NO.PZ202206140600000202
问题如下：
Chasing Alpha Research Case Scenario
Ben McNeil works as a senior manager at Chasing Alpha Research (CAR), a boutique investment house that specializes in managing portfolios for endowment funds. For the past year, CAR has been developing a machine learning (ML) algorithm that leverages frequently updated internal data (e.g., security weights, trades, and returns) and external data sources to construct individual stock portfolios within a pre-determined sector allocation range (–5% to +5% of benchmark). The goal of the portfolio is to outperform the benchmark over a 12-month period, and McNeil is reviewing the performance results to evaluate the effectiveness of the big data strategy. Attribution results for the portfolio are provided in Exhibit 1.
Exhibit 1.
Attribution Results of the ML Tool-Based Portfolio Return Using the Brinson Model
McNeil considers which appraisal method should be used to evaluate the effectiveness of the ML tool. He selects a portfolio constructed by the ML tool based on the investment mandate provided by one of CAR’s clients with the following characteristics: moderate to high risk tolerance and a preference for a short-term return that is 1.5% above the risk-free rate.
In discussing the portfolio’s performance with a colleague, the following statements are made:
Statement 1：The excess return of the portfolio is almost entirely driven by the selection and interaction performance of the financial services sector.
Statement 2：The decision to underweight the health care sector was not beneficial.
Statement 3：The decision to underweight the consumer goods sector was beneficial given the net contribution of 0.41% to the excess return.
In reviewing the overall technology sector return, McNeil realized that a large portion of the return was driven by a decision to sell an equivalent dollar amount of Gamma Technology Inc. and buy Epsilon Blockchain Co., which outperformed the market. Without this trade, the portfolio’s technology sector return would have only been 12.50%. He decides to calculate the associated selection and interaction measure had that trade not occurred.
Question
Based on the client’s investment mandate, the most appropriate appraisal measure for McNeil to use is the:
选项：
A.Sortino ratio. B.Treynor ratio. C.information ratio.解释：
SolutionA is correct. Given that the fund mandate requirement is for a short-term return in excess of the risk-free rate, the Sortino ratio is a more appropriate measure because it penalizes returns below a specific return—in this case, 1.5% above the risk-free rate.
B is incorrect. The Treynor ratio penalizes returns below the risk-free rate. It will not measure the fund’s ability to meet the requirement of a short-term return in excess of the risk-free rate.
C is incorrect. The information ratio evaluates the portfolio return relative to a benchmark. It will not measure the fund’s ability to meet the requirement of a short-term return in excess of the risk-free rate.
C选项虽然公式里没有risk-free,但就像题主问的，不能把r_B看为Rf+1.5%吗？本质上也就是一个benchmark而已，rf+1.5%不能当作benchmark吗？
