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Stander Symposium abstract book - University of Dayton

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9:00 AM to 10:30 AM<br />

Non Domestic Revenue Trends and Stock Performance for Flyer Fund Stocks 2005-2011<br />

Presenter(s): AJ P Ziegler<br />

Advisor(s): Robert D Dean, John E Rapp<br />

Economics and Finance - Independent Research<br />

Increasingly, American companies are turning to overseas markets to grow top line revenue and bottom line earnings. The purpose <strong>of</strong> this study is<br />

to analyze revenue growth and revenue share trends for Flyer Fund stocks and overseas markets over the period 2005-2011. The primary objectives<br />

are to determine: 1) How many <strong>of</strong> the 55 Flyer Fund companies are selling overseas, 2) How fast is overseas revenue growing, 3) Is share <strong>of</strong> total<br />

revenue coming from overseas increasing?, 4) Are companies selling overseas experiencing growth in their share prices? Overseas revenue growth<br />

rates and revenue share percentages are calculated for each year as well as for the complete period. Stocks are also identified by industry/sector<br />

classifications. Results <strong>of</strong> the analysis are forthcoming.<br />

Same Old (New) Deal? Examining the Determinates <strong>of</strong> ARRA Spending<br />

Presenter(s): Amy A Pancher<br />

Advisor(s): Tony S Caporale<br />

Economics and Finance - Honors Thesis<br />

Some twenty-one months before the November 2010 elections, the United States Congress passed the American Recovery and Reinvestment<br />

Act <strong>of</strong> 2009, the primary goals <strong>of</strong> which were to preserve and create jobs, promote economic recovery, and to assist those most impacted by the<br />

recession. Previous studies have questioned whether political considerations played a role in the allocation <strong>of</strong> New Deal stimulus spending during<br />

the Roosevelt administration. The same question can be asked <strong>of</strong> the American Recovery and Reinvestment Act <strong>of</strong> 2009; was the $275 billion in<br />

government grants, contracts, and loans allocated solely in accordance with the legislationâs stated goals, or, considering the importance <strong>of</strong> an<br />

election held in a decennial census year and the subsequent congressional redistricting, did political determinates also play a role in how the<br />

funds were distributed?<br />

Upside/Downside Capture Ratios and S&P 500 Sector Returns in Volatile Markets<br />

Presenter(s): Chris P Sammons, Jessica Thomas<br />

Advisor(s): Robert D Dean, John E Rapp<br />

Economics and Finance - Independent Research<br />

The purpose <strong>of</strong> this study is to determine which S&P 500 sectors outperform in four types <strong>of</strong> markets. The first market is a long-term market that<br />

includes a series <strong>of</strong> upswings and downswings with an overall upward trend. We use the time period 2005-2011 to represent this type <strong>of</strong> market<br />

period. The second type <strong>of</strong> market, a downswing market, is represented by the time period 1/1/08-3/31/09. The third type <strong>of</strong> market, an upswing<br />

market, is represented by the time period 3/31/09-12/31/10. The fourth type <strong>of</strong> market is a trading range, or overall flat market, and is represented<br />

by the 12 months in 2011. Upside and downside capture ratios are calculated for all four market for all 10 S&P 500 sectors and compared for<br />

outperformance. Results are forthcoming.<br />

Exploring the Job Shop Queuing Environment<br />

Presenter(s): Zachary M Kaylor<br />

Advisor(s): John J Kanet<br />

MIS, OM, and Decision Sciences - Honors Thesis<br />

My research revolves around the job shop environment. The job shop is an environment in manufacturing where jobs arrive randomly and take a<br />

random amount <strong>of</strong> time to complete. For instance, a maker <strong>of</strong> specialized metal parts most likely involves some sort <strong>of</strong> job shop. The job shop can<br />

also represent a bottleneck in a larger manufacturing process. The main obstacle in the job shop is the queue <strong>of</strong> jobs waiting to be worked upon.<br />

The trick is to order the queue so as to perform the jobs to optimize for various metrics. These metrics vary and involve things such as the average<br />

time in shop to the aggregate lateness <strong>of</strong> the jobs. My research explores the repercussions <strong>of</strong> ordering the jobs using various queueing rules under<br />

a variety <strong>of</strong> conditions.<br />

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