`What is social stratification? What is social mobility? Are there social classes? How are these categorized? How are they defined? How are stratification systems maintained in this society? What are the two ways in which poverty is defined?`

Theory:

`How do Wright's model of social class and Gilbert and Kahl's model of social class fit within this ideal society? Does the society require different social classes? Why or why not?`

People:

`Are people able to advance their social class within this society? How can people advance their social class within this society? Do prejudice and discrimination fit within this society? How can people from one social class connect with people in a different social class? How do people within a minority relate with others? How does poverty play a role in society? Does poverty impact behaviors and interactions of those within the community?`

Sample Solution

To analyze the prediction accuracy of the remaining human analysts within the market, historical Thomson Reuters analystsâ estimations obtained from the IBES dataset are used to obtain the prediction error for a certain forecast. It follows that the difference between the estimation value at time t and the adjusted price on date t divided by the adjusted price on that date gives the prediction error of a certain estimation by analyst i for stock j. Additionally, the prediction error is squared to emphasize on the analysts that were off most in their forecasts, be it below or above. As the squared prediction error will only return positive values it lays focus on just the deviation itself for the direction of the deviation is not of concern. ãPrediction Errorã_(t,i,j)=((ãestimation valueã_(t,i,j)-ãadjusted priceã_(t,j))/ãadjusted priceã_(t,j) )^2 Consecutively, the analyst prediction error variable will then be tested using regression analysis within the Stata statistical analysis software to see if analystsâ predictions have become statistically more accurate since the development of automation within stock markets. The dataset can be described as an unbalanced three-dimensional panel dataset for which stock ticker, date and analyst name represent the dimensions, for every ticker there are different numbers of analyst estimations on varying dates. The âmissingâ data is due to analysts specializing in specific stocks and because the date at which estimations are placed is random, there is however no actual missing data. The ticker and analyst variable are into a new combined variable called tic_alys where each group merely represents the specific forecasts by analyst i for ticker j. This procedure removes the need to drop the third dimension in order to run a multi-dimensional fixed effects panel data regression within Stata. These dimensions are only combined for regression (14) and (16) where firm and analyst fixed effects are included conjointly. To answer the research question the following hypotheses are developed: H0: Analystsâ prediction error is not influenced by increased algorithmic trading H1: Analystsâ prediction error is influenced by increased algorithmic trading These hypotheses lead to the regressions below of which it is expected that analyst prediction error has indeed increased in the period where automation has taken place. It seems unlikely that analysts can predict the direction of future stock prices as the analysts would have to be able to execute transactions faster than the algorithms.>

To analyze the prediction accuracy of the remaining human analysts within the market, historical Thomson Reuters analystsâ estimations obtained from the IBES dataset are used to obtain the prediction error for a certain forecast. It follows that the difference between the estimation value at time t and the adjusted price on date t divided by the adjusted price on that date gives the prediction error of a certain estimation by analyst i for stock j. Additionally, the prediction error is squared to emphasize on the analysts that were off most in their forecasts, be it below or above. As the squared prediction error will only return positive values it lays focus on just the deviation itself for the direction of the deviation is not of concern. ãPrediction Errorã_(t,i,j)=((ãestimation valueã_(t,i,j)-ãadjusted priceã_(t,j))/ãadjusted priceã_(t,j) )^2 Consecutively, the analyst prediction error variable will then be tested using regression analysis within the Stata statistical analysis software to see if analystsâ predictions have become statistically more accurate since the development of automation within stock markets. The dataset can be described as an unbalanced three-dimensional panel dataset for which stock ticker, date and analyst name represent the dimensions, for every ticker there are different numbers of analyst estimations on varying dates. The âmissingâ data is due to analysts specializing in specific stocks and because the date at which estimations are placed is random, there is however no actual missing data. The ticker and analyst variable are into a new combined variable called tic_alys where each group merely represents the specific forecasts by analyst i for ticker j. This procedure removes the need to drop the third dimension in order to run a multi-dimensional fixed effects panel data regression within Stata. These dimensions are only combined for regression (14) and (16) where firm and analyst fixed effects are included conjointly. To answer the research question the following hypotheses are developed: H0: Analystsâ prediction error is not influenced by increased algorithmic trading H1: Analystsâ prediction error is influenced by increased algorithmic trading These hypotheses lead to the regressions below of which it is expected that analyst prediction error has indeed increased in the period where automation has taken place. It seems unlikely that analysts can predict the direction of future stock prices as the analysts would have to be able to execute transactions faster than the algorithms.>

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