1. Sharpe Performance Index (SPI, Sharpe Ratio)

  • The ratio of the mean excess return on portfolio i to the standard deviation of the returns of portfolio i.
    SPI=E(Ri)−RfσiSPI=\frac{E(R_i)-R_f}{\sigma_i}SPI=σi​E(Ri​)−Rf​​
  • A measure of excess return per unit of risk (total risk), the higher is better.
    • SPI greater than the slope of the CML: superior performance to equilibrium return.
    • SPI below the slope of the CML: inferior performance to equilibrium return.

2. Treynor Performance Index (TPI)

  • The ratio of the mean excess return on portfolio i to the Beta(β\betaβ) of portfolio i.
    TPI=E(Ri)−RfβiTPI=\frac{E(Ri)-R_f}{\beta_i}TPI=βi​E(Ri)−Rf​​
  • A measure of excess return per unit of risk (systematic risk), the higher is better.
  • For a well-diversified portfolio, beta is widely accepted as an appropriate measure of risk.
  • According to CAPM, then
    TPI=E(Ri)−Rfβi=E(Rm)−RfTPI = \frac{E(R_i)-R_f}{\beta_i}=E(R_m)-R_fTPI=βi​E(Ri​)−Rf​​=E(Rm​)−Rf​

    • E(Rm)−RfE(R_m)-R_fE(Rm​)−Rf​ also called the alpha measure.
    • TPI is greater than the alpha measure is considered to have a positive alpha (indicting superior performance) and vice versa.
    • TPI>E(Rm)−RfTPI>E(R_m)-R_fTPI>E(Rm​)−Rf​, superior performance
    • TPI<E(Rm)−RfTPI<E(R_m)-R_fTPI<E(Rm​)−Rf​, inferior performance

3. Jensen’s Performance Index (JPI)

  • The difference between actual return and return required to compensate for systematic risk (CAPM), also called Jensen’s Alpha.
  • JPI is like TPI, as both measures assume investors hold well-diversified portfolios.
    αi=Ri−[Rf+β(Rm−Rf)]\alpha_i =R_i-[R_f+\beta(R_m-R_f)]αi​=Ri​−[Rf​+β(Rm​−Rf​)]

4. Sortino Ratio

  • The Sortino ratio (SR) is modification of SPI. Both ratios measure the risk-adjusted return of an asset or portfolio.
  • However, fi the primary focus is on downside risk, then SR is considered to be an improvement over SPI.
  • Sortino ratio is more appropriate for a case where returns are not symmetric.
    SR=Rp−T1N∑t=1Nmin(0,Rpt−T)2SR=\frac{R_p-T}{\sqrt{\frac{1}{N}\sum^N_{t=1}min(0,R_{pt}-T)^2}}SR=N1​∑t=1N​min(0,Rpt​−T)2​Rp​−T​
  • The denominator is the downside deviation, as measured by the standard deviation of negative returns.
  • TTT is the target or required rate of return fro the investment strategy, also known as MAR or minimum accepted rate of return.
  • TTT may be set to the risk free rate or another hurdle rate.

5. Tracking Error

  • The standard deviation of return difference between the portfolio and the benchmark.
    TE=σRp−RbTE=\sigma_{R_p-R_b}TE=σRp​−Rb​​

6. Information Ratio

  • The residual return of the managed portfolio relative to its benchmark divided by the tracking error.
    IR=E(Rp)−E(RB)σRp−RBIR=\frac{E(R_p)-E(R_B)}{\sigma_{R_p-R_B}}IR=σRp​−RB​​E(Rp​)−E(RB​)​

1.4.4 Performance Measures相关推荐

  1. 每日一佳——A Support Vector Method for Multivariate Performance Measures(Thorsten Joachims,ICML,2005)

    PDF 这篇Paper是2005年ICML的Best Paper. 题目意思:用于多变量性能度量的一个支持向量方法 摘要: This paper presents a Support Vector M ...

  2. 机器学习中常用的评价指标(Performance Measures)

    机器学习中常用的评价指标 混淆矩阵 混淆矩阵也称误差矩阵,是表示精度评价的一种标准格式,用n行n列的矩阵形式来表示.具体评价指标有总体精度.制图精度.用户精度等,这些精度指标从不同的侧面反映了图像分类 ...

