using historical ETL. The portfolio constructed using the RT has the smallest historical tail loss, smallest maximum drawdown, smallest Sortino Ratio, and smallest Sharpe Ratio.
Corporate Executives on American Foundations’ Boards of Directors and Foundation Financial Performance
with Joannie Tremblay-Boire
(Draft available upon request)
We examine the relationship between the financial performance of American corporations and that of American foundations which have executives of these corporations as trustees. Various literatures, such as the board interlock and nonprofit literatures, suggest that we should see a positive association between the two organizations’ (company and foundation) financial performance. The board interlock literature in management has demonstrated that corporate executives often use boards of directors as mechanisms to import and export various ideas, practices, and innovations from one firm to another. Nonprofit empirical research shows that corporate executives, based on their managerial experience and financial knowledge, influence nonprofits’ financial performance positively. It may be the case that foundations seek out executives as trustees specifically to help them become more efficient financially. We hypothesize that when an executive of a high (low) performance company is on a foundation’s board of trustees, that foundation is also more likely to perform well (poorly) financially. We operationalize “financial performance” for companies as the volatility and return on their stocks. For foundations, we operationalize financial performance as return on investments, fiscal efficiency, and grantmaking performance. We conduct statistical analyses using financial returns of S&P 500 companies and tax filings of grantmaking foundations.
Work in Progress
Managing Known Unknowns: The Response of Firm Investment to Pure Uncertainty Shocks
with Mu-Jeung Yang
(Draft available soon)
What is the effect of uncertainty about the aggregate economy on investment, holding news shocks constant? Recent empirical studies have struggled to answer this question, as times of high economic uncertainty are typically also times of bad news. We propose a new methodology to measure and separate uncertainty and news shocks in stock return data. By using option prices to adjust abnormal returns for time-varying risk premia, we are able to estimate the impact of uncertainty shocks on firm investment while controlling for news shocks. Using quarterly data from 1996 to 2015 on public firms, we find that uncertainty shocks systematically depress investment, even after controlling for bad news. Moreover, lumpy investments reinforce the negative effect of uncertainty on investment, while better management systematically attenuates this negative effect.
Computational Finance And Financial Econometrics (Graduate and Undergraduate)
Introduction to Econometrics (Graduate)
Introduction to Statistical Methods (Undergraduate)
Introduction to Microeconomics (Undergraduate)
Ethics in the Finance Profession (Graduate)
Risk in Financial Institutions (Graduate)
Credit Risk Management (Graduate)
Computational Finance and Financial Econometrics (Graduate and Undergraduate)
Developing, Coding, and Evaluating Financial Trading Systems (Graduate)
Introduction to Political Economy (Undergraduate)