Publications:
1. Can Markets Discipline Government Agencies? Evidence from the Weather Derivatives Market
with Amiyatosh Purnanandam. The Journal of Finance, 2016, 71: 303–334.
We analyze the role of financial markets in shaping the incentives of government agencies using a unique empirical setting: the weather derivatives market. We show that the introduction of weather derivative contracts on the Chicago Mercantile Exchange (CME) improves the accuracy of temperature measurement by 13% to 20% at the underlying weather stations. We argue that temperature-based financial markets generate additional scrutiny of the temperature data measured by the National Weather Service, which motivates the agency to minimize measurement errors. Our results have broader implications: the visibility and scrutiny generated by financial markets can potentially improve the efficiency of government agencies.
2. Financial Sector Stress and Risk Sharing: Evidence from the Weather Derivatives Market
Review of Financial Studies, forthcoming.
Internet Appendix
I examine the impact of financial sector stress on risk sharing in a novel setting: the Chicago Mercantile Exchange’s weather derivatives market. The structure of the market allows me to disentangle price movements due to financial sector stress from price movements due to fundamentals. In normal times, contracts are priced near their actuarially fair value. During the recent financial crisis, contract prices one-month before maturity fall by 2.2%. This discount is equivalent to an annualized weather risk premium of 26%. Contract prices decline with increases in the TED spread and the VIX and the declines are greatest for high margin and high total risk contracts. Examining end users, I find the risk-sharing benefits of the weather derivatives market decrease during the crisis. The results provide causal evidence of the importance of financial sector stress in the pricing of financial contracts and its effect on risk sharing in the economy.
with Amiyatosh Purnanandam. The Journal of Finance, 2016, 71: 303–334.
We analyze the role of financial markets in shaping the incentives of government agencies using a unique empirical setting: the weather derivatives market. We show that the introduction of weather derivative contracts on the Chicago Mercantile Exchange (CME) improves the accuracy of temperature measurement by 13% to 20% at the underlying weather stations. We argue that temperature-based financial markets generate additional scrutiny of the temperature data measured by the National Weather Service, which motivates the agency to minimize measurement errors. Our results have broader implications: the visibility and scrutiny generated by financial markets can potentially improve the efficiency of government agencies.
2. Financial Sector Stress and Risk Sharing: Evidence from the Weather Derivatives Market
Review of Financial Studies, forthcoming.
Internet Appendix
I examine the impact of financial sector stress on risk sharing in a novel setting: the Chicago Mercantile Exchange’s weather derivatives market. The structure of the market allows me to disentangle price movements due to financial sector stress from price movements due to fundamentals. In normal times, contracts are priced near their actuarially fair value. During the recent financial crisis, contract prices one-month before maturity fall by 2.2%. This discount is equivalent to an annualized weather risk premium of 26%. Contract prices decline with increases in the TED spread and the VIX and the declines are greatest for high margin and high total risk contracts. Examining end users, I find the risk-sharing benefits of the weather derivatives market decrease during the crisis. The results provide causal evidence of the importance of financial sector stress in the pricing of financial contracts and its effect on risk sharing in the economy.
Working Papers:
1. Revealed Heuristics: Evidence from Investment Consultants' Search Behavior
with Sudheer Chava and Soohun Kim
SFS Cavalcade*, WFA*, Oregon SFC*, Helsinki*, NFA*, MARC (Outstanding Paper Award), Cavalcade Asia, Behavioural Finance WG*, MFHFFI*, APFM
Using proprietary data from a major fund data provider, we analyze the screening activity of investment consultants (ICs) who advise institutional investors with trillions of dollars in assets. We find that ICs frequently shortlist funds using threshold screens clustered at round, base 5 or base 10 numbers: $500MM for AUM, 0% for the return net of a benchmark, and quartiles for return percentile rank screens. A fund’s probability of being eliminated by a screen is significantly negatively related to its future fund attention and flows, with funds just above the $500MM AUM threshold getting 14 to 18% more page views and 5 to 9 pps greater flows over the next year compared to similar funds just below the threshold. Our results are consistent with ICs using a two-stage, consider-then-choose decision making process, and cognitive reference numbers in selecting screening thresholds.
