I'd like to talk about the performance evaluation. One of the main applications of financial theory is the actual reward of managers or replacing of manages for performance or underperformance. We've already developed the tools whereby we can go through and do a reasonable first cut at the performance evaluation problem. For example, one thing that we've looked at is something that's referred to as the sharp measure. I didn't call it this before, but let me to find the sharp measure as expected excess return on a security or portfolio divided by its volatility.
Now the sharp measure gives us a reward to risk ratio. However, let's be careful about what we refer to as risk. In the denominator of the sharp measure is the volatility of that particular asset or project. So in order for this to be risk, it must be the case of looking at projects that are mutually exclusive, that means you can either do one or the other, and we assume that you do not hold that diversified portfolio. So the sharp measure is only a valid measure of performance evaluation when we're talking about mutually exclusive situations. It's expected excess return divided by the volatility. The simple measured calculate and hear practitioners' talking about sharp measures all the time. Of course the major shortcoming with the sharp measure is that it looks at the volatility of the particular asset and refers to that as the risk. We could improve upon this particular measure by looking at a different type of performance evaluation measure and that's what I manna do next.
What we can do is to look at the performance evaluation in the context of an arithmetic model. So the arithmetic model delivers the data for the security or the portfolio and there's a risk preem that's associated with the data. We multiply the two together and we get the expected return on a project given the level of risk. Now there's a realized return. We compare the realized return to the actual return and we get a difference, which is been called the performance. If the realized return exceeds what is expected given the level of risk, then we say that this asset or this manager has performed better than expected given the level of risk. Sometimes we call this the alpha. And we always look for positive alpha and sometimes people lose and talking about alpha and will say, you know, I'm no alpha manager. Well, I think it's better to be known as a positive alpha manager. You want to achieve a return over and above what is expected given the level of risk. So we're always looking for alpha. And the alpha concept was first introduced by Michael Jensen in his dissertation, 1969. And Jensen studied the performance of a number of mutual funds, and he measured their alphas in the lecture note, there's original graph from Jenson's studies. And he noticed that the performance of these mutual fund managers was less than distinguished, that most of the outputs were negative.

这篇文章很抽象,我不是研究这方面的,可能有些专业用语翻的不准,下面是我翻译的:
我想要谈谈性能赋值[评估].金融理论的一种重要应用就是对做的好的经理要奖励,对做的不好的经理就要换人替代他.我们已经发现了一种工具,通过它我们可以对性能赋值[评估]问题做一个初步解释.比如说,我们通常认为的"严格测定"方法.我从前不这么称呼它,让我像我所预计的那样找出作为债券和证券波动的附带效果的"严格测定方法".现在这种"严格测定方法"使我们能够应对风险的一定几率.但是,我们对于所谓的风险还是要小心.假设"严格测定方法"以某项特殊财产的证券和某项项目工程债券为全部范围.为了使这具有风险,这个假设必须是在这两个项目相对立的情况下(一个升,另一个就降).这意味着你可以选择投资其中的一个.并且我们假设你不拥有其他别的债券.所以"严格测定方法"是在相互对立的情况下,唯一的测量性能赋值[评估]的方法.他是波动带来的可预见的附带效果.一直计算并听取实际操作者对"严格测定方法"的评论.当然"严格测定方法"的主要缺点是它只考虑特定资产的波动,并把这看做是风险.通过对比别的性能赋值[评估]方法,我们可以改善这种测量方法,这也是我下一步要做的.

我们可以在算术模式内来研究性能赋值[评估],算术模式传递了债券和证券的资料,并且关于这些资料在算术模式里可反映出风险的初步映象.我们将两者相乘,我们得到了可预见的在特定风险下在某项目上的附带效果.现在有了一个被计算出来的附带效果.我们把计算出来的附带效果和实际效果想比,发现他们不相同.我们把这称作性能.如果计算出来的效果在特定风险下超过了预计的效果,我们就说这种资产或这个经理在特定风险下比预计的要做的好.通常我们把这定为A级.我们经常寻找这样的A级人物,有时候人们赔钱了并与别人谈A级人物,我会说,你知道,我不是A级人物.我想最好知道一下A级人物是一个什么样,如果你想在特定的风险下获得比预期更大的回报.所以我们经常都在寻找这样的A级人物.A级概念第一次由Michael Jensen在1969年他的论文中提出.Jensen研究了彼此相互作用的几种基金的性能,并在他的演讲记录本中计算了他们到达A级的程度.它们最初的表格出自Jenson's 的研究.他指出这些相互作用的基金管理者的表现好坏很不容易区分,因为大多数效果都是消极的.

.