Risk Management

Course Objectives

This is course is designed as an advanced course on financial markets and investment management. It will cover the functioning of financial markets per se, portfolio analysis, diversification and investment processes. At the end of the module, students should feel comfortable with the terminology and some of the different methodologies used in portfolio selection. Moreover, the student should be able to construct and test the performance of optimal efficient portfolios.


SECTION 1: Introduction and overview of the financial system

  • The investment environments
  • Asset classes and financial instruments: fixed income, equity, and derivative securities
  • Statistical overview of the historical asset management industry

This section provides a general overview of the financial system and investment products common in the financial markets.

SECTION 2: Optimal portfolio selection

  • Introduction to the Markowitz paradigm: diversification, mean-variance analysis and the efficient frontier

This section covers the foundations of quantitative portfolio selection. In particular, it introduces the concept of portfolio diversification, which is the cornerstone of modern portfolio theory.

Workshop: Mean-variance optimal portfolios

The aim of this workshop is to implement a variety of mean-variance efficient portfolios using the real-life data of stocks traded on NYSE.

SECTION 3: Factor Investing

In this section, we will learn how we can use factor investing techniques to construct portfolios. Quantitative investment firms do not pick stocks. The quantitative investment industries make use of factor investing or, similarly, smart beta investing. This investment technique uses firm attributes in order to identify mispriced firms or sources for risk premium.

Workshop: Portfolio construction with factor investing techniques

In this workshop, we will exploit the six anomalies – size, value, momentum, investment, profitability, and low volatility – to improve the performance of the benchmark portfolio.

Homework: Beating the market with factor investing

Factor investing is a quantitative investment approach that exploits anomalies in the cross section of stock returns to systematically tilt the market portfolio towards stocks with good returns.[ For a detailed discussion on factor investing read “Asset Management: A Systematic Approach to Factor Investing” by Andrew Ang.] In this assignment, we are going to study whether factor investing can help investors overcome the challenges highlighted by “the 1/N paper” and beat the market even out of sample.

To do this, we will evaluate the in- and out-of-sample performance of two different portfolios that exploit the following six anomalies: size, value, momentum, investment, profitability, and low volatility. Specifically, we will consider the two portfolios obtained by adding to the market portfolio:

1.An equally weighted combination of six “factor portfolios” corresponding to the aforementioned six anomalies,
2.A mean-variance efficient combination of these six “factor portfolios”.

SECTION 4: Efficient market hypothesis, CAPM and beyond CAPM

  • Efficient Market Hypothesis and CAPM
  • Testing market efficiency and the CAPM model
  • The Fama French three factor model and the size and value factors
  • The Carhart four factor model and the momentum factor
  • The Fama French five factor model and the profitability and investment factors

SECTION 5: Empirical investigation of stock prices and macroeconomic time series

  • S&P 500 Index and interest rates
  • S&P 500 Index and macroeconomic variables
  • S&P 500 Index and financial uncertainty

Homework: Anomalies in Foreign Exchange Rates

The aim of this individual assignment is to explore the anomalies in the cross-section of exchange rates. Students will construct a set of risk factors documented by the existing literature and will investigate whether these factors help explain the currencies of developed and emerging economies. Further, students will study the portfolio benefits for the investors using risk factors when forming an optimal portfolio of currencies.

SECTION 6: Introduction to investment funds and fund performance evaluation, tokenization of assets and tokenized funds

  • Introduction to the fund industry
  • Case study: Dimensional Fund Advisors Case, 2002
  • Recent trends in the fund management industry: cryptocurrency funds

This section provides a general overview of indirect investment products common in the financial markets. During this lecture, we will discuss different types of traditional funds (open-end and close-end funds, hedge funds, exchange traded funds, sovereign wealth funds, university endowments) and recent trends in the fund industry associated with the tokenization of assets.

Workshop: Measuring skills of fund managers

Just over 20 years have passed since the publication of Mark Carhart’s landmark 1997 study on mutual funds. Its conclusion – that the data did “not support the existence of skilled or informed mutual fund portfolio managers” – was the capstone of an academic literature, which began with Michael Jensen in 1968, that formed the conventional wisdom that active management does not create value for investors.

The aim of this workshop is to assess the extent to which current research still supports the conventional wisdom in the environment of cryptocurrency fund managers. Specifically, we will look at the performance of the funds trading cryptos over the last three years and will try to address the following three questions:

  • Does the average fund underperform after fees?
  • Does the performance of the best funds persist?
  • Do fund managers provide skill in excess of costs?

SECTION 7: Advanced econometrics, machine learning and network analysis (optional)

  • Advanced regression methods (linear, logistic, GAMs, LASSO, ridge)
  • Machine Learning with Tree-Based Models
  • Machine Learning with Neural Networks
  • (Optional) Network Analysis

Course Assessment

  • Homework on Portfolio Construction (25%)
  • Homework on Risk Factors in Foreign Exchange Rates (25%)
  • Group Project on Machine Learning (50%)


Investments and Portfolio Management by Bodie, Kane, and Marcus, McGraw-Hill Irwin

Additional reading material

  • Dimson, E., Marsh, P., & Staunton, M. (2000). Risk and Return in the 20th and 21st Centuries. Business Strategy Review, 11(2), 1-18.
  • DeMiguel, V., Garlappi, L., & Uppal, R. (2007). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? The Review of Financial studies, 22(5), 1915-1953.
  • Kenneth French discusses his model
  • Ang, Andrew. Asset Management: A Systematic Approach to Factor Investing. Oxford University Press, 2014.
  • Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. the Journal of Finance, 47(2), 427-465.
  • Fama, E. F., & French, K. (2008). Mutual fund performance. Journal of Finance, 63(1), 389-416.
  • Fama, Eugene F. and Kenneth R. French (2010). Luck versus Skill in the Cross Section of Mutual Fund Returns, Journal of Finance, 65, 1915-1947.
  • Berk, Jonathan and Jules van Binsbergen (2015), Measuring Skill in the Mutual Fund Industry, Journal of Financial Economics, 118, 1-20.
  • Pastor, L., Stambaugh, R. F., & Taylor, L. A. (2015). Scale and skill in active management. Journal of Financial Economics, 116(1), 23-45.

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