  3. A Step By Step Guide to Tomcat Performance Monitoring【转】

    原文地址 https://stackify.com/tomcat-performance-monitoring/ Overview Monitoring the metrics and runtime ...

  4. 【 Notes 】MOBILE LOCALIZATON METHOD BASED ON MULTIDIMENSIONAL SIMILARITY ANALYSIS

    目录 ABSTRACT INTRODUCTION LINEAR TOA LOCALIZATION MULTIDIMENSIONAL SIMILARITY ANALYSIS SUBSPACE BASED ...

  5. 医学图像分类_全面梳理:图像配准综述

    内容导读: 1 定义 2 问题背景和应用 3 相关关键词 4 问题分类 4.1 基于问题特点的分类 4.2 根据算法本质的分类 5 图像配准通用流程 5.1 基于特征的图像配准通用流程 6 图像配准质 ...

  6. Ukbench图像数据集

    Ukbench图像数据集官网地址:http://www.vis.uky.edu/~stewe/ukbench/ Revised set! In the first set which went onl ...

  7. Reading Club week 3 prepare document

    Reading Club 文章目录 Reading Club Q (1) What are the commonalities and differences between the articles ...

  8. 今日arXiv精选 | TNNLS/ICCV/TIP/ACM MM/CIKM/WWW/ICME

     关于 #今日arXiv精选  这是「AI 学术前沿」旗下的一档栏目,编辑将每日从arXiv中精选高质量论文,推送给读者. Medical-VLBERT: Medical Visual Languag ...

  9. CVPR 2019 | PoolNet:基于池化技术的显著性目标检测

    作者丨文永亮 学校丨哈尔滨工业大学(深圳) 研究方向丨目标检测.GAN 研究动机 这是一篇发表于 CVPR 2019 的关于显著性目标检测的 paper,在 U 型结构的特征网络中,高层富含语义特征捕 ...

  10. [机器学习] 面试常见问题+解析汇总

    机器学习面试题的分类 The first really has to do with the algorithms and theory behind machine learning. You'll ...

最新文章

  1. xlrd.biffh.XLRDError: Excel xlsx file; not supported
  2. 利用java虚拟机的工具jmap分析java内存情况
  3. Vmo前端数据模型设计
  4. 浅谈android的selector,背景选择器
  5. linux如何卸载virtualbox,如何在Mac上卸载VirtualBox | MOS86
  6. halcon 旋转_HALCON高级篇:3D相机标定(3/3)
  7. Java2实用教程(第二版)程序代码——第十四章 Component类的常用方法
  8. 阶乘取模算法java_np问题(大数阶乘取模)
  9. 爬虫项目之爬取页面并按界面样式导入excel表格
  10. netcat,nmap常用例子
  11. dataframe先分组再画图
  12. 安装Python和Anaconda
  13. InfoGAN 翻译
  14. FPGA 主流芯片选型指导和命名规则(一)
  15. 类似win7系统泡泡屏保
  16. 计算机毕业设计ssm农贸市场摊位管理系统c22ux系统+程序+源码+lw+远程部署
  17. git报错git@gitlab.com: Permission denied
  18. python函数应用
  19. 台式电脑开机跳出来计算机,电脑开机出现DHCP怎么办?开机出现DHCP的解决办法...
  20. Android蓝牙UUID

热门文章

  1. Learning deep representations by mutual information estimation and maximization
  2. 计算机系统中文件命名的,你电脑上的文件命名规范吗
  3. 【强推】8个实用的Python程序
  4. FPGA蜂鸣器演奏音乐
  5. 时序分析 29 - 时序预测 - 格兰杰因果关系(下) python实践2
  6. 计算机共享网络热点,手把手教你在win7电脑中设置共享wifi热点
  7. 加性高斯白噪声 AWGN
  8. 洛谷p1330 封锁阳光大学-二分图染色
  9. 皮尔森 统计学相关性分析_统计学之三大相关性系数(pearson、spearman、kendall)...
  10. 无需重装系统,Windows Server 2019系统硬盘无损从MBR转换为GPT格式