Essays on Behavioral Economics and the Utility of Gambling

Chapter 1: Using Randomization Methods to Analyze Treatment Significance on Behavioral Finance Experiments. Using R.A. Fisher's (1935) randomization methods for statistical inference, this paper examines how stock market crashes affect future return beliefs using Safford et al. (2018) experimental data. Safford et al. (2018), find that experiencing a crash causes a significant difference in the overall belief distributions between individuals who experienced a market crash at the beginning of an investment task, and individuals who never experienced a crash. The randomization results show that market crashes do not significantly alter future return expectations, as the post-crash belief distribution - evaluated at the mean, standard deviation, skewness, and kurtosis - is not statistically different from those that did and did not experience a market crash. This indicates that the correlation between market crashes and changes in risk aversion passes mainly through a preference channel rather than a belief channle just as Bucciol and Zarri (2015); Voors et al. (2012); Callen et al. (2014); Kim and Lee (2014) have found. Chapter 2: The Effects of Intergenerational Investment Advice. Building on Thomas (2023), this study investigates the compounded intergenerational transmission of investment advice and its impact on financial decision-making across three generations. By examining the effects of both positive and negative advice from parents and grandparents, our research investigates the influence of familial guidance on the investment behaviors and risk perceptions of the younger generation. Our findings confirm that advice from previous generations significantly affects the investment choices of current generations, with double positive advice from both parents and grandparents boosting endowment allocations to risky assets, whereas double negative advice increases investment in safer assets. Interestingly, while the first experiment's impact of advice was mediated through beliefs, our study suggests that risk aversion plays a crucial role. This research underscores the profound influence of intergenerational advice on financial decision-making, offering new insights into the psychological and behavioral facets of investing. Chapter 3: Revisiting the Utility of Gambling: Insights from a Machine Learning Analysis. This study examines the predictive capabilities of machine learning models in identifying distinct gambling behaviors among slot machine players. By utilizing an extensive dataset of 38,299 unique slot machine players with over 24 million gambles, our research investigates the reasons behind prolonged gambling sessions versus shorter engagements. By integrating insights from Conlisk's (1993) theoretical framework on the utility of gambling, modern psychology theory, and advanced machine learning techniques, we developed two predictive models that can classify gamblers based mainly on their interaction with the slot machine within the first 15 minutes of play and at the end of their initial gambling session. These models, one based on a Random Forest and the other on a Logistic Regression classifier, demonstrate robust predictive power, with the Random Forest model achieving an accuracy score of up to 0.912 and an area under the receiver operating characteristics curve (AUC) score of 0.903, and the Logistic Regression model reaching accuracy scores of 0.814 and AUC values of up to 0.842. Our analysis reveals that initial betting amount, gambling pace, players' age, number of changes in the machines played, number of unique machines utilized, the account depletion rate, and the utility generated by each bet are significant predictors of a player's likelihood to engage in prolonged gambling sessions. Our findings underscore the potential of machine learning in developing early intervention strategies that could lead to responsible gambling practices and the creation of predictive tools for gambling disorder prevention.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctorate Degree

Grantor

University of Nevada, Reno

Year dc:date.issued

2024

Author dc:creator

• Solorio, Mauricio

Advisor dc:contributor.advisor

• Guerrero, Federico

Committee members dc:contributor.committeemember

• Nichols, Mark
• Sundali, James
• Ridinger, Garret
• O'Donohue, William

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11027

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11027