The Sub-proportionality of Subjective Probability Weighting in Poker
by William Clark
Abstract
This study uses Texas Hold’em poker to investigate decision-making under
uncertainty and the concept of probability weighting, where individuals may
overvalue or undervalue uncertain outcomes. I conduct an experiment to assess
Cumulative Prospect Theory’s relevance to subjective probabilities in poker by
simplifying the game to compare complex and simple gamble evaluations. The
research aims to understand how risk preferences and probability estimation
without complete information are influenced by individuals’ poker experience
and framing effects. We find that deviations from what theory predicts in the
subjective-probability Poker frame can be explained well by the framing effects
made in the decision maker’s editing phase. By examining the difference in the
predictive power of decision making models in explicit vs subjective probability
gambles, the study seeks to improve comprehension of cognitive processes in
navigating uncertainty.
Professor Philipp Sadowski, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Classification: C91, D80, D91
A Two-Stage Analysis Considering Gun Theft & Overall Crime: Evidence from Child Access Prevention Laws
by Ronan Brew
Abstract
Child Access Prevention Laws (CAP) came to prominence in the early 1990s in the wake of the highest
recorded rate of overall and adolescent firearm deaths seen in the United States at that time, placing
mandatory firearm storage requirements on adults living in a home with children. While the primary – and
perhaps sole – intention behind these policies are to prevent adolescent gun death, I contend CAP laws have
the added function of reducing the rate of firearms stolen from homes due to the legal incentives against
improper firearm storage. In the first of a two-stage analysis, CAP laws are proven to substantially reduce
the rate of household firearm theft based on the ascending stringency of different CAP law storage
requirements. The scope of the study is then widened in the second stage of analysis, where I demonstrate
the overall impact illicitly-obtained firearms have on predicting increased firearm homicides.
Professor John DeSimone, Faculty Advisor
JEL Codes: C23, K00, K42
The Case for Clemency: Differential Impacts of Pretrial Detention on Case and Crime Outcomes
By George Rateb
Abstract
About half-million of individuals in US jails are detained pretrial while legally presumed
innocent. Using data on quasi-randomly assigned bail judges in the third-largest court system in
the U.S., we study the impact of pretrial detention on defendants’ court and crime outcomes
between 2008 and 2012. We supplement our primary analysis to document patterns on bail
amounts and how they differentially impact Black defendants relative to their white and Hispanic
counterparts. Instrumental variable estimates suggest that pretrial detention increases the
likelihood of being found guilty, mainly driven by the uptake of guilty pleas, especially for
minorities. By linking court and jail data, we provide mechanistic evidence that jail time is
positively correlated with the uptake of these guilty pleas. To the best of our knowledge, these
findings have not been empirically documented due to a lack of previous data availability.
Professor Bocar Ba, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL classification: C26; J15; K14
Tale of Two Cities An Econometric Analysis of East & West Coast Fine Art Galleries
By Daniella Victoria Paretti
Abstract
In a 2021 report published alongside Art Basel and UBS, renowned cultural economist Dr.
Clare McAndrew posited that the value of art sales in 2020 amounted to an impressive $50 billion
(although this actually marks an over 10-year low). It is no secret that the global art markets are
extremely lucrative, attracting the interest of industry magnates and business tycoons alike.
Though it is important to note that art markets are historically quite distinct from their normal good
counterparts — the sector is laden with issues regarding transparency, high barriers to entry, and
hiding of wealth. Amidst the COVID-19 pandemic, however, the tides began to turn; online
platforms for museums, auction houses, and galleries were employed more than ever before,
effectively modernizing the antiquated industry and expanding its reach to new consumers. How
has this trend of digitalization changed and improved art markets? More specifically, how can data
analytics and other technological resources serve the interests of private galleries? Using sales data
from a parent gallery with multiple locations across the United States (each displaying similar
works/artists), I have conducted a number of qualitative and statistical analyses to identify key
differences between the West and East coast locations. In short, the gallery on the West coast sold
more works and at a lower average cost than its counterpart, providing key insights into this local
market’s consumer base. Beyond this, factors like size, medium, and artist gender were found to
have statistically significant effects on the ultimate sale price and turnover rate of works. My
findings suggest that means of data analytics should be utilized by all actors in the art markets to
optimize their approach to business, as well as understand their consumers better than ever before.
