Gestion de portfolio

PORTFOLIO MANAGEMENT PROJECT

By

De Almeida, Amanda                7795228

Friesen, Miranda                 7614879

Joanisse, Gerri                        7724821

Maric, Luka                        6530881

Nguyen, Lilian                 7709553

(Team N12)

Submitted to Professor William F. Rentz

for the course

Portfolio Management

(ADM3352 N)

Telfer School of Management

University of Ottawa

March 31, 2017

TABLE OF CONTENTS

Executive Summary        3

Introduction        4

Methodology        4

Asset Allocation        5

Portfolio Analysis        5

20 Securities        9

30 Securities        10

40 Securities        12

Improvements        13

Conclusion        14

EXECUTIVE SUMMARY

To the investor, the money market presents a world of possibilities that is nearly too abundant to grasp. With so many investment options available, it is crucial to be able to evaluate performance and predict future market trends in order to build an efficient portfolio that will yields positive returns and long-term success.

Two key analyses can be derived from this decision. The first, which deals with the allocation of assets across a variety of classes, determines the type of securities to be selected. It requires the investor to study historical data and recent news and to define an appropriate mix between equity, bonds and other cash equivalents. The second, which uses an intricate model, determines the portfolio weights in which are invested each security that produces the highest risk premium for a given level of risk. These steps are absolutely essential for optimizing one’s portfolio.

The following reports examines this development in order to detect the extent to which portfolio characteristics such as its size and the distribution of assets impacts efficiency. In creating multiple portfolios of 20- 30- and 40- securities, we were able to track its Sharpe ratio and ascertain the effect of the diversification on overall performance.

Please note that all interim reports were submitted directly to the section email provided by Professor Rentz. We were made aware by the teaching assistant that he did not receive interim reports 3 and 4 but as per the direction of the professor, this indicates that they were delivered and that we can provide copies of the email if necessary.

INTRODUCTION

American entrepreneur Robert D. Arnold once said, “In investing, what is comfortable is rarely profitable.” In this statement, he is clearly implying that the only way to realize significant gains is to move past the boundaries of one’s comfort zone. But how does a person measure their level of ease and how can it be practiced in an environment as erratic as the market? These concerns are directly related to risk tolerance which varies immensely from one investor to the next. While some investors will do all that it takes to make a profit, others are a lot less bold. For those who are more conservative, risk is taken into extreme consideration when picking securities. It really comes down to how they can maximize their returns with the least probability of default. Asset allocation plays a massive role in making this judgment. In this report, we will analyze the effect of diversification and portfolio size on performance and the risk-to-reward ratio. Using historical equity, fixed-income and cash equivalent information released to the public by established companies and national governments, we have built 20- 30- and 40-security portfolios. They will be used to identify different levels of volatility and to provide guidance as to which portfolio is optimal based on the individual’s investment goals.  

METHODOLOGY

This study involves numerous processes which can be summarized in two parts: the investment decision and analysis.

The investment decision mainly has to do with the assessment of assets. Given the project framework and the guidelines within which we were expected to work, discussions as to the type and quantity of securities to be included were very limited. Our team selected a U.S. treasury bill which represented the risk-free asset, as well as an exchange-traded fund. We then proceeded to identify portfolio characteristics in order to determine securities across broad asset classes. As the size of the portfolio increased, previously retrieved data was re-evaluated which led to new assumptions.      

The analysis is more complex because it deals with a series of technical operations. The Bloomberg terminal, that is the Excel Template Library (XLTP Historical Studies for multiple securities) and various screening functions, was the primary tool used to conduct this section. Once our team exported the historical total returns for each security in the portfolio, the data was organized in a table. To find the optimal risky portfolio, we first had to identify the risk-return combinations available from the set of risky assets. Also referred to as the minimum-variance frontier of risky assets, we illustrated the lowest-possible variance that can be attained for a given portfolio’s expected return (Bodie et al. , 2014). It was calculated using expected returns, variances and covariances and charted with standard deviation as its argument. Essentially, we selected desired risk premiums and ran Solver reports that corresponded with the constraints we had set. Next, we had to identify the optimal portfolio of risky assets by finding the portfolio that resulted in the steepest CAL (Bodie et al. , 2014). This part of the optimization model involved the risk-free asset and was determined in a way that is similar to the previous step. It was chosen based on which portfolio yielded the highest Sharpe ratio. Finally, we computed the points for the entire CAL line and superimposed its chart onto that of the efficient frontier.

...