|
|
|
||
|
Compact course will be taught by Jakub Rojček on June 10-12, 2026, 9:30 - 12:20 h + 14-15:20 h - room 314, course materials can be found here: https://github.com/jakubrojcek/gaa_students
Course objectives Asset allocation is a set of tools and techniques used by any institution to allocate capital across asset classes to reach investment goals. Typical market participants engaging in asset allocation are asset and wealth managers, pension funds, endowments, family offices, central banks, insurance companies, and treasury departments of larger companies. This three-day course provides a light, discussion-based introduction to the topic of asset allocation from both a scientific and practical perspective. It synthesizes the most important academic and industry references across fields of portfolio optimization, asset pricing, and risk management on asset class level and always provides a link to practical real-world applications. The course is rooted in Modern portfolio theory and its subsequent developments until the present time from both theoretical and empirical perspective. First, we motivate the topic of asset allocation by highlighting the current practical industry applications. Afterwards, we briefly review the basic concepts of risk and return trade-off, and the resulting mean-variance optimization, but quickly move towards the current state of asset allocation models with expected returns modeled using building block approach, use of regimes to tackle uncertainty in estimates, and further robust optimization techniques along with inclusion of views into optimal portfolios. We also cover the topic of shorter-term tactical asset allocation rooted in global macroeconomics, and market signals, and include tactical portfolio construction. We conclude by discussing avenues of future research and practical applications. We put particular emphasis on hypothesis testing using a realistic walk-forward backtesting approach. In addition to lectures, coursework consists of practical coding sessions geared towards successful completion of group assignments. We build our research code bottom up and backtest many of the models on international data. In the group projects, students are expected to solve a practical asset allocation problem and write a short report. The course builds on in-class discussions and active participation. Students are expected to gain a broad overview of asset allocation topics, learn about practical considerations of predicting future returns and portfolio optimization, as well as gain practical coding experience. Last update: Čech František, PhDr., Ph.D. (09.06.2026)
|
|
||
|
Class participation (25%) Group project (75%) ● Deadline: July 15th, 2026 midnight CET. ● Students are required to analyze and tackle a project in groups of two and submit a report of maximum ten pages alongside working Python code. Sample data will be provided for each individual project. Students may choose from the following list of topics or submit and get approval of their own topic by the last lecture. ● Possible topics: a. Yale endowment portfolio investment office: Analyze Yale’s endowment portfolio and analyze possible scenarios for pandemic related long-term performance, based on a case study. b. UBS Risk parity portfolios: Create, backtest and report on a risk-parity allocation investing in bonds, equities, commodities and inflation protected treasuries by replicating the UBS paper. c. Free choice of topic from course material
Last update: Čech František, PhDr., Ph.D. (13.05.2026)
|
|
||
|
DAY 1 (depending on progress in class) 1. Introduction to asset allocation - Why invest? Reading required: Kinlaw et al (2021) chapters 1, 2, 3, 10; Fabozzi et al (2007) chapters 1, 14 2. Portfolio theory - What trade-offs do we face? Reading required: Fabozzi et al (2007) chapters 2, 3, 4, 5, 9; Kinlaw et al (2021) chapter 8, 12 3. Robust covariance estimation - How to model risk? Reading required: Fabozzi et al (2007) chapter 8 4. Backtesting multi-asset strategies (and Coding session 1) - How to evaluate a systematic multi-asset strategy? Reading required: López de Prado, M. (2018) chapters 10, 11, 12, 13, 14, 15 DAY 2 (depending on progress in class) 5. Long-term expected returns - How to forecast returns? Reading required: Barclays (2021) Capital Market Assumptions; Ilmanen (2012); Kinlaw et al (2021) chapter 13 6. Robust optimization - How to optimize robustly? Reading required: Fabozzi et al (2007) chapters 10, 11, 12; Kinlaw et al (2021) chapter 19; Scherer (2007); Rockafellar and Uryasev (2002) 7. Views on markets - What if there are more people at the table? Reading required: Fabozzi et al (2007) chapter 9 8. Coding session 2 DAY 3 (depending on progress in class) 9. Macroeconomic and market regimes - What is moving the markets in the moment? Reading required: Kinlaw et al (2021) chapters 14, 22 10. Tactical asset allocation and dynamic strategies - How to outperform strategic portfolios? Reading required: Kinlaw et al (2021) chapter 17; Ang and Bekaert (2002) 11. Coding session 3 References Books (required) Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A., & Focardi, S. M. (2007). Robust portfolio optimization and management. John Wiley & Sons. Kinlaw, W., Kritzman, M. P., & Turkington, D. (2021). Asset allocation: from theory to practice and beyond. John Wiley & Sons. Books (further reading) Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press. Grinold, R. C., & Kahn, R. N. (2000). Active portfolio management. Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards (Vol. 535). John Wiley & Sons. Ilmanen, A. (2012). Expected returns on major asset classes. CFA Institute Research Foundation, 1. Ilmanen, A. (2022). Investing Amid Low Expected Returns: Making the Most when Markets Offer the Least. John Wiley & Sons. Illmanen, A (2025). Understanding Returns Expectations Part 1-7. AQR Website. https://www.aqr.com/Insights/Research/White-Papers/How-Do-Investors-Form-Long-Run-Return-Expectations López de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons. López de Prado, M. (2023). Causal factor investing: can factor investing become scientific?. Cambridge University Press. Meucci, Attilio. Risk and asset allocation. Vol. 1. New York: Springer, 2005. Pardo, R. (2011). The evaluation and optimization of trading strategies. John Wiley & Sons. Pedersen, L. H. (2019). Efficiently inefficient: how smart money invests and market prices are determined. Princeton University Press. Siegel, J. J. (2021). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. McGraw-Hill Education. Articles (selected) Ang, A., & Bekaert, G. (2002). International asset allocation with regime shifts. The review of financial studies, 15(4), 1137-1187. AQR, 2017: “Capital Market Assumptions Technical Appendix: Equity Expected Return Methodology” AQR, Q1 2021: “Capital Market Assumptions for Major Asset Classes” Bacchetta, P., Benhima, K., & Renne, J. P. (2022). Understanding Swiss real interest rates in a financially globalized world. Swiss Journal of Economics and Statistics, 158(1), 16. Barberis, N., Jin, L. J., & Wang, B. (2021). Prospect theory and stock market anomalies. The Review of Financial Studies, 34(6), 2630–2717. Barclays, 2021: “Capital Market Assumptions” Bauer, M. D., & Rudebusch, G. D. (2020). Interest rates under falling stars. American Economic Review, 110(5), 1316-1354. Bryzgalova, S., Huang, J., & Julliard, C. (2026). Macro Strikes Back: Term Structure of Risk Premia. Working Paper. Campbell, J. Y., & Shiller, R. J. (2001). Valuation ratios and the long-run stock market outlook: An update. Cirulli, A., De Nard, G., Traut, J., & Walker, P. (2026). Low risk, high variability: Practical guide for portfolio construction. The Journal of Portfolio Management, 52 (6), 215 - 246 De Nard, G. (2022). Oops! I shrunk the sample covariance matrix again: Blockbuster meets shrinkage. Journal of Financial Econometrics, 20(4), 569-611. Engle, R. F., Ledoit, O., & Wolf, M. (2019). Large dynamic covariance matrices. Journal of Business & Economic Statistics, 37(2), 363-375. Ferreira, M. A., & Santa-Clara, P. (2011). Forecasting stock market returns: The sum of the parts is more than the whole. Journal of Financial Economics, 100(3), 514-537. Fung, W., & Hsieh, D. A. (2004). Hedge fund benchmarks: A risk-based approach. Financial Analysts Journal, 60(5), 65-80. Geltner, D. M. (1991). Smoothing in appraisal-based returns. The Journal of Real Estate Finance and Economics, 4, 327-345. Geltner, D. (1993). Estimating market values from appraised values without assuming an efficient market. Journal of Real Estate Research, 8(3), 325-345. Goodwin, T. H. (1998). The information ratio. Financial Analysts Journal, 54(4), 34-43. Grinold, R. C., Kroner, K., & Siegel, L. B. (2011). A Supply Model of the Equity Premium. Rethinking the Equity Risk Premium, 53-70. Harvey, C. R., Liu, Y., & Zhu, H. (2016). … and the cross-section of expected returns. The Review of financial studies, 29(1), 5-68. Holston, K., Laubach, T., & Williams, J. C. (2017). Measuring the natural rate of interest: International trends and determinants. Journal of International Economics, 108, S59-S75. