22-08-2018, 11:41 AM
Complementando o Africano e o Cabeça de Vento (aka Nicolau Gurgel 
): leia sobre o viés da sobrevivência (ou suvirvorship bias: In business, finance and economics[edit]
In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies which were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 90% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed.[citation needed]
In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding).[1] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:
"Bias is defined as average α for surviving funds minus average α for all funds"
(Where α is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance).
Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500 would be counted as if they were in the index during that growth period, which they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index that was later removed and would not be counted in the results. Using the actual membership of the index and applying entry and exit dates to gain the appropriate return during inclusion in the index would allow for a bias-free output.
Michael Shermer in Scientific American[2] and Larry Smith of the University of Waterloo[3] have described how advice about commercial success distorts perceptions of it by ignoring all of the businesses and college dropouts that failed.[4] Journalist and author David McRaney observes that the "advice business is a monopoly run by survivors. When something becomes a non-survivor, it is either completely eliminated, or whatever voice it has is muted to zero".[5]
In his book The Black Swan, financial writer Nassim Taleb called the data obscured by survivorship bias "silent evidence. fonte wikipedia inglesa)
AIn business, finance and economics[edit]
In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies which were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 90% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed.[citation needed]
In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding).[1] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:
"Bias is defined as average α for surviving funds minus average α for all funds"
(Where α is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance).
Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500 would be counted as if they were in the index during that growth period, which they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index that was later removed and would not be counted in the results. Using the actual membership of the index and applying entry and exit dates to gain the appropriate return during inclusion in the index would allow for a bias-free output.
Michael Shermer in Scientific American[2] and Larry Smith of the University of Waterloo[3] have described how advice about commercial success distorts perceptions of it by ignoring all of the businesses and college dropouts that failed.[4] Journalist and author David McRaney observes that the "advice business is a monopoly run by survivors. When something becomes a non-survivor, it is either completely eliminated, or whatever voice it has is muted to zero".[5]
In his book The Black Swan, financial writer Nassim Taleb called the data obscured by survivorship bias "silent evidence.
Sabem qual livro explica isso muito bem?. No caso do livro ele desmonta o erro de achar que prisões com condições brutais fortalecem os prisioneiros, a hormese é válida até certo ponto de stress, nesse caso é só um viés cognitivo mesmo. O Ogro tem razão de tomar cuidado...
): leia sobre o viés da sobrevivência (ou suvirvorship bias: In business, finance and economics[edit]In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies which were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 90% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed.[citation needed]
In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding).[1] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:
"Bias is defined as average α for surviving funds minus average α for all funds"
(Where α is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance).
Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500 would be counted as if they were in the index during that growth period, which they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index that was later removed and would not be counted in the results. Using the actual membership of the index and applying entry and exit dates to gain the appropriate return during inclusion in the index would allow for a bias-free output.
Michael Shermer in Scientific American[2] and Larry Smith of the University of Waterloo[3] have described how advice about commercial success distorts perceptions of it by ignoring all of the businesses and college dropouts that failed.[4] Journalist and author David McRaney observes that the "advice business is a monopoly run by survivors. When something becomes a non-survivor, it is either completely eliminated, or whatever voice it has is muted to zero".[5]
In his book The Black Swan, financial writer Nassim Taleb called the data obscured by survivorship bias "silent evidence. fonte wikipedia inglesa)
AIn business, finance and economics[edit]
In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies which were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 90% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed.[citation needed]
In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding).[1] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:
"Bias is defined as average α for surviving funds minus average α for all funds"
(Where α is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance).
Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500 would be counted as if they were in the index during that growth period, which they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index that was later removed and would not be counted in the results. Using the actual membership of the index and applying entry and exit dates to gain the appropriate return during inclusion in the index would allow for a bias-free output.
Michael Shermer in Scientific American[2] and Larry Smith of the University of Waterloo[3] have described how advice about commercial success distorts perceptions of it by ignoring all of the businesses and college dropouts that failed.[4] Journalist and author David McRaney observes that the "advice business is a monopoly run by survivors. When something becomes a non-survivor, it is either completely eliminated, or whatever voice it has is muted to zero".[5]
In his book The Black Swan, financial writer Nassim Taleb called the data obscured by survivorship bias "silent evidence.
Sabem qual livro explica isso muito bem?
Spoiler Revelar
- Sem a visão de um objetivo um homem não pode gerir a sua própria vida, e muito menos a vida dos outros.
