Woongsun Yoo
Assistant Professor of Finance
Central Michigan University
Woongsun Yoo
Assistant Professor of Finance
Central Michigan University
Embedded Files
Assistant Professor of Finance
Central Michigan University
Research
Research
Publications
Publications
Black, Bernard S., Hemang Desai, Katherine Litvak, Woongsun Yoo, Jeff Jiewei Yu, 2024. "The SEC's Short-Sale Experiment: Evidence on Causal Channels and Reassessment of Indirect Effects." Management Science 70 (8), 5131-5156.
Black, Bernard S., Hemang Desai, Katherine Litvak, Woongsun Yoo, Jeff Jiewei Yu, 2024. "The SEC's Short-Sale Experiment: Evidence on Causal Channels and Reassessment of Indirect Effects." Management Science 70 (8), 5131-5156.
During 2005-2007, the SEC conducted a randomized trial in which it removed short-sale restrictions from one-third of the Russell 3000 firms. Early studies found modest market microstructure effects of removing the restrictions but no effect on short interest, stock returns, volatility or price efficiency. More recently, many studies have attributed a wide range of indirect outcomes to this experiment, mostly without assessing the causal channels for those outcomes. We examine the three most often cited causal channels for these indirect effects: short interest, returns and managerial fear. We find no evidence to support these channels. We then reexamine the principal findings in four recent studies using a sample that closely matches the actual experiment and a common research design and find minimal support for the reported indirect effects. Our findings highlight the importance of confirming a causal channel or an economic mechanism and show that sample selection and specification choices can produce statistical significance even without an underlying economic mechanism.
During 2005-2007, the SEC conducted a randomized trial in which it removed short-sale restrictions from one-third of the Russell 3000 firms. Early studies found modest market microstructure effects of removing the restrictions but no effect on short interest, stock returns, volatility or price efficiency. More recently, many studies have attributed a wide range of indirect outcomes to this experiment, mostly without assessing the causal channels for those outcomes. We examine the three most often cited causal channels for these indirect effects: short interest, returns and managerial fear. We find no evidence to support these channels. We then reexamine the principal findings in four recent studies using a sample that closely matches the actual experiment and a common research design and find minimal support for the reported indirect effects. Our findings highlight the importance of confirming a causal channel or an economic mechanism and show that sample selection and specification choices can produce statistical significance even without an underlying economic mechanism.
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Fišar, M., Greiner, B., Huber, C., Katok, E., Ozkes, A.I., and the Management Science Reproducibility Collaboration, 2024. "Reproducibility in Management Science." Management Science 70 (3), 1343-1356.
Fišar, M., Greiner, B., Huber, C., Katok, E., Ozkes, A.I., and the Management Science Reproducibility Collaboration, 2024. "Reproducibility in Management Science." Management Science 70 (3), 1343-1356.
Note: Member of the Management Science Reproducibility Collaboration
Note: Member of the Management Science Reproducibility Collaboration
With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.
With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.
Felton, James, Daniel McLaughlin, and Woongsun Yoo, 2024. "The Impact of COVID-19 on Commodity and S&P 500 Sector Return Volatility." Journal of Applied Business and Economics 26 (3), 218-228.
Felton, James, Daniel McLaughlin, and Woongsun Yoo, 2024. "The Impact of COVID-19 on Commodity and S&P 500 Sector Return Volatility." Journal of Applied Business and Economics 26 (3), 218-228.
We investigate the impact of COVID-19 on commodity return volatility. We find that the impact of COVID-19 on return volatility is different across different markets. Unlike S&P 500 sector indices, commodity return volatility is less sensitive to the impact of COVID-19. The impact of vaccination programs on return volatility is weak for both commodity and financial markets. We employ Fama-French 3 Factor Model and APARCH (1,1) for return volatility estimation. The variation in COVID-19’s impact across different markets has an important implication for return volatility hedging.
We investigate the impact of COVID-19 on commodity return volatility. We find that the impact of COVID-19 on return volatility is different across different markets. Unlike S&P 500 sector indices, commodity return volatility is less sensitive to the impact of COVID-19. The impact of vaccination programs on return volatility is weak for both commodity and financial markets. We employ Fama-French 3 Factor Model and APARCH (1,1) for return volatility estimation. The variation in COVID-19’s impact across different markets has an important implication for return volatility hedging.
Felton, James and Woongsun Yoo, 2024. "Pandemic GPAs." Journal of Higher Education Theory and Practice 24 (9), 27-34.
Felton, James and Woongsun Yoo, 2024. "Pandemic GPAs." Journal of Higher Education Theory and Practice 24 (9), 27-34.
