Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Finance

Program Name/Specialization

Finance

Faculty/School

Lazaridis School of Business and Economics

First Advisor

Si Li

Advisor Role

supervise my work and provide feedback

Abstract

This dissertation comprises three essays that investigate topics in corporate finance and applied Econometrics.

The first essay examines how digital credit, a Fintech technology, improves micro-business owners' performance by comparing an economically important but often financially disadvantaged group, migrants, with their comparable natives. Using the data on micro-business owners registered with the largest Fintech firm in China, we find that with the access to microloans, migrants achieve greater business revenues compared to their native counterparts. The differential impact of Fintech on migrants versus natives is more pronounced in the businesses with more financial constraints, in more economically developed areas, for more risk averse business owners, and among the owners who are relatively new to the Fintech platform. Overall, our findings support that Fintech improves financial access and business performance for the financially excluded population.

The second essay applies factor model into selecting a subset of assets for (partial) index replication, based on the latest research on factor models of large dimensions. Our method automates the asset selection process and reduces cost for passive fund managers who finds full replication infeasible or too costly. Our selection methodology is consistent as the sample size and the number of assets jointly approach infinity. Monte Carlo experiments show that our estimated index replica tracks the underlying index with relatively small tracking errors in finite samples. We show the applicability of the method by tracking the S&P 500 equally weighed index and the MSCI USA Small Cap index with promising out-of-sample performance.

The third essay explores another application of factor model on price discovery. We investigate how market completeness influences the price discovery process in the option market, by studying how the introduction of new option contracts on the S&P 500 index has changed the distribution of information content among all S&P500 index options. Using a novel methodology, we quantify the daily contribution of an individual option contract to price discovery. Our results show dramatic changes during the sample period between 2004 and 2018. We document an important shift in price discovery from call to put options, and from long-term contracts to short-term contracts. Our results also suggest that due to market frictions, market completion does not immediately lead to changes in information shares.

Convocation Year

2022

Convocation Season

Fall

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