Professor Michelle Connolly, Faculty Advisor
Professor Hans Van Miegroet, Faculty Advisor
JEL classification: Z11, C10, J11, O33
Bayesian Non-Parametric Risk Metric
By Kiwan Hyun
Abstract
This thesis constructs completely non-parametric Risk Metric models through Dirichlet process in order to account for both the parametric uncertainty and model uncertainty that a Risk Metric may bring.
Value at Risk (VaR), along with its integrated form Continuous Value at Risk (CVaR) / Expected Shortfall (ES), is one of the most frequently used risk metrics in finance. VaR is a quantile value of forecasted return of a portfolio—linear and non-linear. [Siu, et. al., 2006] According to the Basel 95% and 99% VaR are recommended to be posted by the financial institutions for portfolios and assets; 97.5% CVaR/ES value needs to be set aside when making an investment for “capital buffer”. [Obrenovic & Akhunjonov, 2016] Therefore, an accurate estimation of risk is critical for VaR models and CVaR/ES models.
The traditional approach of a normal approximation to VaR and CVaR/ES has been discredited—especially for daily returns—and even blamed by some for causing the 2008 Financial Crisis [Nocera, 2009] Many advancements have been made to the VaR model including Bayesian inference to the normal model [Siu, et. al., 2006], Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) VaR model [Bollerslev, 1986], and Conditional Autoregressive Value at Risk (CAViaR) model [Engle & Manganelli, 2004]. When tested against 6 years (Jan, 2001 – Jan, 2005) of daily returns data of 10 different market indexes, the Bayesian CAViaR model has shown to be the most accurate in predicting daily 95% and 99% VaR. [Gerlach, et. al., 2011]
However, there were certain years for certain indexes where the 99% Bayesian CAViaR VaR did not perform well, especially for years that had multiple > 5% daily drops. Moreover, the Bayesian CAViaR models—though are almost non-parametric—follow a Skewed-Laplace distribution. To even account for the uncertainty of the likelihood model, this thesis constructed daily 97.5% VaRs for 7 different country indexes for 7 years (Jan, 2012 – Dec, 2019) using the completely non-parametric Dirichlet Process.
The Dirichlet Process 97.5% VaR outperformed all Bayesian Normal, Bayesian GARCH, and Bayesian CAViaR models of years when CAViaR models underperformed. The model may be inefficient for normal years since it is overly conservative. Nevertheless, the non-parametric model still seems to be significantly more accurate during fluctuant years.
Professor Kyle Jurado, Ph.D., Faculty Advisor,
Assistant Professor Simon Mak, Ph.D., Faculty Advisor
Generic Entry and The Effect on Prices in the Prescription Drug Market
By Sahana Giridharan
Abstract
Drug firms have utilized a variety of strategies that contribute to rising drug prices in the
U.S. for the last few years. Strategic entry timing and number of indications a drug is approved
might be two factors that contribute to this rise in prices. While there have been some studies
uncovering a positive relationship between generic entry and branded prices, there has been little
research done on the effects of generic entry on generic prices thus far. This work can impact
policy aimed at decreasing generic drug prices and increasing competition in the generic drug
market.
Oncology and inflammatory bowel disease (IBD) are two disease areas that have a high
price burden to patients in the U.S. today, hence using Medicare Part B Average Sales Price
(ASP) data, I analyze the effect of entry timing on the price of 24 drugs in these two indication
areas. Using the Drugs@FDA Database, I collect data on the FDA approval date of a drug, and
on the indications a drug is approved for. Utilizing OLS, my results suggest that later entry times
lead to lower drug prices, with a 1 year increase in entry time resulting in a 6.99% increase in
prices. Results also suggest that an increase of 1 in the number of indications a drug is approved
for leads to a 49.79% decrease in drug price. This could suggest that having existing generic
competitors in the pharmaceutical market decreases generic prices, and that number of
indications is a strong indicator of drug price.
