Asset Management (2235.YR.014102.1)

General information

Type:	OPT	Curs:	1	Period:	S semester
ECTS Credits:	3 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 1	Jose Suarez-Lledo Grande	Economía, Finanzas y Contabilidad	ENG

This course builds upon concepts acquired on core finance modules like Financial Markets, Asset Pricing or Portfolio Theory. Examples of such concepts are bond pricing, stock valuation, financial ratios from corporate finance, risk - return dynamics, optimal portfolio choice (tangent portfolio, global minimum variance), portfolio performance metrics, market factors, market indices, ETFs, etc. As for technical knowledge, it is assumed that the students dominate standard notions of linear algebra, econometrics, statistics.

This course is primarily empirical and most of the work will be implemented on a computer via standard programming languages, like Python. Students can choose whichever they prefer although Python will be the language of instruction. However, the focus will not be on the coding language but on the development and mplementation of the concepts and ideas.

Several trends that have gained traction in the recent past will shape the near future in financial markets and the investment practice. The increasing automation of financial analysis, portfolio management or financial advice; the use of quantitative models to analyze and trade securities; the fast-growing availability of data, computational power, and analytical tools to extract valuable information, are relevant examples of such trends. Being well-equipped with the skills to successfully navigate such competitive markets will be of critical importance.

This is reflecting on the elective course offering of MBAs and Masters in Finance at top business schools around the globe. However, most such curricula present to a large degree mainstream standard content that still does not address some of these trending topics or do so with theoretically oriented courses.

This program aims to meet both the strong and increasing demand for skilled professionals in the investment arena, and the demand for quantitative and applied training in these subjects. The content provides common investment and portfolio management strategies employed at renowned quantitative funds and financial institutions.

Being aware that it is not feasible to cover all the fields that underlie the above mentioned trends, this course will focus on a subset of topics that are forecast to have a preeminent role in coming years: Robo-advisors, smart-indexing, and quantitative investment strategies.

The objective of the course is two-fold:
- Introduce the students to the main concepts in the three areas outlined previously: robo-advisors, smart-beta and quantitative investing/asset management. This will involve discussing the theoretical and mathematical underpinning whenever necessary
- Take those concepts to the data. This is very much an applied course where students will have the opportunity to cover all the steps from data gathering to model development to model/strategy implementation and back-testing.
The classwork for this course will heavily involve critical thinking, in order to understand and dissect the financial products that are presented to them, and creative thinking, in the form of idea generating discussions to design investment strategies that can be put to the test.

1. Market Trends

1.1. Robo-Advisors: global map of robo-advisors, Profile mapping, investing process automation, portfolio design, Fees, performance

1.2. Smart Indexing: Index construction, Main Smart-Beta product map (funds, ETFs), Fees, performance and key benchmark statistics

1.3. Hedge Funds & Mutual Funds: Methodology and Styles, Institutional Clients, Fees & performance

2. Quantitative Investment Strategies

2.1. Logistic Regressions with Macro & Market Factors i. Macroeconomic, Market and Fundamental factors ii. Factors from APT

2.2. Strategic & Tactical Asset Allocation i. From macroeconomic factors to ETF Portfolios ii. Factor Timing (Market Factors: Value, Momentum, Growth, Size, Low Volatility, ...) iii. Other Factors

2.3. Technical Analysis i. Momentum: MAs, MACD, Oscillators ii. Volatility: Bollinger Bands, ATR, Donchian & Keltner Channels.... iii. Market Sentiment/Volume: Elliot Waves, AD Line, McClellan iv. Momentum and Mean Reversion Strategies

2.4. Statistical Arbitrage i. Mean Reversion ii. Cointegration & Pairs Trading iii. Trees and PCA to identify pairs

2.5. Long-Short Portfolios i. Ranking of positions with market & fundamental factors ii. Position management & Rebalancing

2.6. Building a system/developing investment strategies i. Generating ideas, retrieving & filtering data ii. Model development iii. Backtesting: tecniques, biases iii. Implementation

2.7. Machine Learning in Finance i. Ridge and Lasso Regressions ii. Clustering algorithms iii. Tree-based models 1. Classifications vs regression trees 2. Random Forests, XGBoost iv. Neural Networks v. KNN and Hidden Markov Chains vi. Back-testing: cross-validations and time series

2.8. Risk Management i. Asset Allocation: Beyond the Efficient Frontier 1. Probabilistic weighting, Risk Parity, Hierarchical Risk Parity, Sortino ratio, Calmar ratio, The Kelly criterion ii. Drawdown & Liquidity Management: 1. Probabilistic Drawdown Management, Constant Proportion Portfolio Insurance (CPPI) 2. Order types: stop-loss, profit-taking, limit,...

