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Asset Management

Objectives

The course has two main objectives. The first one is to introduce students to the main concepts and methodologies of asset allocation, portfolio construction and portfolio evaluation. The second is to implement and test against data some of such methodologies, through lab sessions in Python. We will critically assess (i) mean-variance investing, for the short and long runs, and its limits; (ii) benefits and limits of diversification; (iii) linear factor models and factor investing (iv) machine learning methods in asset management (v) and ESG responsible investing. While no prior programming knowledge is required for the Lab sessions, I will provide links to tutorials which may help coding. During last week of the course, we will host a guest speaker from the asset management industry.

General characterization

Code

2214

Credits

3.5

Responsible teacher

Daniele D'arienzo

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

n/a 


Bibliography

Ang, Andrew. Asset management: A systematic approach to factor investing . Oxford University Press, 2014

Further readings: lectures slides reference supplementary and non mandatory readings, including academic papers and article in newspapers 


Teaching method

Theory parts will be teached using:

The lab sessions (to expose students to Python 3 using Google CoLab)

Interactions with students (are key)


Evaluation method

The assessment consists of two parts:

1. Individual Assignement (weight 30%)

2. Final closed book exam (weight 70%)

The final grade is the weighted average of 1. and 2. 


Subject matter

1. Asset Management industry: past and future. Mean-variance investing. Short- and long-term investment horizons. Rebalancing. Tactical asset allocation and volatility timing. Lab section 1: Introduction to Python; numpy; panda; importing, preparing and plotting financial data.

2. Introduction to the diversification pricinple: merits and limits. Lab section 2: mean-variance portfolio analysis. Shrinking methods.

3. Multi-factor models. Perfomance evaluation. Lab section 3: linear factor models and performance analysis.

4. Machine learning (ML) methods and Asset Management. Lab section 4: Python implementation of some ML methods discussed.

5. Responsbile investing: the ESG efficient-frontier. Lab Section 5: performance of ESG investing. Week 6. Mock exam. Guest lecture.