A student’s grade point average (GPA) is very important to most scholarship committees, potential employers, and graduate school selection committees. Some students place such importance on grades that they actively manipulate their GPA higher by taking actions that raise grades without additional study and learning. Students can seek out easy professors, transfer grades for difficult topics from community colleges, try to gain favoritism with their professors, or cheat on assignments. During the Covid-19 pandemic, universities offered grading leniency, such as allowing students to switch grades to credit / no credit after the semester’s end, which artificially inflated grades. We apply Felton and Koper’s (2005) proposal for having two grades on a transcript, Nominal GPA and Real GPA, as an easy to understand and apply method for dealing with some of the methods of GPA manipulation.
A student’s grade point average (GPA) is very important to most scholarship committees, potential employers, and graduate school selection committees. Some students place such importance on grades that they actively manipulate their GPA higher by taking actions that raise grades without additional study and learning. Students can seek out easy professors, transfer grades for difficult topics from community colleges, try to gain favoritism with their professors, or cheat on assignments. During the Covid-19 pandemic, universities offered grading leniency, such as allowing students to switch grades to credit / no credit after the semester’s end, which artificially inflated grades. We apply Felton and Koper’s (2005) proposal for having two grades on a transcript, Nominal GPA and Real GPA, as an easy to understand and apply method for dealing with some of the methods of GPA manipulation.
Liu, Sheen, Chunchi Wu, Chung-Ying Yeh, and Woongsun Yoo, 2019. "What Drives Systemic State Credit Risk? Evidence from the State Credit Default Swap (CDS) Market." Journal of Fixed Income 28 (4), 5-45.
Liu, Sheen, Chunchi Wu, Chung-Ying Yeh, and Woongsun Yoo, 2019. "What Drives Systemic State Credit Risk? Evidence from the State Credit Default Swap (CDS) Market." Journal of Fixed Income 28 (4), 5-45.
The root cause of systemic risk is an issue of on-going debate. We contribute to this debate by documenting evidence strongly supporting the hypothesis that common shocks of macroeconomic fundamentals are key driver of US state systemic credit risk. We find that macroeconomic variables have higher explanatory power for the temporal variation in state credit spreads and its systemic component than do financial market variables. Empirical evidence points to the macroeconomic linkages, not the financial linkages, as the dominating source of systemic credit risk, and suggests that the state financial crisis is largely due to the weakness in economic fundamentals.
The root cause of systemic risk is an issue of on-going debate. We contribute to this debate by documenting evidence strongly supporting the hypothesis that common shocks of macroeconomic fundamentals are key driver of US state systemic credit risk. We find that macroeconomic variables have higher explanatory power for the temporal variation in state credit spreads and its systemic component than do financial market variables. Empirical evidence points to the macroeconomic linkages, not the financial linkages, as the dominating source of systemic credit risk, and suggests that the state financial crisis is largely due to the weakness in economic fundamentals.
Working Papers
Working Papers
Crowding Out Banks: Credit Substitution by Peer-to-Peer Lending (with Jess Cornaggia and Brian Wolfe)
Crowding Out Banks: Credit Substitution by Peer-to-Peer Lending (with Jess Cornaggia and Brian Wolfe)
Through superior technology, financial technology (FinTech) firms may expand credit markets. Alternatively, consumers may substitute one credit provider for another, generating adverse selection problems for incumbent lenders. We analyze the unsecured consumer loan market and identify the influence of FinTech lending on commercial banks using a novel approach that takes advantage of regulatory restrictions for FinTech borrowers and investors. We show that high-risk FinTech loans substitute for bank loans while low-risk loans may be credit expansionary. However, the influence on banks is heterogeneous. Our results highlight the changing landscape of financial intermediation and the regulatory challenges faced by FinTech firms.
Through superior technology, financial technology (FinTech) firms may expand credit markets. Alternatively, consumers may substitute one credit provider for another, generating adverse selection problems for incumbent lenders. We analyze the unsecured consumer loan market and identify the influence of FinTech lending on commercial banks using a novel approach that takes advantage of regulatory restrictions for FinTech borrowers and investors. We show that high-risk FinTech loans substitute for bank loans while low-risk loans may be credit expansionary. However, the influence on banks is heterogeneous. Our results highlight the changing landscape of financial intermediation and the regulatory challenges faced by FinTech firms.