If the current work is confirmed by future studies similar to this studying entry time and
price in the generic pharmaceutical market, it is possible that future drug policy should focus on
promoting competition within the pharmaceutical market to lower generic prices.
Professor Frank Sloan, Faculty Advisor
Professor Grace Kim, Faculty Advisor
Professor Kent Kimbrough, Faculty Advisor
JEL Classification: L11; I11; C3
Economic Situations and Social Distance: Taxation and Donation
By Alexander Brandt
Abstract:
This experimental study evaluated the effects of two common economic situations –
taxation and donation – on the social distance between participants in the situations, an original
effect of interest that is the opposite of prior research. This study employed a novel survey
framework, in which subjects gave money to others in the economic situations and socially
judged recipients of their money. Findings mostly did not support predictions that the economic
situations would differently affect social distance, but the novel framework enabled an effective
test of the effect of economic situations on social distance and is a major contribution to the field.
Professor Rachel E. Kranton, Faculty Advisor
Professor Scott A. Huettel, Faculty Advisor
Professor Grace Kim, Seminar Advisor
JEL classification: C91; D64; D89; D90
Redefining Resource Allocation in Computing Systems
By Jacob Chasan
A new kernel1 is in town. The current industry-standard for resource allocation on computers does not take the user’s preferences into account, rather programs are given access to resources based on the time that each requested to be run. Although this system can lead to solutions that minimize the time it takes for a program to receive an allocation, it often leads to an incentive misalignment between the programs and the user. This misalignment is exacerbated as the current queue based systems have no inherent mechanism to prevent a tragedy of the commons issue, whereby programs take more resources from the system than the value they provide to the user. By shifting to a market-based approach, where computing resources are allocated to programs based on how much utility the user receives from each program, the incentives of the programs and the users align. With inherent market mechanisms to keep the incentives aligned, this new paradigm leads to at least superior levels of utility for a user.
1As described in subsequent parts of this paper, the kernel is the core program within an operating system which is given the authority to allocate the hardware resources amongst the programs on the computer.
Advisors: Professor Benjamin C. Lee, Professor Atila Abdulkadiroglu, Professor Michelle Connolly | JEL Codes: C8, C80
Hedonic Pricing in the Sneaker Resale Market
By Kevin Ma and Matthew Treiber
This paper explores the secondary resale market for high-end and limited-edition sneakers, specifically analyzing the determinants that affect what value sneakers trade for in the secondary market. While it is common knowledge that the sneaker resale market is a thriving and active secondary market, there is little to no empirical research about what exactly causes such sneakers to sell for exorbitant prices in the resale market. The study utilizes a hedonic pricing approach to investigate the determinants of sneaker resale price. We use a dataset of sneaker resale transactions from the online marketplace StockX between the years of 2016 and 2020 as the basis for our research. After analyzing the results, we have determined that the amount of “hype” that surrounds a sneaker as well as supply scarcity are statistically significant factors when determining the resale price premium a particular sneaker commands in the secondary market. This work adds to the sparse literature on the sneaker resale industry and brings an econometrics-approach to determining the price a given pair of sneakers commands in the resale market.
View Data (Email for Access)
Advisors: Professor Kyle Jurado, Professor Michelle Connolly, Professor Grace Kim| JEL Codes: C2, C20, J19
“Behind the Scenes:” An Empirical Investigation of Broadway Show Success Factors
By Alexander Sanfilippo
This paper analyzes the impact of financial and objective factors on Broadway show success. The analysis differs from previous literature through its exclusive focus on Broadway productions that open between June and February, so defined as the “Pre-Season,” as well as its attempts to establish causality through an instrumental variable regression. Two other methods of analysis are also used in accordance with past research: an ordinary least squares regression and relative risks hazard model. The results demonstrate the significant impact of first week attendance on long-term show success and reiterate the essential function of the Tony Awards in Broadway survival. This paper also introduces the positive impact of ticket pricing on show survival. Discussion on the implications surrounding the difficulty of obtaining show-specific budget data concludes the paper, arguing that this should be an area of future focus and collaboration between researchers and Broadway producers.
Advisors: Professor James Roberts, Professor Brad Rogers, Professor Kent Kimbrough| JEL Codes: C4, C41, C26
