ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

Displaying 1 - 5 of 5
Filtering by

Clear all filters

152668-Thumbnail Image.png

Evidence on the value of director monitoring: a natural experiment

Description
I examine the determinants and implications of the level of director monitoring. I use the distance between directors' domiciles and firm headquarters as a proxy for the level of monitoring and the introduction of a new airline route between director domicile and firm HQ as an exogenous shock to the…

I examine the determinants and implications of the level of director monitoring. I use the distance between directors' domiciles and firm headquarters as a proxy for the level of monitoring and the introduction of a new airline route between director domicile and firm HQ as an exogenous shock to the level of monitoring. I find a strong relation between distance and both board meeting attendance and director membership on strategic versus monitoring committees. Increased monitoring, as measured by a reduction in effective distance, by way of addition of a direct flight, is associated with a 3% reduction in firm value. A reduction in effective distance is also associated with less risk-taking, lower stock return volatility, lower accounting return volatility, lower R&D; spending, fewer acquisitions, and fewer patents.
ContributorsBennett, Benjamin (Author) / Coles, Jeffrey (Thesis advisor) / Hertzel, Michael (Committee member) / Babenka, Ilona (Committee member) / Custodio, Claudia (Committee member) / Arizona State University (Publisher)
Created2014
153574-Thumbnail Image.png

Breaking hash-tag detection algorithm for social media (Twitter)

Description
In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm…

In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter).

The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.

In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
ContributorsAwasthi, Piyush (Author) / Davulcu, Hasan (Thesis advisor) / Tong, Hanghang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
153793-Thumbnail Image.png

Essays in corporate policy: dividend policy, index labeling effect, investment, and cash flow duration

Description
This dissertation consists of two essays on corporate policy. The first chapter analyzes whether being labeled a “growth” firm or a “value” firm affects the firm’s dividend policy. I focus on the dividend policy because of its discretionary nature and the link to investor demand. To address endogeneity concerns, I…

This dissertation consists of two essays on corporate policy. The first chapter analyzes whether being labeled a “growth” firm or a “value” firm affects the firm’s dividend policy. I focus on the dividend policy because of its discretionary nature and the link to investor demand. To address endogeneity concerns, I use regression discontinuity design around the threshold to assign firms to each category. The results show that “value” firms have a significantly higher dividend payout - about four percentage points - than growth firms. This approach establishes a causal link between firm “growth/value” labels and dividend policy.

The second chapter develops investment policy model which associated with du- ration of cash flow. Firms are doing their business by operating a portfolio of projects that have various duration, and the duration of the project portfolio generates dif- ferent duration of cash flow stream. By assuming the duration of cash flow as a firm specific characteristic, this paper analyzes how the duration of cash flow affects firms’ investment decision. I develop a model of investment, external finance, and savings to characterize how firms’ decision is affected by the duration of cash flow. Firms maximize total value of cash flow, while they have to maintain their solvency by paying a fixed cost for the operation. I empirically confirm the positive correlation between duration of cash flow and investment with theoretical support. Financial constraint suffocates the firm when they face solvency issue, so that model with financial constraint shows that the correlation between duration of cash flow and investment is stronger than low financial constraint case.
ContributorsLee, Tae Eui (Author) / Mehra, Rajnish (Thesis advisor) / Tserlukevich, Yuri (Thesis advisor) / Custodio, Claudia (Committee member) / Arizona State University (Publisher)
Created2015
154756-Thumbnail Image.png

Sentiment analysis for long-term stock prediction

Description
There have been extensive research in how news and twitter feeds can affect the outcome of a given stock. However, a majority of this research has studied the short term effects of sentiment with a given stock price. Within this research, I studied the long-term effects of a…

There have been extensive research in how news and twitter feeds can affect the outcome of a given stock. However, a majority of this research has studied the short term effects of sentiment with a given stock price. Within this research, I studied the long-term effects of a given stock price using fundamental analysis techniques. Within this research, I collected both sentiment data and fundamental data for Apple Inc., Microsoft Corp., and Peabody Energy Corp. Using a neural network algorithm, I found that sentiment does have an effect on the annual growth of these companies but the fundamentals are more relevant when determining overall growth. The stocks which show more consistent growth hold more importance on the previous year’s stock price but companies which have less consistency in their growth showed more reliance on the revenue growth and sentiment on the overall company and CEO. I discuss how I collected my research data and used a multi-layered perceptron to predict a threshold growth of a given stock. The threshold used for this particular research was 10%. I then showed the prediction of this threshold using my perceptron and afterwards, perform an f anova test on my choice of features. The results showed the fundamentals being the better predictor of stock information but fundamentals came in a close second in several cases, proving sentiment does hold an effect over long term growth.
ContributorsReeves, Tyler Joseph (Author) / Davulcu, Hasan (Thesis advisor) / Baral, Chitta (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2016
153557-Thumbnail Image.png

Analyzing the effects of Bollinger bands on the probability of stock options using support vector machines

Description

The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value…

The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for δ of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.

Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are θ, δ, γ, v, and ρ. The variable, θ is the rate of change in price of the option due to time decay, δ is the rate of change of the option’s price due to the stock’s changing value, γ is the rate of change of δ, v represents the rate of change of the value of the option in relation to the stock’s volatility, and ρ represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same δ. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of δ reveals how far in or out of the money an option is.

The machine learning technique used to analyze the data and the probability



is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.
ContributorsReeves, Michael (Author) / Richa, Andrea (Thesis advisor) / McCarville, Daniel R. (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015