As mentioned, sessions will include some theoretical discussion of concepts and ideas, but they will be predominantly empirical and applied. Work will be conducted as in a research team within a real investment institution. Topics or ideas will be presented and analysed/discussed/challenged with the rest of the class. Students will also work in groups to retrieve data and analyse concepts; or develop, implement and test their ideas. Class dynamics will also involve presenting their results and brainstorming for improvements on strategies/ideas. Sessions will also include some theoretical discussion of relevant notions, but they will be predominantly empirical and applied. Time will also be devoted to interpreting relevant market news and shaping that into investment ideas.

While the sections on Robo-Advisors and Smart Beta will likely not involve coding work, the section on quantitative investment strategies will. Students can choose what programming language/software to employ in their work, but special emphasis will be placed on Python as has become the dominant language in many areas of Finance. Student groups will have to submit their work on each assignment (one solution per group) and all group members will receive the same grade. Individual assessments will be based on student's interaction in class as well as the final project.

Non-Academic Reading List:

1. "The Quants: How a New Breed of Math Wizzes Conquered Wall Street and Nearly Destroyed it" by Scott Patterson
2. "Hedge Fund Market Wizards: How Winning Traders Win" by Jack Schwager
3. "The New Market Wizards: Conversations with America's Top Traders" by Jack Schwager
4. "Mastering the Market Cycle: Getting the Odds on Your Side" by Howard Marks
5. "The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes and Real Money" by Steven Drobny, Nouriel Roubini and Jared Diamond
6. "The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds" by Maneet Ahuja
7. "Confidence Game: How a Hedge Fund Manager Called Wall Street's Bluff" by Christine Richard
8. "Inside the Black Box: The Simple Truth about Quantitative Trading" by Rishi Narang
9. "Inside the House of Money: Top Hedge Fund Traders on Profiting in the Global Markets" by Steven Drobny and Niall Ferguson
10. "Keeping Up with the Quants: your Guide to Understanding and Using Analytics" by Thomas Davenport and Jinho Kim
11."The Man Who Solved the Markets: How Jim Simons Launched the Quant Revolution" by Gregory Zuckerman
12. "Unknown Market Wizards: The Best Traders you Have Ever Heard of" by Jack Schwager

Academic or Mainstreams References:

o Chan E.; Quantitative Trading: How to Build your Own Algorithmic Trading Business, Wiley
o Chan E.; Algorithmic Trading: Winning Strategies and their Rationale, Wiley
o Hastie, Tibshirani, Friedman; The Elements of Statistical Learning, Springer
o Sironi, P.; Modern Portfolio Management: from Markowitz to Probabilistic Scenario Optimization, Risk Books
o Fabozzi and Markowitz; The Theory and Practice of Investment Mangement, Wiley
o Ang, A.; Asset Management: a Systemic Approach to Factor Investing, Oxford
o Kula G., Raab M., Stahn S.; Beyond Smart Beta, Wiley Finance
o Meucci, A.; Risk and Asset Allocation, Springer Finance (Ch. 3, 4, 5)
o Litterman and the Quantitative Resources Group; Modern Investment Management: and equilibrium approach, Wiley Finance
o Grinold and Kahn; Active Portfolio Management, McGraw-Hill
o Cochrane, J.; Asset Pricing, Princeton University Press (Ch. 1, 2, 3, 4)
o Fabozzi, Focardi, Rachev, and Arshanapalli; The Basics of Financial Econometrics, Wiley
o Ilmanen; Expected Returns, Wiley
o Stewart S., Piros C., Heisler J.; Running Money, McGraw Hill
o Goldman Sachs Asset Management: Market Insights
o J.P. Morgan Asset Management: Insights
o BlackRock Financial Markets and Investment Division
o Barclays Financial Markets Analysis

Group	Teacher	Department
Year 1	Jose Suarez-Lledo Grande	Economía, Finanzas y Contabilidad