Many of us can think of leaders we have come to admire, be they historical figures, pillars of the industry we work in, or leaders we know personally. The leadership of individuals such as Abraham Lincoln and Margaret Thatcher has been studied and discussed repeatedly. However, you may have interacted with leaders you feel demonstrated equally competent leadership without ever having a book written about their approaches.
What makes great leaders great? Every leader is different, of course, but one area of commonality is the leadership philosophy that great leaders develop and practice. A leadership philosophy is basically an attitude held by leaders that acts as a guiding principle for their behavior. While formal theories on leadership continue to evolve over time, great leaders seem to adhere to an overarching philosophy that steers their actions.
What is your leadership philosophy? In this Assignment, you will explore what guides your own leadership.
To Prepare:
Identify two to three scholarly resources, in addition to this Moduleâs readings, that evaluate the impact of leadership behaviors in creating healthy work environments.
Reflect on the leadership behaviors presented in the three resources that you selected for review.
Reflect on your results of the CliftonStrengths Assessment, and consider how the results relate to your leadership traits.
Sample Solution
To shed mild on the automation technique that involves the shift from human traders to automated buying and selling systems, analyst predictions and their accuracy could be elaborated on when it comes to algorithmic trading. but, first our scope will awareness on how algorithmic trading is degree and the way dispersion has modified via algorithmic buying and selling. moreover, all impartial variables so as to be used in regressions, are standardized to facilitate economic interpretation. Standardization is finished with the aid of subtracting the corresponding time seriesâ suggest from the variables and dividing this deviation by the time collectionâ general deviation. by way of standardizing all unbiased variables in such style, the standardized regression coefficients will represent a preferred deviation alternate of the independent variables inside the established variable. for this reason, unbiased variable X is standardized such that: ãXâã_tj =(X_tj- μ(X))/( Ï(X)) Algorithmic trading measure Preparatory, a proxy has been advanced to degree the development of algorithmic trading over time in the available CRSP facts. To quantify algorithmic trading in a variable Hendershott, Jones, and Menkveld (2011) and Boehmer, Fong & WU (2015) use the daily variety of electronic messages from the TAQ database consistent with $100 of trading extent as proxy to measure algorithmic trading. it’s miles the most set up measure inside academic studies, but the TAQ database is not at this researchâs disposal and therefore an inferior but similar proxy is created. Inferiority lies within the reality that electronic messaging site visitors information is not available in CRSP. but, as volume information is available, the pleasant alternative measure would be a proxy that replaces the variety of electronic messages with a similar variable. Our statistics indicates that extent did no longer boom over time even as the number of trades did in a comparable way to the digital messages used in HJMâs proxy, making this a simplified however functioning substitute within our proxy for algorithmic buying and selling. moreover, algorithmic buying and selling is associated with stepped forward liquidity and an extended number of trades with smaller extent consistent with change (Hendershott et al., 2011). subsequently the new proxy for algorithmic buying and selling is calculated as the each day number of trades carried out for ticker j consistent with greenback trading extent of that day derived from the CRSP database. (2) ãAlgorithmic tradingã_tj =ãwide variety of tradesã_tj/ãvolumeã_tj For it being a much noisier proxy, it gives a completely comparable representation of the improvement of algorithmic trading through the years that became established by Glantz & Kissel (2013) which can be stated in discern 1 & 2. consequences of Algorithmic buying and selling on Dispersion it’s far assumed that algorithms have more similarities than its human counterparts and for that reason dispersion is predicted to lower with greater algorithmic buying and selling. As flash crashes are recognised to manifest with algorithmic buying and selling (Johnson et al., 2012) intense quick-time period dispersion might have expanded alternatively. but, considering that this take a look at is only able to use day by day statistics, flash crashes aren’t anticipated to steer the outcomes. subsequently, the hypotheses are formulated as: H0: Dispersion does not alternate with accelerated algorithmic buying and selling H1: Dispersion adjustments with elevated algorithmic trading Idiosyncratic or inventory-specific volatility is used to measure dispersion. Idiosyncratic threat may be calculated in numerous methods, the numerous measures but all provide comparable outcomes (Malkiel & Xu, 2003). furthermore, in keeping with Bello (2008) there are no good sized variations between the Cap>
To shed mild on the automation technique that involves the shift from human traders to automated buying and selling systems, analyst predictions and their accuracy could be elaborated on when it comes to algorithmic trading. but, first our scope will awareness on how algorithmic trading is degree and the way dispersion has modified via algorithmic buying and selling. moreover, all impartial variables so as to be used in regressions, are standardized to facilitate economic interpretation. Standardization is finished with the aid of subtracting the corresponding time seriesâ suggest from the variables and dividing this deviation by the time collectionâ general deviation. by way of standardizing all unbiased variables in such style, the standardized regression coefficients will represent a preferred deviation alternate of the independent variables inside the established variable. for this reason, unbiased variable X is standardized such that: ãXâã_tj =(X_tj- μ(X))/( Ï(X)) Algorithmic trading measure Preparatory, a proxy has been advanced to degree the development of algorithmic trading over time in the available CRSP facts. To quantify algorithmic trading in a variable Hendershott, Jones, and Menkveld (2011) and Boehmer, Fong & WU (2015) use the daily variety of electronic messages from the TAQ database consistent with $100 of trading extent as proxy to measure algorithmic trading. it’s miles the most set up measure inside academic studies, but the TAQ database is not at this researchâs disposal and therefore an inferior but similar proxy is created. Inferiority lies within the reality that electronic messaging site visitors information is not available in CRSP. but, as volume information is available, the pleasant alternative measure would be a proxy that replaces the variety of electronic messages with a similar variable. Our statistics indicates that extent did no longer boom over time even as the number of trades did in a comparable way to the digital messages used in HJMâs proxy, making this a simplified however functioning substitute within our proxy for algorithmic buying and selling. moreover, algorithmic buying and selling is associated with stepped forward liquidity and an extended number of trades with smaller extent consistent with change (Hendershott et al., 2011). subsequently the new proxy for algorithmic buying and selling is calculated as the each day number of trades carried out for ticker j consistent with greenback trading extent of that day derived from the CRSP database. (2) ãAlgorithmic tradingã_tj =ãwide variety of tradesã_tj/ãvolumeã_tj For it being a much noisier proxy, it gives a completely comparable representation of the improvement of algorithmic trading through the years that became established by Glantz & Kissel (2013) which can be stated in discern 1 & 2. consequences of Algorithmic buying and selling on Dispersion it’s far assumed that algorithms have more similarities than its human counterparts and for that reason dispersion is predicted to lower with greater algorithmic buying and selling. As flash crashes are recognised to manifest with algorithmic buying and selling (Johnson et al., 2012) intense quick-time period dispersion might have expanded alternatively. but, considering that this take a look at is only able to use day by day statistics, flash crashes aren’t anticipated to steer the outcomes. subsequently, the hypotheses are formulated as: H0: Dispersion does not alternate with accelerated algorithmic buying and selling H1: Dispersion adjustments with elevated algorithmic trading Idiosyncratic or inventory-specific volatility is used to measure dispersion. Idiosyncratic threat may be calculated in numerous methods, the numerous measures but all provide comparable outcomes (Malkiel & Xu, 2003). furthermore, in keeping with Bello (2008) there are no good sized variations between the Cap>