2. Disaster Lending: "Fair" Prices, but "Unfair" Access
with Taylor Begley, Umit Gurun, and Amiyatosh Purnanandam
Red Rock, Univ. of KY, FIRS*, Finance Down Under, MoFiR*, Front Range, CFIC, EBCN, MFA, MD4SG
We find that under risk-insensitive loan pricing – a feature present in many government programs – marginal credit quality borrowers are less likely to receive credit. By restricting price flexibility, marginal applicants that would likely receive a loan at a higher interest rate are instead denied credit altogether. Our particular setting is the Small Business Administration’s disaster-relief home loan program, where risk-based pricing is absent, but screening on credit quality remains. We find that this program denies more loans in areas with larger shares of minorities, subprime borrowers, and higher income inequality, even relative to private market denial rates. Thus, despite ensuring “fair” prices, risk-insensitive pricing may lead to “unfair” access to credit. As a consequence, the government’s own lending program ends up denying credit to minority and poor borrowers at a higher rate than private markets.
3. Estimating Financial Constraints with Machine Learning
with Matt Linn
We use random decision forests, a statistical machine learning technique, to classify firms’ equity and debt constraints using only firm-level financial information. We train our model on the Hoberg and Maksimovic (2015) text-based measures, which are informative, but lack coverage. By mapping to financial variables we are able to extend the coverage of the text-based measures both in the cross-section and the time series, increasing the number of classified firm-years by 245%. Our method captures important non-linearities and interactions between financial variables and constraints and exhibits significant out-of-sample performance. We assess the informativeness of our constraint classifications using multiple tests — tests that commonly-used indices have been previously shown to fail. Our classifications perform well. They even outperform the Hoberg and Maksimovic (2015) classifications over a similar sample period likely due to the increased coverage.
4. Are Market Returns Predictable? (R&R Review of Asset Pricing Studies)
with Jussi Keppo and Tyler Shumway
Appendix
Helsinki, Fed HH Econ. and Decision-Making, AEA
We document significant persistence in the ability of individual investors to time the stock market, including during periods that people describe as bubbles. Using data on all trades by individual Finnish investors over more than 14 years, we show that investors who successfully time the market in the first half of the sample are more likely to successfully time in the second half. We further show that investors who time the market during the run-up and crash around 2000 are more likely to time the run-up and crash around 2008. Our evidence suggests that it is possible to use the trading patterns of these smart investors to anticipate market movements, lending some credibility to the view that market bubbles are identifiable in real time.
5. Seeing the Forest Through the Trees: Do Investors Under-react to Systemic Events?
with Matt Linn
NFA, Dolomites
We examine whether investors efficiently incorporate the effect of financial sector shocks into the equity prices of non-financial firms. Shocks to the financial sector are complex macroeconomic events affecting many firms to varying degrees. Prices may adjust slowly in response to financial sector shocks if information processing capabilities are constrained. We test this hypothesis by examining the ability of systemic risk shocks to predict future returns of financially constrained firms. We find that in the month following the month of large systemic events, large debt constrained firms exhibit significantly lower returns than unconstrained firms with an average relative risk-adjusted return of -3.5% (-42% annualized). More generally, innovations in systemic risk exhibit significant predictability for returns of constrained firms in excess of unconstrained firms. To identify financially constrained firms we introduce a new classification method using random decision forests, a machine learning technique. This method allows us to extend the measures based on textual analysis of firm filings both in the cross-section and time series using only financial information. Overall, the results provide evidence that the market is slow to incorporate the effects of financial sector shocks into equity prices.
with Sudheer Chava and Soohun Kim
SFS Cavalcade*, WFA*, Oregon SFC*, Helsinki*, NFA*, MARC (Outstanding Paper Award), Cavalcade Asia, Behavioural Finance WG*, MFHFFI*, APFM
Using proprietary data from a major fund data provider, we analyze the screening activity of investment consultants (ICs) who advise institutional investors with trillions of dollars in assets. We find that ICs frequently shortlist funds using threshold screens clustered at round, base 5 or base 10 numbers: $500MM for AUM, 0% for the return net of a benchmark, and quartiles for return percentile rank screens. A fund’s probability of being eliminated by a screen is significantly negatively related to its future fund attention and flows, with funds just above the $500MM AUM threshold getting 14 to 18% more page views and 5 to 9 pps greater flows over the next year compared to similar funds just below the threshold. Our results are consistent with ICs using a two-stage, consider-then-choose decision making process, and cognitive reference numbers in selecting screening thresholds.
