A woman went to the emergency room for severe abdominal cramping. She was diagnosed with diverticulitis; however, as a precaution, the doctor ordered a CAT scan. The CAT scan revealed a growth on the pancreas, which turned out to be pancreatic cancerâthe real cause of the cramping.
Because of a high potential for misdiagnosis, determining the precise cause of abdominal pain can be time-consuming and challenging. By analyzing case studies of abnormal abdominal findings, nurses can prepare themselves to better diagnose conditions in the abdomen.
In this assignment, you will analyze a SOAP note case study that describes abnormal findings in patients seen in a clinical setting. You will consider what history should be collected from the patients, as well as which physical exams and diagnostic tests should be conducted. formulate a differential diagnosis with several possible conditions.
Sample Solution
previously researched by Hendershott, Jones & Menkveld (2011) and Lyle & Naughton (2015). For this reason, the main theme within this study is to evaluate how increased algorithmic trading has affected analystsâ capabilities to predict future market movements. Removing emotional entities from the market is expected to improve the efficiency of the market and hence decrease the market predictability. Moreover, another sub question is used to develop an empirical foundation for answering the main question which sums up to: Does algorithmic trading lead to less price dispersion within the stock market? Chaboud et al. (2014) show that automated trading strategies are less diverse than strategies used by human investors and that humans are responsible for a larger part of the variance in returns than their algorithmic counterparts. It follows that as algorithms possess more similarities than human traders it leads to suspect that the size of the range of returns also known as dispersion has decreased with increased algorithmic trading. Moreover, when looking at our data graphically it can be observed that return dispe rsion shows a clear downtrend over time, except for some extreme values during the financial crisis in 2008/2009, see Figure 3. Additionally, regressing dispersion against time confirms the downward slope resulting in a negative statistically significant coefficient on time with a p-value of 0.001. Considering that algorithmic trading increased over time it could imply a relation with dispersion. Figure 3. Dispersion against time The current study investigates the effects of algorithmic trading in more detail, by systematically performing fixed effects panel data regressions. This might enable us to see how increased algorithmic trading has affected return dispersion and market predictability. The regression findings lead to the conclusion that dispersion is indeed reduced through increased algorithmic trading. Furthermore, it is found that more algorithmic trading led to smaller prediction errors and hence improved market predictability. In the next chapter, the theoretical framework that was used to establish this research will be discussed, built on the following research questions: Does increased algorithmic trading within the market affect analystsâ capabilities to predict future market movements? Sub question: Does algorithmic trading lead to less price dispersion in the stock market? Theoretical Background Current State of Literature To determine the influence of algorithmic trading on dispersion and market predictability, first of all the origins of trading algorithms and the use of automated trading systems must be investigated. Additionally, to find how fewer human traders impact market predictability>
previously researched by Hendershott, Jones & Menkveld (2011) and Lyle & Naughton (2015). For this reason, the main theme within this study is to evaluate how increased algorithmic trading has affected analystsâ capabilities to predict future market movements. Removing emotional entities from the market is expected to improve the efficiency of the market and hence decrease the market predictability. Moreover, another sub question is used to develop an empirical foundation for answering the main question which sums up to: Does algorithmic trading lead to less price dispersion within the stock market? Chaboud et al. (2014) show that automated trading strategies are less diverse than strategies used by human investors and that humans are responsible for a larger part of the variance in returns than their algorithmic counterparts. It follows that as algorithms possess more similarities than human traders it leads to suspect that the size of the range of returns also known as dispersion has decreased with increased algorithmic trading. Moreover, when looking at our data graphically it can be observed that return dispe rsion shows a clear downtrend over time, except for some extreme values during the financial crisis in 2008/2009, see Figure 3. Additionally, regressing dispersion against time confirms the downward slope resulting in a negative statistically significant coefficient on time with a p-value of 0.001. Considering that algorithmic trading increased over time it could imply a relation with dispersion. Figure 3. Dispersion against time The current study investigates the effects of algorithmic trading in more detail, by systematically performing fixed effects panel data regressions. This might enable us to see how increased algorithmic trading has affected return dispersion and market predictability. The regression findings lead to the conclusion that dispersion is indeed reduced through increased algorithmic trading. Furthermore, it is found that more algorithmic trading led to smaller prediction errors and hence improved market predictability. In the next chapter, the theoretical framework that was used to establish this research will be discussed, built on the following research questions: Does increased algorithmic trading within the market affect analystsâ capabilities to predict future market movements? Sub question: Does algorithmic trading lead to less price dispersion in the stock market? Theoretical Background Current State of Literature To determine the influence of algorithmic trading on dispersion and market predictability, first of all the origins of trading algorithms and the use of automated trading systems must be investigated. Additionally, to find how fewer human traders impact market predictability>
