Academic research and publications in finance and economics
My research mainly focuses on empirical asset pricing, behavioral finance, big data, and the intersection of technology with financial markets, with published articles on empirical asset pricing, behavioral finance, and other areas. My articles have been published in academic journals such as the Journal of Empirical Finance, Journal of Information Systems, Finance Research Letters, Journal of Investing, and Journal of Asset Management.
We document a novel cross-country relationship between gender bias, as portrayed in a country’s folklore, and firms’ corporate cash holdings, while controlling for firm characteristics, country-specific macroeconomic attributes, governance factors, and cultural differences. We show that firms tend to hold significantly less cash in countries with a more pronounced Male Bias, defined as men being portrayed as more violent, physically active, and independent. We provide evidence that these differences can be attributed to countries with a larger Male Bias exhibiting less patience, consistent with the precautionary motive for holding cash. Our findings highlight the value of utilizing folklore data to comprehensively capture the complex and nuanced effects of cultural dimensions on economic and financial behaviors.
This study examines the degree to which Loughran and McDonald (LM) word lists are informative at the item level of SEC filings, such as risk factors (RF) and management’s discussion and analysis (MDA) disclosures in 10-X reports. In this context, we explore if sentiment types are informative when associated with other material events, namely cybersecurity breaches. Our results support the assertion that sentiment types, beyond positive and negative, are informative at the individual disclosure item level, as tested in the RF and MDA sections. We also find that investors respond to different types of sentiment between RF and MDA. We find an economically significant estimated average economic impact of $469 million/firm. We further contribute to the literature by applying novel statistical methods that advance empirical accounting literature.
Using option pricing methodology, we provide evidence the oil and banking industries’ default likelihood decreased following OPEC’s November 2016 oil production cut announcement. The effect is present within several oil sub-industries and for the banks conducting business in states with the most oil production. In addition, for the oil industry we find the decrease in default likelihood is more pronounced for firms with higher leverage, low financial slack, small market value, and small book-to-market ratios. For commercial banks, banks with higher non-performing assets and provision for loan losses experienced a greater decline in default likelihood. In addition, similar to the oil industry, size and book-to-market are significant determinants of the change in default likelihood.
Measurement of dispersion and variation have been studied and evaluated in many applications. Volatility in the field of finance is an important measure as it directly impacts allocation, risk management, and valuation. Pitman Closeness criterion is used to compare estimators of standard deviation from equity returns in a control charting application. Three estimators are evaluated over the 30 DJIA component stocks in an effort to determine if one method of estimation has better performance within an application of control charting for identifying outliers. The study uses three sample sizes to also determine if the better estimator is sample size dependent.
Market Volatility has been investigated at great lengths, but the measure of historical volatility, referred to as the relative volatility, is inconsistent. Using historical return data to calculate the volatility of a stock return provides a measure of the realized volatility. Realized volatility is often measured using some method of calculating a deviation from the mean of the returns for the stock price, the summation of squared returns, or the summation of absolute returns. We look to the stocks that make up the DJIA, using tick-by-tick data from June 2015 - May 2016. This research helps to address the question of what is the better measure of realized volatility? Several measures of volatility are used as proxies and are compared at four estimation time intervals. We review these measures to determine a closer/better fit estimator to the true realized volatility, using MSE, MAD, Diebold-Mariano test, and Pitman Closeness. We find that when using a standard deviation based on transaction level returns, shorter increments of time, while containing some levels of noise, are better estimates of volatility than longer increments.
This paper examines the impact of investor sentiment and geography on stock returns. We measure investor sentiment and location using direct measures derived from Twitter posts. We find Twitter sentiment is among important factors that can have an impact on stock returns. Negative tweets have a larger impact than positive tweets. The direct effect of sentiment on daily returns is an economically significant 0.036 and 0.078% for positive and negative sentiment, respectively. Our results support the Hirshleifer (J Finance 56(4):1533–1597, 2001) premise that both risk and misvaluation are important to asset pricing.