2. Disaster Lending: "Fair" Prices, but "Unfair" Access
with Taylor Begley, Umit Gurun, and Amiyatosh Purnanandam
Red Rock, Univ. of KY, FIRS*, Finance Down Under, MoFiR*, Front Range, CFIC, EBCN, MFA, MD4SG
We find that under risk-insensitive loan pricing – a feature present in many government programs – marginal credit quality borrowers are less likely to receive credit. By restricting price flexibility, marginal applicants that would likely receive a loan at a higher interest rate are instead denied credit altogether. Our particular setting is the Small Business Administration’s disaster-relief home loan program, where risk-based pricing is absent, but screening on credit quality remains. We find that this program denies more loans in areas with larger shares of minorities, subprime borrowers, and higher income inequality, even relative to private market denial rates. Thus, despite ensuring “fair” prices, risk-insensitive pricing may lead to “unfair” access to credit. As a consequence, the government’s own lending program ends up denying credit to minority and poor borrowers at a higher rate than private markets.
3. Estimating Financial Constraints with Machine Learning
with Matt Linn
We use random decision forests, a statistical machine learning technique, to classify firms’ equity and debt constraints using only firm-level financial information. We train our model on the Hoberg and Maksimovic (2015) text-based measures, which are informative, but lack coverage. By mapping to financial variables we are able to extend the coverage of the text-based measures both in the cross-section and the time series, increasing the number of classified firm-years by 245%. Our method captures important non-linearities and interactions between financial variables and constraints and exhibits significant out-of-sample performance. We assess the informativeness of our constraint classifications using multiple tests — tests that commonly-used indices have been previously shown to fail. Our classifications perform well. They even outperform the Hoberg and Maksimovic (2015) classifications over a similar sample period likely due to the increased coverage.
4. Are Market Returns Predictable? (R&R Review of Asset Pricing Studies)
with Jussi Keppo and Tyler Shumway
Appendix
Helsinki, Fed HH Econ. and Decision-Making, AEA
We document significant persistence in the ability of individual investors to time the stock market, including during periods that people describe as bubbles. Using data on all trades by individual Finnish investors over more than 14 years, we show that investors who successfully time the market in the first half of the sample are more likely to successfully time in the second half. We further show that investors who time the market during the run-up and crash around 2000 are more likely to time the run-up and crash around 2008. Our evidence suggests that it is possible to use the trading patterns of these smart investors to anticipate market movements, lending some credibility to the view that market bubbles are identifiable in real time.
5. Seeing the Forest Through the Trees: Do Investors Under-react to Systemic Events?
with Matt Linn
NFA, Dolomites
We examine whether investors efficiently incorporate the effect of financial sector shocks into the equity prices of non-financial firms. Shocks to the financial sector are complex macroeconomic events affecting many firms to varying degrees. Prices may adjust slowly in response to financial sector shocks if information processing capabilities are constrained. We test this hypothesis by examining the ability of systemic risk shocks to predict future returns of financially constrained firms. We find that in the month following the month of large systemic events, large debt constrained firms exhibit significantly lower returns than unconstrained firms with an average relative risk-adjusted return of -3.5% (-42% annualized). More generally, innovations in systemic risk exhibit significant predictability for returns of constrained firms in excess of unconstrained firms. To identify financially constrained firms we introduce a new classification method using random decision forests, a machine learning technique. This method allows us to extend the measures based on textual analysis of firm filings both in the cross-section and time series using only financial information. Overall, the results provide evidence that the market is slow to incorporate the effects of financial sector shocks into equity prices.
Papers-In-Progress:
6. Dream Chasers: House Price Booms and the Misallocation of Human Capital
with Taylor Begley and Peter Haslag
We examine the effect of the early 2000s house price boom on human capital allocation. We use detailed data on the career paths of over 25 million individuals to document significant increases in employment in real estate-related professions during the house price boom with greater increases in areas with greater house price growth. We focus our analysis on individuals that switched jobs into the real estate agent profession during the boom. We show that boom-period switchers earned similar wages – not a premium – compared to non-switchers during the boom. At the onset of the house-price bust, however, there was strong divergence. Boom-period switchers ended up in jobs with annual wages about $12,000 lower than non-switchers, and this difference has not dissipated. We use sharp upturns in local house prices and undergraduate MSA house prices as instrumental variables to show the causal effect of boom-induced switching into real estate agent on long-term wages. Our results highlight important long-term effects of asset price cycles on the (mis)allocation of human capital.
*scheduled