NO.PZ202206140600000203
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.
QuestionIn the discussion between McNeil and his colleague about the portfolio performance shown in Exhibit 1, the most accurate statement is:
选项:
A.Statement 1. B.Statement 2. C.Statement 3.解释:
SolutionB is correct. The decision to underweight the health care sector was not beneficial, because the allocation contribution to the excess return is negative (–0.16%).
A is incorrect. Although the financial services sector performed well, it is the technology sector performance that provided the largest contribution to the excess return of the portfolio.
C is incorrect. The decision to underweight the consumer goods sector negatively affected the excess return (–0.15%), which is not a benefit.
选项b的health care sector因为benchmark表现不如组合平均,所以如果用BH模型来看,其实是beneficiary,但是也理解老师所讲解的这道题的理解思路为因为health care整体收益为正,所以低配就not beneficiary。
有一道类似的原版书课后题就是在问对不同国家allocation的效果所给组合带来的影响,就考虑了不同板块benchmark的收益与组合平均收益间的大小关系。
请问考试的时候应该怎么区分到底从哪个角度出发呢?硬要说的话就是a和c错的更彻底,b还有一个不同理解角度出发的回旋余地。。