The findings of this study are similar to those of Chow, et al (2018) where the Parkinson Estimator outperforms the other range-based estimators when comparing the volatility of the weekly average of the daily returns. When looking at the variances of the weekly averages of the weekly returns the Parkinson Estimator is again superior when considering the MSE method of comparison. However, the Rogers & Satchell Estimator is slightly a better performer than the Parkinson Estimator when considering the comparisons of MAD and PC. As is often the case, the most appropriate method of comparison is often dependent on more than one aspect of your study. However, from these results the time interval is less impactful on the overall performance of the estimators than the method of overall comparison. Future research may look at shorter time horizons at monthly or weekly intervals, and could utilize high frequency data for establishing the calculated measure for variance.
Using 43,285 results for Alabama real estate license exams administered over 13 years, we explore the relationship between examinees' pass-fail rates and the sources of their pre-license education. The findings reveal professional school graduates fare better than their non-professional school counterparts. This suggests non-professional schools should work to either improve their approach to real estate exam pre-license education, or leave it entirely to those doing so for a living. Our findings provided the basis for the Alabama Real Estate Commission's adoption of multiple policy changes aimed at improving the quality of Alabama real estate pre-license education and the pass-fail rates of future Alabama real estate license examinees. We call on all U.S. licensing authorities to adopt transparent reporting standards for real estate license education, including, at a minimum, public disclosure of pass-fail rates for both license test examinees and pre-license education providers.
As any well-versed investor should know, there are many ways in which beta can be calculated based on factors such as the choice of time interval and market proxy used in the estimation process. Of course, this can lead to wide variation in beta estimates reported through publication sources. In this paper, we create portfolios based on the dispersion in the estimate of 27 different beta calculations. Defining stocks with higher variation in their beta estimates as higher risk, and consistent with risk-return theory, we find that portfolios of stocks with high dispersion across beta estimates outperform portfolios of stocks with low dispersion regardless of their level of systematic risk.
From an agency standpoint, we show that firms trade client focus for firm success. As a firm grows, more clients are distributed to fewer advisors compared to smaller firms, which have the potential to offer more individualized attention. Although past research has acknowledged the existence of agency problems in the investment advisor relationship, not one has looked at the underlying compensation arrangements of actual firms. Overall, we provide an in-depth analysis of the compensation agreements investment advisors provide and how these can be affected by different firm characteristics.
This study examines the relationship between REITs’ uses of the audit process to increase financial transparency, and their ability to attract and/or maintain reasonable access to capital investment. We find that capital investment is positively and significantly associated with auditor quality, specialization, and reputation. After controlling for the effects of the 2007-2008 financial crisis, we find that when REITs seek to attract new capital investment, using the audit process to increase financial transparency is just as important before the crisis as after it. These findings suggest that regardless of the economic conditions, auditor quality, specialization, and reputation add value.
We use the Pitman Closeness Criteria to compare four range-based volatility models. We utilize the daily high-low-open-close data for the S&P500, DJIA and NASDAQ indices, which are readily available, instead of a commonly used, yet more difficult to obtain, high frequency data to calculate the volatility of the daily returns. We then use the 30 equities of the DJIA to make similar comparisons of estimator performance. In both cases of the comparison of estimator performance for indices and equities, the better performing estimator is dependent on the method of measurement of variance, as well as the method of performance comparison.
We explore the readability of 30,000 registered investment advisor disclosures and find that these disclosures are written to a college reading level. This finding suggests it will be challenging for the average person to read a typical disclosure, which can lead to misunderstandings regarding conflicts of interest, fee structures, and the advisor’s background. Moreover, there may be agency problems, and uninformed investors may also be deterred from seeking financial advice. The readability of these disclosures has decreased over the past seven years. These results are consistent among four different readability proxies and contradict SEC requirements for ‘‘plain English’’ language in disclosures, specifically for firms providing financial planning services to individuals.
Using a unique sample of Twitter posts, also called tweets, we examine the impact of social media on the return, volume, and volatility of the stock market using word list and algorithmic content analysis. We show market returns may be predicted using confidence and sentiment levels. Volume is best predicted by confidence. Volatility is most related to sentiment. We examine one dimension of Twitter user characteristics, namely gender. Our results show that men are more confident and less optimistic than women when they communicate about stocks. We find differences in the ability of communications by men and women to predict market returns, volume, and volatility.
