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1. Introduction 介绍
这篇论文的目的是构建和评估一个投资组合，根据我们的客户的要求。用于股票选择的数据是5年每周返回到26一月2015从彭博社和评估处理的时间为26至一月和17，四月2015。本文从投资组合结构入手，运用相关的方法。然后，它描述了调整和评估的投资组合的性能。最后，论述了财务理论与投资组合结果的关系。The aim of this essay is to construct and evaluate a portfolio based on our client’s requirement. The data used for stock selection is 5-year weekly return to 26 January 2015 from Bloomberg and evaluation processed for period between 26 January and 17 April 2015. This essay starts with portfolio construction by using relevant methodologies. And then, it describes adjustments and evaluates the portfolio performance. Finally, it discusses the relationship between financial theories and portfolio results.
2. Portfolio Construction 投资组合管理与资产配置
2.1. Objective description 客观描述
2.2. Equities selection
The methodology applied to select equities is top-down approach. Precisely, this approach requires an investigation in the global and industrial condition before choosing stocks from markets. Countries with high GDP growth normally offer more growing stocks. Therefore, two emerging markets, China and India, with relatively high GDP growth have been added into portfolio apart from the major markets included in the S&P 1200 index. At sector level, the chosen sectors generally prospect a better sales and profit growth than the others. The final stage is to point stocks from countries and sectors that have been identified. Only those stocks attained higher P/E ratio and EPS growth than its competitors will be finally accepted.
2.3. Asset allocation
The fund has been distributed at two levels, country and equity. At country level, the methodology applied is the black-Litterman model (Table 1). According to the capitalisation of the nine markets, market weights and the implied market returns can be computed. Additionally, to incorporate the growth rate of each country, the forecasted market return has been calculated by multiplying the current market return for the GDP growth multiplier. Then analysis opinions were added to those markets with significant difference between forecasted return and implied return. Lastly, the actively managed portfolio weights were calculated by solver. At stock level, the fund is allocated to each stock based on the Markowitz efficiency theory. The weight is determined by maximizing the Sharpe ratio. Finally, to satisfy with the investor’s requirement on liquidation, the 3-month US T-Bill was incorporated. Depending on Campbell and Viceira (2001), the percentage of liquid asset can be determined by maximising the utility upon a known risk aversion level. Due to the outstanding Sharpe ratio of the portfolio, the client with a relatively low risk aversion will invest over 100% in the portfolio and short sell the riskless asset. But to fit the liquidity requirement, it has been decided to invest 10% into the T-bill.
3. Performance adjustment 性能调整
The performance of the portfolio has been assessed for the period from 26 January to 12 February 2015. As the result showed, the portfolio successfully outperformed the benchmark. Specifically, the return of portfolio has more than doubled than benchmark and both Sharpe ratio and Treynor ratio of portfolio are higher (Table 2). However, several poorly performed stocks came into light during the assessment. For instance, the health care company in America called Celgene incurred a sharp drop during the evaluating period. Based on Bloomberg, the current market return in Asia decreased slightly during that time which would adjust down the investor’s opinion. In contrast, the Amerisourcebergen in USA presented a strong upward trend. The Aeon in Japan also indicated a growth potential due to its recent expansion plan to satisfy the increasing global demand (Aeon, 2015). Consequently, the weights of those stocks were adjusted manually. Specifically, Celgene was reduced by 1%; Galaxy Entertainment was reduced by 5%; Aeonn and Amerisourcebergen were increased by 1% and 5% respectively.
