My creative project is a Python program designed to simulate a $100,000 stock portfolio using real data about the stock market. It runs continuously on my computer and executes the main body of the code once per day at 11:00 am AZ time. It will pull prices from the internet for all stocks in the S&P 500 between 07/01/2023 and now. Each day, the program outputs two .csv files showing the makeup of the portfolio and an aggregated list of all transactions that have taken place. The financial decisions are made using Modern Portfolio Theory and the Efficient Frontier model, balancing risk and maximizing the Sharpe ratio to create the most mathematically optimal portfolio. There is a lot of documentation available to users to show the process of the code through daily executions, how to install required packages, and ultimately how to use the program. It was designed as a simulation for this project but has the potential to be expanded beyond its current bounds and eventually become a legitimate algorithm trading bot.

This dissertation consists of three essays studying the relationship between corporate finance and monetary policy and macroeconomics. In the first essay, I provide novel estimations of the monetary policy’s working capital channel size by estimating a dynamic stochastic macro-finance model using firm-level data. In aggregate, I find a partial channel —about three-fourths of firms’ labor bill is borrowed. But the strength of this channel varies across industries, reaching as low as one-half for retail firms and as high as one for agriculture and construction. These results provide evidence that monetary policy could have varying effects across industries through the working capital channel. In the second essay, I study the effects of the Unconventional Monetary Policy (UMP) of purchasing corporate bonds on firms’ decisions in the COVID-19 crisis. Specifically, I develop a theoretical model which predicts that the firm’s default probability plays a crucial role in transmitting the effects of COVID-19 shock and the UMP. Using the model to evaluate two kinds of heterogeneities (size and initial credit risk), I show that large firms and high-risk firms are more affected by COVID-19 shock and are more responsive to the UMP. I then run cross-sectional regressions, whose results support the theoretical predictions suggesting that the firm’s characteristics, such as assets and operating income, are relevant to understanding the UMP effects. In the third essay, I document that capital utilization and short-term debt are procyclical. I show that a strong positive relationship exists at the aggregate and firm levels. It persists even when I control the regressions for firm size, profits, growth, and business cycle effects. In addition, the Dynamic Stochastic General Equilibrium (DSGE) model shows that in the presence of capital utilization, positive real and financial shocks cause the firm to change its financing of the equity payout policy from earnings to debt, increasing short-term debt.

This dissertation studies how forecasting performance can be improved in big data. The first chapter with Seung C. Ahn considers Partial Least Squares (PLS) estimation of a time-series forecasting model with data containing a large number of time series observations of many predictors. In the model, a subset or a whole set of the latent common factors in predictors determine a target variable. First, the optimal number of the PLS factors for forecasting could be smaller than the number of the common factors relevant for the target variable. Second, as more than the optimal number of PLS factors is used, the out-of-sample explanatory power of the factors could decrease while their in-sample power may increase. Monte Carlo simulation results also confirm these asymptotic results. In addition, simulation results indicate that the out-of-sample forecasting power of the PLS factors is often higher when a smaller than the asymptotically optimal number of factors are used. Finally, the out-of-sample forecasting power of the PLS factors often decreases as the second, third, and more factors are added, even if the asymptotically optimal number of the factors is greater than one. The second chapter studies the predictive performance of various factor estimations comprehensively. Big data that consist of major U.S. macroeconomic and finance variables, are constructed. 148 target variables are forecasted, using 7 factor estimation methods with 11 information criteria. First, the number of factors used in forecasting is important and Incorporating more factors does not always provide better forecasting performance. Second, using consistently estimated number of factors does not necessarily improve predictive performance. The first PLS factor, which is not theoretically consistent, very often shows strong forecasting performance. Third, there is a large difference in the forecasting performance across different information criteria, even when the same factor estimation method is used. Therefore, the choice of factor estimation method, as well as the information criterion, is crucial in forecasting practice. Finally, the first PLS factor yields forecasting performance very close to the best result from the total combinations of the 7 factor estimation methods and 11 information criteria.

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.

This report will provide an analysis of frontier market equity-based investment funds with respect to bivariate correlation analysis, global integration analysis, and US optimized portfolio statistics. My analysis has indicated strong diversification benefits of including frontier market equities in a US portfolio, given its low correlation to US equity concentrated portfolios especially portfolios that would consist of midcap and smallcap stocks. With the drawbacks of the bivariate correlation test, an additional global integration analysis has been included to reaffirm the value frontier markets offer in the form of integration. Integration is a second layer of the diversification analysis. I find that when analyzing frontier markets (FM) against developed markets (DM) there exhibits significantly less integration as compared to emerging markets against developed markets. This analysis goes one step further and quantifies integration of specific frontier market funds against the broader emerging and developed markets. This study finds that iShares MSCI frontier 100 ETF (Ticker: FM) exhibits the least integration amongst Guggenheim Frontier Markets ETF (Ticker: FRN), Templeton Frontier Markets A (Ticker: TFMAX), and Morgan Stanley Frontier Emg (Ticker: MFMIX). Lastly, this analysis covers the inadequacy with using Sharpe ratios and minimum volatility parameters to achieve portfolio optimization under a Monte-Carlo style 1000 portfolio simulation with frontier market funds in a broader US equity portfolio but finds better results when using a US equity and US bond combination portfolio. Overall, this analysis of frontier markets and frontier market funds has shown there still exists significant diversification benefits to US Investors when they engage in FM investments, specifically through diversified FM investment funds.

This thesis aims to promote financial literacy in the community. It was driven by the realization that there was a lack of basic financial knowledge among people at ASU and beyond. The people involved in the reason for the guide had all heard of bonds and understood the basic concepts, but lacked the knowledge of the finite details. The research starts with an overview of the United States bond market and focuses on the creation of a short simple guide. The goal is that anyone can read the guide and have a basic understanding of bonds, talk to financial managers, and do some basic investing. The easy guide is basically a two-page crash course on investing in bonds. Anyone can take a class or watch a video on bonds, but how do they actually start investing in them? This thesis works to answer this question by providing knowledge of real world application. The goal is to take knowledge beyond a book or video and learn from actively investing in a safe and clear way. Bonds are a very useful tool in investing and provide safe returns. The investing proposed is one that would be an alternative to putting money into a savings account. The guide recommends a good starting point of a way to invest in bonds (Specifically the US Treasury). At the same time does some analysis on other investing options for more advanced investors. The work includes an analysis of five bond portfolios and the calculations of finding their actual returns after loads and other fees.