ASU Electronic Theses and Dissertations
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.
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- All Subjects: Finance
- All Subjects: Skyline Query Processing
- Creators: Davulcu, Hasan
Efficient processing of skyline queries on static data sources, data streams and incomplete datasets
An assumption commonly made by many skyline algorithms is that a skyline query is applied to a single static data source or data stream. Unfortunately, this assumption does not hold in many applications in which a skyline query may involve attributes belonging to multiple data sources and requires a join operation to be performed before the skyline can be produced. Recently, various skyline-join algorithms have been proposed to address this problem in the context of static data sources. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively to obtain the skyline-join results; moreover, the pruning techniques employed to eliminate tuples are largely based on expensive tuple-to-tuple comparisons. On the other hand, most data stream techniques focus on single stream skyline queries, thus rendering them unsuitable for skyline-join queries.
Another assumption typically made by most of the earlier skyline algorithms is that the data is complete and all skyline attribute values are available. Due to this constraint, these algorithms cannot be applied to incomplete data sources in which some of the attribute values are missing and are represented by NULL values. There exists a definition of dominance for incomplete data, but this leads to undesirable consequences such as non-transitive and cyclic dominance relations both of which are detrimental to skyline processing.
Based on the aforementioned observations, the main goal of the research described in this dissertation is the design and development of a framework of skyline operators that effectively handles three distinct types of skyline queries: 1) skyline-join queries on static data sources, 2) skyline-window-join queries over data streams, and 3) strata-skyline queries on incomplete datasets. This dissertation presents the unique challenges posed by these skyline queries and addresses the shortcomings of current skyline techniques by proposing efficient methods to tackle the added overhead in processing skyline queries on static data sources, data streams, and incomplete datasets.
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.
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.