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Description
In this paper we conduct an out-of-sample test on gross profitability and investment in the same manner as Davis, Fama, and French (2000) for the pre-Compustat period (1926-1955). We hand-collect financial statement data from Moodys Industrial Manuals using the company PERMNO list first created by DFF. In total, we collect data from 1,291 firms, largely industrial firms but with some utilities. We then run Fama-Macbeth (1973) regressions using gross profit, scaled operating profit, scaled net income, and investment along with existing variables like book-to-market, market equity, one-month reversal, and one-year momentum. We find that the premiums on gross profitability and investment are not significant for any part of our sample period. For the overall sample period as well as the first half (before the 1933 Securities Act), our accounting data is often missing or cross-sectionally inconsistent. Despite the better-quality data in the period after 1935, however, neither gross profitability not investment have significant Fama-Macbeth slopes. We believe this is caused by inconsistent and incomplete accounting data, chiefly the number of firms that combine SG&A and COGS data into one "cost" number and the inclusion of investment-like costs, like R&D, in COGS or SG&A. This causes gross profitability to not reflect direct economic profitability as closely as in prior research. However, net income has significantly positive coefficients during this period and is not subsumed by gross profitability; this contradicts prior research for the post-1962 period. More data cleaning and analysis is needed in order to form firm conclusions on the gross profitability, net income, and investment premiums during this period.
ContributorsBergauer, Stephen (Co-author) / Pashayev, Iskandar (Co-author) / Wahal, Sunil (Thesis director) / Bessembinder, Hank (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This dissertation consists of three essays studying topics in financial economicsthrough the lens of quantitative models. In particular, I provide three examples of the
effective use of data in the disciplining of financial economics models. In the first essay,
I provide evidence of a significant transitory component of aggregate equity payout.
Leading asset pricing models assume exogenous dividend growth processes which are
inconsistent with this fact. I find that imposing market clearing for consumption
and income in these models induces the relevant behaviors in dividend growth, even
when dividend growth is obtained indirectly. In the second essay, I provide a novel
decomposition of the unconditional equity risk premium. In the data, the majority of
the equity premium is attributable to moderate left tail risks, not those associated
with disaster states. In stark contrast to the data, leading asset pricing models do
not predict that this intermediate left tail region meaningfully contributes to the
equity premium. The shortcomings of the models can be pinned on unreasonably low
prices of risk for tail events relative to the data. In the third essay, I document a
large dispersion in household allocations to risky assets conditional on age. I show
that while standard household portfolio choice models can be made to match the
average risky share over the lifecycle, the models fall short of generating sufficient
heterogeneity in the cross-section of household portfolios.
ContributorsBeason, Tyler (Author) / Mehra, Rajnish (Thesis advisor) / Wahal, Sunil (Thesis advisor) / Pruitt, Seth (Committee member) / Schreindorfer, David (Committee member) / Arizona State University (Publisher)
Created2021
Description
My project has been a long journey, one that I have learned a tremendous amount on. The final version of my project has come out to be a booklet teaching first time users of code and python the basic steps of getting started and some vital information that I learned while I was learning the language. I started my thesis with the idea of creating a portfolio of stock, bonds and commodities to determine the best allocation of your money over a 30-year period. To do this, I needed to learn how to code and become proficient quickly so I could create a program that would be powerful enough as well as spit out the correct output in the end. Unfortunately, I fell short of being able to build this portfolio out. I took on the challenge of learning Python on my own with no knowledge of any coding language to see if I could pull the whole project together. I failed, but I learned so much along the way and that I think is more valuable than anything. Since I was unable to complete my code, I shifted my attention to creating a small booklet on the basics of getting started in Python as if you have never looked at a coding language. Many of the tips I discuss in my booklet are problems I struggled with when I began. In the beginning I couldn’t even figure out how to get to a coding platform to begin my work, so I began to research and found many helpful tips that took me quite a while to understand.
ContributorsToumbs, Jason David (Author) / Boguth, Oliver (Thesis director) / Schreindorfer, David (Committee member) / Department of Finance (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05