4. Performance evaluation and attribution绩效评价与归因
4. 1 Performance evaluation
After adjusting the weights of stocks, the new portfolio has achieved an average daily return of 0.2118% compared to 0.1920% of the unadjusted portfolio and 0.0392% of the benchmark, during our evaluation period from 26th January to 17th April 2015. Portfolio daily returns have outperformed the benchmark most of the time (see Figure1). Taking into account the currency surprise risk as we diversify our investment in 9 countries, we have received a total periodic return of 0.470%, among which 0.487% is from price change of stocks (Table 3.1-4.2). The foreign exchange factor in fact deteriorated our performance by -0.018%, which indicates if investors not constrained by investing in derivatives, we can further enhance the performance by hedging. Additionally, regarding total risk, our adjusted portfolio has generated a Sharpe ratio of 0.297 compared to 0.255 of former portfolio and 0.046 of benchmark. If only considering systematic risk, the 0.0044 Treynor ratio of our new portfolio still outperform in comparison to 0.0040 (old portfolio) and 0.0003 (benchmark). Under Jensen’s alpha evaluation, active portfolio management of the new portfolio has increased the abnormal return from 0.0015 to 0.0017 and the benchmark alpha approximates zero. The M square of 0.0018 demonstrates that we have improved the additional return on top of the market premium by 0.03%. In addition, the excess return to the benchmark over the portfolio’s diversifiable risk (i.e. the Information Ratio) has grown by 9.68% from 0.4225 to 0.5193. Lastly,diving the sum of all positive excess returns to benchmark by the absolute value of those negative ones, the omega rise from 1.758 to 1.963. Our portfolio has been improved and outperformed the benchmark in terms of all evaluation methods (detailed data see Table 5.1-5.2).
4.2 Performance attribution
The superior performance of our adjusted portfolio demonstrated above results from our active portfolio management, of which we break down to ‘Asset Allocation’, ‘stock Selection’, ‘Interaction’ effects utilising Brinson and Fachler’s (1985) techniques. With consideration to currency exposure, we now further incorporate the ‘Currency Management’ factor as proposed by Ankrim and Hensel (1994). Asset allocation procedure add value by under-weighting cash and over-weighting equities, and both of the old and adjusted portfolio has contributed a return of 0.01569% (8% of performance as asset weighing unchanged) . As we have applied heavier weights on superior stocks in better performing industries after adjustment, stock selection procedure has generated a return of 0.09092% compared to 0.08102% of the former portfolio. The value added accounts for the largest portion, 46%, of overall performance. 37% of average daily return (0.07273%) has been generated from the interaction effect from the above two procedures. For currency management, we have employed the return from foreign exchange (-0.018%) since it is almost impossible to capture all currency movements in the market and unfair to compare such broad data with our portfolio. In
total, the adjusted active management has improved the value added from 0.143% to 0.161% (all data included in Table6.1-6.2).
5. Relation between results and financial theories 结果和金融理论的关系
According to the performance attribution, our portfolio have outperformed the benchmark resulting from superior asset allocation and stock selection and currency management. We will discuss these procedures with corresponding financial theories.
5.1 Theories on stocks selection
Our portfolio is based on the active management style, which relies on the premise that mispricing exist at any point in time. That is to say, the market is assumed to be weak-form or semi-strong form efficiency (Ball,1995). Therefore, our portfolio is constructed on the basis of looking for these mispriced securities. Most of active return comes from stock selection. Initially, we have employed Black-litterman model to determine the weighting for each country in portfolio construction. By incorporating our opinions (significant difference between forecasted return and implied return in each market) into the market-based optimal model, heavier weighting will put on the high growth market (Black and Litterman,1991). As a consequence, more active return will be acquired because of realization of analyst’s opinions. In the subsequent stock selection, we use Markowitz efficient frontier to maximize the sharp ratio of our portfolio, which tries to maximize expected return within certain degree of portfolio risk(Edwin and Martin,2011). In this way, it also contributes to the better performance of portfolio.
构建的投资组合是基于100万美元的负责人。客户的主要目标是实现低风险厌恶情绪的高回报。施加在投资的限制包括只对股权投资限制，流动性的要求，很长一段时间的视野。因此，投资组合是由全球成长股形成和部分基金将在无风险资产进行投资。此外，在全球范围选择股票可以最大限度地减少客户端绑定的分散风险。为了具有可比性，投资组合的目标将是超越这是由相同的权重标准普尔全球1200指数和美国3个月期国库券构成的基准。The constructed portfolio is based on a principal of USD 100 million. The main objective of the client is to attain a high return with low risk aversion. Constraints imposed on the investment include limitation on equity investment only, requirement of liquidity and a long time horizon. Therefore, portfolio is formed by global growth stocks and part of the fund will be invested in the risk free asset. Additionally, selecting stocks globally can minimize the diversifiable risk bound by the client. To be comparable, the portfolio will aim to outperform the benchmark which is constituted by equal weight of S&P Global 1200 index and the US 3-month Treasury Bills.
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