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. Kaplan, S. N., & Schoar, A. (2005). Private equity performance: Returns, persistence, and capital flows. The journal of finance, 60(4), 1791-1823. Kim, J. H., Kim, W. C., & Fabozzi, F. J. (2014). Recent developments in robust portfolios with a worst-case approach. Journal of Optimization Theory and Applications, 161, 103-121. Knox, B., & Vissing-Jorgensen, A. (2022). A stock return decomposition using observables. Kuhn, D., Esfahani, P. M., Nguyen, V. A., & Shafieezadeh-Abadeh, S. (2019). Wasserstein distributionally robust optimization: Theory and applications in machine learning. In Operations research & management science in the age of analytics (pp. 130-166). Informs. Kritzman, M., Li, Y., Page, S., & Rigobon, R. (2010). Principal components as a measure of systemic risk. Available at SSRN 1582687. Ilmanen, A., Israel, R., Moskowitz, T. J., Thapar, A. K., & Lee, R. (2021). How do factor premia vary over time? A century of evidence. A Century of Evidence (February 18, 2021). Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of multivariate analysis, 88(2), 365-411. Ledoit, O., & Wolf, M. (2022). The power of (non-) linear shrinking: A review and guide to covariance matrix estimation. Journal of Financial Econometrics, 20(1), 187-218. López de Prado, M. (2016). Building diversified portfolios that outperform out of sample. The Journal of Portfolio Management, 42(4), 59-69. López de Prado, M., & Lewis, M. J. (2019). Detection of false investment strategies using unsupervised learning methods. Quantitative Finance, 19(9), 1555-1565. López de Prado, M., & Zoonekynd, V. (2026). Correcting the Factor Mirage: A Research Protocol for Causal Factor Investing. Journal of Portfolio Management, 52(3). Menkveld, A. J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., ... & Khomyn, M. K. (2024). Nonstandard errors. The Journal of Finance, 79(3), 2339-2390. Meucci, A. (2010). Fully flexible views: Theory and practice. arXiv preprint arXiv:1012.2848. Michaud, R. O., & Michaud, R. (2007). Estimation error and portfolio optimization: a resampling solution. Available at SSRN 2658657. Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of banking & finance, 26(7), 1443-1471. Rogoff, K. (1996). The purchasing power parity puzzle. Journal of Economic literature, 34(2), 647-668. Rousseeuw, P. J., & Driessen, K. V. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3), 212-223. Ross, S., 1976a. The arbitrage theory of capital asset pricing. Journal of Economic Theory 13, 341–60. Ross, S., 1976b. Risk, return and arbitrage. Risk Return in Finance ed. I. Friend and J. Bicksler, Cambridge, Mass.: Ballinger. Scherer, B. (2007). Can robust portfolio optimisation help to build better portfolios?. Journal of Asset Management, 7, 374-387. Stambaugh, R. F. (1997). Analyzing investments whose histories differ in length. Journal of Financial Economics, 45(3), 285-331. Straehl, P. U., & Ibbotson, R. G. (2017). The long-run drivers of stock returns: Total payouts and the real economy. Financial Analysts Journal, 73(3), 32-52. Sung, C. H. (2023). Invesco Investment Insights. Tütüncü, R. H., & Koenig, M. (2004). Robust asset allocation. Annals of Operations Research, 132, 157-187. Last update: Čech František, PhDr., Ph.D. (13.05.2026)
|
|
||
|
Python resources Students are expected to bring their own laptops to the coding sessions. Data and functional Jupyter notebooks will be distributed at the beginning of the first class and we’ll cover these during the coding sessions. Students are responsible for creating their own Python capable setup that they will be able to use throughout the course and during the group projects. Google Colab is the easiest setup and I will use this throughout the class: For advanced users, I recommend using an individual VS Code (IDE) distribution with uv package to manage virtual environments You will find the resources there helpful, this video should also help get you started AI coding: For portfolio optimization, we’ll try coding a few routines based on cvxpy and scipy packages ourselves, but we’ll also make use of already existing software. I encourage you to explore e.g. the very good pypfopt package Useful libraries for statistics and econometric purposes would be scipy and statsmodels https://www.statsmodels.org/stable/index.html Last update: Čech František, PhDr., Ph.D. (13.05.2026)
|