Do FinTech Lenders Strategically Allocate Loans Among Investors? Quid Pro Quo and Regulatory Scrutiny in Marketplace Lending (with Li-Ting Chiu and Brian Wolfe)
Do FinTech Lenders Strategically Allocate Loans Among Investors? Quid Pro Quo and Regulatory Scrutiny in Marketplace Lending (with Li-Ting Chiu and Brian Wolfe)
FinTech debt platforms select which investors will have the opportunity to fund loans. We provide evidence that platforms strategically allocate loans among investors over time. Our results suggest that platforms balance the cost of potential regulatory intervention against the benefit of future volume commitments from large investors. Favoring retail investors with better loans may help to minimize the cost of potential regulatory intervention. However, we observe that when the value of additional future loan commitments may outweigh these costs, platforms shift preferential allocation to favor institutional investors. Interestingly though, marketplace lending platforms do not appear to distinguish among institutional investors. Our results demonstrate FinTech platforms’ strategic behavior to maximize origination volume and the lasting effects of early regulatory intervention in emerging capital market technologies.
FinTech debt platforms select which investors will have the opportunity to fund loans. We provide evidence that platforms strategically allocate loans among investors over time. Our results suggest that platforms balance the cost of potential regulatory intervention against the benefit of future volume commitments from large investors. Favoring retail investors with better loans may help to minimize the cost of potential regulatory intervention. However, we observe that when the value of additional future loan commitments may outweigh these costs, platforms shift preferential allocation to favor institutional investors. Interestingly though, marketplace lending platforms do not appear to distinguish among institutional investors. Our results demonstrate FinTech platforms’ strategic behavior to maximize origination volume and the lasting effects of early regulatory intervention in emerging capital market technologies.
The Role of Regulation and Technology in Segmenting Primary Markets and Security Flipping (with Shyam Venkatesan, Brian Wolfe, and Jun Yang)
The Role of Regulation and Technology in Segmenting Primary Markets and Security Flipping (with Shyam Venkatesan, Brian Wolfe, and Jun Yang)
Flipping is when traders purchase an asset in the initial offering of a security and immediately sell the asset for a higher price in a secondary market. Flipping is the natural result when segmentation exists in the primary market (initial offering), and investors have heterogeneous price beliefs. We provide empirical evidence for two novel types of segmentation caused by regulation and technology in the primary market for FinTech debt securities. Our results show both forms of segmentation increase flipping activity. Additional tests suggest platforms include an interest premium, potentially encouraging flipping and circumventing the investor regulatory restrictions that cause segmentation. Welfare benefits accrue to traders engaging in flipping activity. We estimate that excluded secondary market investors concede an average 288 BP in yield to investors flipping notes.
Flipping is when traders purchase an asset in the initial offering of a security and immediately sell the asset for a higher price in a secondary market. Flipping is the natural result when segmentation exists in the primary market (initial offering), and investors have heterogeneous price beliefs. We provide empirical evidence for two novel types of segmentation caused by regulation and technology in the primary market for FinTech debt securities. Our results show both forms of segmentation increase flipping activity. Additional tests suggest platforms include an interest premium, potentially encouraging flipping and circumventing the investor regulatory restrictions that cause segmentation. Welfare benefits accrue to traders engaging in flipping activity. We estimate that excluded secondary market investors concede an average 288 BP in yield to investors flipping notes.
The Road to False Positives: Sample Selection and Specification Choice in Randomized and Natural Experiments (with Bernard Black, Hemang Desai, Kate Litvak, and Jeff Jiewei Yu)
The Road to False Positives: Sample Selection and Specification Choice in Randomized and Natural Experiments (with Bernard Black, Hemang Desai, Kate Litvak, and Jeff Jiewei Yu)
During 2005-2007, the SEC conducted a randomized trial in which it removed shortsale restrictions from one-third of the Russell 3000 firms. Early studies found no effect of removing the restrictions on short interest, share returns, volatility, or price efficiency. In prior work, we confirm the lack of evidence for a natural causal channel that could explain these results (Black et al., 2024). Yet over 80 studies report evidence for a wide range of indirect effects on firms from the experiment. Given the lack of a causal channel, many of these results are likely to be false positives. We confirm that suspicion by closely reexamining the principal results from 10 of these studies using a simple specification with firm and year fixed effects. None of the results survive. We then examine best-match specifications that closely follow the sample selection, methodology and specification reported in the respective papers. Again, we mostly obtain null results. The gaps between reported and best match results reflects reported coefficients being much larger in magnitude, reported standard errors being much smaller than best-match, or both. The large gaps between best-match and reported results implies that the study authors made choices that are not evident from the explanation of their research design, which strongly affected the reported results. Our results suggest that researchers retain extensive discretion over the sample and model specification, even (as here) for a true randomized experiment. The choices in these studies produced statistically significant results when other reasonable choices would not. We draw lessons from our analysis for empirical practice and for ways in which researchers can find false positives results.
