Date of Award

Spring 5-2020

Degree Type


Degree Name


Degree Program

Financial Economics


Business Administration

Major Professor

Dr. Tarun Mukherjee

Second Advisor

Dr. Atsuyuki Naka

Third Advisor

Dr. Sudha Krishnaswami

Fourth Advisor

Dr. Ramesh Adhikari


This dissertation consists of two essays. In the first essay, I introduce a new measure of the firm life cycle and compare its efficacy with the three existing life cycle proxies: ‘cashflow patterns’, ‘earned contributed capital mix’, and the firm’s public ‘age’. More specifically, I show that two groups of firms, similar in all respects except in their innovations efficiencies, will adopt different dividend policies regardless of their calendar age, earned income, or the cash flow patterns. I employ a large sample of US manufacturing firms spanning from 1973 to 2017. I find that more innovative firms pay lower dividends than the less innovative firms, irrespective of how we describe the life cycle stages. Besides, I perform a comprehensive cross-sectional look at the interrelations among various factors, including innovation output, growth, firm life cycle, and the dividend payout. I conclude that the intensity of innovation outputs has a direct relation with the firm's growth rate, and that, in turn, affects the firm’s life cycle, and thereby its dividend policy.

In the second essay, I evaluate the returns to scale, tracking error, and the role of fund characteristics on the ETFs risk-return performance. I investigate the impact of asset base size growth on the risk-adjusted performance and on the tracking ability of ETFs to their benchmark indices. I use the quantile regression approach with survivorship biased free non-leveraged, non-active, equity-only ETFs sample for ten years. I find that the ‘universe of equity ETFs’ do not provide increasing returns to scale. The results show that the size has a more substantial negative impact on the highest performing quantiles of the ETF cluster. I also observe that the ‘illiquidity,’ ‘expense ratio,’ the ‘equal-weighted index composition’ among others are the main key drivers that exacerbate the inverse relationship between the size and the performance. However, the core blend style and the capitalization-weighted index composition have a positive effect. Finally, I conclude a negative relationship between the size and the tracking error. I document that the ‘illiquidity,’ ‘expense ratio,’ and ‘volatility’ have a positive relationship with the tracking error.


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