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The intent of this paper is inform and educate people on micro-investing, so they can better understand this new and growing category of investing. Given that micro-investing is a relatively new phenomenon, people naturally have many questions about it. What is micro-investing, and what makes it different from traditional investing?

The intent of this paper is inform and educate people on micro-investing, so they can better understand this new and growing category of investing. Given that micro-investing is a relatively new phenomenon, people naturally have many questions about it. What is micro-investing, and what makes it different from traditional investing? What are the origins of this growing segment of financial technology? What features and characteristics do micro-investing platforms have in common and what differentiates them from each other? Is micro-investing viable and cost effective, and if so, is it right for you? What is the future of micro-investing, and is it here to stay? This paper seeks to answer these questions and additional questions that the reader may have.
Contributorsde la Vara, Nicholas (Author) / Budolfson, Arthur (Thesis director) / Hoffman, David (Committee member) / Department of Finance (Contributor, Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
I built a short-term West Texas Intermediate (WTI) crude oil price-forecasting model for two periods to understand how various drivers of crude oil behaved before and after the Great Recession. According to the Federal Reserve the Great Recession "...began in December 2007 and ended in June 2009" (Rich 1). The

I built a short-term West Texas Intermediate (WTI) crude oil price-forecasting model for two periods to understand how various drivers of crude oil behaved before and after the Great Recession. According to the Federal Reserve the Great Recession "...began in December 2007 and ended in June 2009" (Rich 1). The research involves two models spanning two periods. The first period encompasses 2000 to late 2007 and the second period encompasses early 2010 to 2016. The dependent variable for this model is monthly average WTI crude oil prices. The independent variables are based on what the academic community believes are drivers of crude oil prices. While the studies may be scattered across different time periods, they provide valuable insight on what the academic community believes drives oil prices. The model includes variables that address two different data groups including: 1. Market fundamentals/expectations of market fundamentals 2. Speculation One of the biggest challenges I faced was defining and quantifying "speculation". I ended up using a previous study's definition of "speculation", which it defined as the activity of certain market participants in the Commitment of Traders report released by the Commodity Futures Trading Commission. My research shows that the West Texas Intermediate crude oil market exhibited a structural change after the Great Recession. Furthermore, my research also presents interesting findings that warrant further research. For example, I find that 3-month T-bills and 10yr Treasury notes lose their predictive edge starting in the second period (2010-2016). Furthermore, the positive correlation between oil and the U.S. dollar in the period 2000-2007 warrants further investigation. Lastly, it might be interesting to see why T-bills are positively correlated to WTI prices and 10yr Treasury notes are negatively correlated to WTI prices.
ContributorsMirza, Hisham Tariq (Author) / McDaniel, Cara (Thesis director) / Budolfson, Arthur (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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This thesis explores the profitability of various technical trading strategies to explore their ability, generate returns in the American stock market. All strategies are based on five popular technical indicators, including the volume-weighted average price, moving average convergent-divergent index, Bollinger bands, support/resistance, and simple momentum trading. Most strategies were tested

This thesis explores the profitability of various technical trading strategies to explore their ability, generate returns in the American stock market. All strategies are based on five popular technical indicators, including the volume-weighted average price, moving average convergent-divergent index, Bollinger bands, support/resistance, and simple momentum trading. Most strategies were tested from 2019-2022 and tested for the SPY and QQQ stocks, representing the S&P 500 and NASDAQ. 3 of the 26 strategies had win rates of over 50%, but several were able to greatly outperform broader market returns. The best performing strategies were based on simple momentum trading, while the MACD and Bollinger Bands produced the worst results. Some strategies based on simple momentum trading or Bollinger bands found results greatly exceeding standard market returns in recent years, but most do not.
ContributorsEberle-Taylor, Nicholas (Author) / Boguth, Oliver (Thesis director) / Ikram, Atif (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2022-05