During 2005-2007, the SEC conducted a randomized trial in which it removed shortsale restrictions from one-third of the Russell 3000 firms. Early studies found no effect of removing the restrictions on short interest, share returns, volatility, or price efficiency. In prior work, we confirm the lack of evidence for a natural causal channel that could explain these results (Black et al., 2024). Yet over 80 studies report evidence for a wide range of indirect effects on firms from the experiment. Given the lack of a causal channel, many of these results are likely to be false positives. We confirm that suspicion by closely reexamining the principal results from 10 of these studies using a simple specification with firm and year fixed effects. None of the results survive. We then examine best-match specifications that closely follow the sample selection, methodology and specification reported in the respective papers. Again, we mostly obtain null results. The gaps between reported and best match results reflects reported coefficients being much larger in magnitude, reported standard errors being much smaller than best-match, or both. The large gaps between best-match and reported results implies that the study authors made choices that are not evident from the explanation of their research design, which strongly affected the reported results. Our results suggest that researchers retain extensive discretion over the sample and model specification, even (as here) for a true randomized experiment. The choices in these studies produced statistically significant results when other reasonable choices would not. We draw lessons from our analysis for empirical practice and for ways in which researchers can find false positives results.
Pre-Analysis Plan for the Reg SHO Reanalysis Project (with Bernard Black, Hemang Desai, Kate Litvak, and Jeff Jiewei Yu)
Pre-Analysis Plan for the Reg SHO Reanalysis Project (with Bernard Black, Hemang Desai, Kate Litvak, and Jeff Jiewei Yu)
Liquidity, Taxes and Yield Spreads between Tax-exempt and Taxable Bonds (with Chunchi Wu)
Liquidity, Taxes and Yield Spreads between Tax-exempt and Taxable Bonds (with Chunchi Wu)
Work-In-Progress
Work-In-Progress
AI in Asset Management: A Performance Comparison Between AI-Powered Mutual Funds and Quantitative Hedge Funds (with Holly Ireton)
AI in Asset Management: A Performance Comparison Between AI-Powered Mutual Funds and Quantitative Hedge Funds (with Holly Ireton)
Is There a Quality Payoff from an Extra Year of Cardiology Training? (with Bernard Black and Ali Moghtaderi)
Is There a Quality Payoff from an Extra Year of Cardiology Training? (with Bernard Black and Ali Moghtaderi)
Municipal Bond Pricing and Banking Consolidation (with Charles Trzcinka and Brian Wolfe)
Municipal Bond Pricing and Banking Consolidation (with Charles Trzcinka and Brian Wolfe)
Teaching
Teaching
Instructor
Instructor
FIN 442 Intermediate Financial Management (HyFlex: Spring 2021, Fall 2020), Central Michigan University
FIN 442 Intermediate Financial Management (HyFlex: Spring 2021, Fall 2020), Central Michigan University
FIN 425 Options and Futures (HyFlex), Central Michigan University
FIN 425 Options and Futures (HyFlex), Central Michigan University
FIN 332 Managerial Finance, Central Michigan University
FIN 332 Managerial Finance, Central Michigan University
FIN 302 Integrated Financial Analysis (Online: Fall 2023, HyFlex: Spring 2021, Fall 2020), Central Michigan University
FIN 302 Integrated Financial Analysis (Online: Fall 2023, HyFlex: Spring 2021, Fall 2020), Central Michigan University
FIN 604 Managerial Finance (MBA - Online), Saginaw Valley State University
FIN 604 Managerial Finance (MBA - Online), Saginaw Valley State University
FIN 405 Financial Policy, Saginaw Valley State University
FIN 405 Financial Policy, Saginaw Valley State University
FIN 403 Advanced Financial Management (Online), Saginaw Valley State University
FIN 403 Advanced Financial Management (Online), Saginaw Valley State University
FIN 304 Financial Management, Saginaw Valley State University
FIN 304 Financial Management, Saginaw Valley State University
FIN 302 Investment Analysis, Saginaw Valley State University
FIN 302 Investment Analysis, Saginaw Valley State University
FIN 104 Consumer Finance, Saginaw Valley State University
FIN 104 Consumer Finance, Saginaw Valley State University
MGE 302 Applied Economics, State University of New York at Buffalo
MGE 302 Applied Economics, State University of New York at Buffalo
Ad Hoc Instructor, State University of New York at Buffalo
Ad Hoc Instructor, State University of New York at Buffalo
MGF 696 Portfolio Theory and Strategy (MBA), Fall 2014 & Spring 2014
MGF 696 Portfolio Theory and Strategy (MBA), Fall 2014 & Spring 2014
MGF 635 Financial Derivatives (MBA), Fall 2014 & Spring 2013
MGF 635 Financial Derivatives (MBA), Fall 2014 & Spring 2013
MGF 407 Financial Derivatives and Their Markets, Fall 2014
MGF 407 Financial Derivatives and Their Markets, Fall 2014
MGF 402 Investment Management, Spring 2013
MGF 402 Investment Management, Spring 2013
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