Page 16 - MONECO Financial Training Catalogue
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PORTFOLIO CONSTRUCTION AND ASSET ALLOCATION
        DATES: October 20 – 22, 2025 • PRICES: € 1,950 In-class, € 1,463 Online • LOCATION: Prague and Online

        Attend this 3-day training seminar and learn about:
        •  Modern Portfolio Theory         Course Description
          Framework and going beyond       This training covers the latest trends in portfolio construction and asset allocation,
          MPT                              putting them in context of 50 years of modern portfolio theory and practice. The
                                           approach of this course is top-down and practical, providing guidance for practitioners
        •  The Approaches to Forecasting   how to take their asset allocation activities one step further and delivering valuable
          Expected Returns                 insights for potential practical implementation of more advanced quantitative
                                           techniques. The program is designed to accommodate plenum discussions and features
        •  Risk-Based Investment
                                           concept applications in six group exercises.
          Strategies and Estimating
          Risk                             Target Audience
        •  Portfolio Construction beyond   Chief investment offi cers, quantitative analysts, investment committee members, senior
          Mean and Variance                asset managers, investment analysts and portfolio managers.
        •  Tail Risk and Drawdown Risk     Materials
          Management                       Participants will receive the slides presented, spreadsheets containing example
                                           calculations for all models and concepts discussed and important papers in PDF
        •  Diversifi cation in a Non-
                                           format.
          Normal and Non-Linear World

         MONDAY, OCTOBER 20                  Scenario-based methods: Markov    Bayesian shrinkage estimators:
           00
         09 –09 10                             regime switching and practitioner’s   Ledoit-Wolf
           Welcome                             approaches                      Filtering noise in covariances:
           10
         09 –12 30                           Deriving returns from scores and   Random Matrix Theory
           Introduction                        ranks                           Modelling and tweaking correlations:
          •  Contemporary Challenges         Building allocations from scores   consistency issues & solutions,
          Financial Crisis of 2008        and ranks without optimizers      correlation scenarios and stress
          Low-Yield Environment         Incorporating active views: relative   testing
          Coronavirus Pandemic of 2020    forecasts
                                                                                30
                                             Bayesian methods: the Black/  12 –13 30
           Review of Modern Portfolio Theory   Litterman model and noise fi ltering     Lunch break
         (MPT) & Going Beyond MPT              using shrinkage methods (James   13 –17 00
                                                                                30
          •  From Mean-Variance Optimization to   Stein estimator)              Estimation Risk and Estimation Risk
          the CAPM: A quick summary of MPT                                    Management
          •  Applications of MPT            TUESDAY, OCTOBER 21                •  Estimation risk as risk in input
          Active Management            09 –12 30                           parameters
                                              00
          Liability-Aware Portfolio      Risk-Based Investment Strategies &    •  A scenario-based approach to
            Construction                    Estimating Risk                     estimation risk management
          Asset Class Investing         •  Risk-based approaches to investing:    •  The stochastic nature of effi cient
          “Passive” Investing           minimum variance, risk parity,     frontiers: confi dence bands, the
          Core-Satellite Approaches     risk budgeting, equal-weighting,   Resampled Effi cient Frontier™
          •  Critical Assessment and Constructive   maximum diversifi cation    •  Distortions in risk and return estimates:
          Take-Aways from MPT                •  Drivers of success of risk-based   the impact of liquidity and survival
          •  Framework for Going “Beyond MPT”  strategies                       biases, statistical unsmoothing
          The case for adaptive asset    •  ML (machine learning) & AI (artifi cial   approaches, evidence-based multiplier
            allocation in a dynamic world which   intelligence)                 approaches
            is hard to forecast              Waterfall allocations based on    •  Framework for an estimation-risk-aware
          Industry Trends: Factor Investing and   hierarchical clustering  mean-variance portfolio construction
            Smart Beta                       Hierarchical risk parity      process
                                             Some general comments on ML, AI    •  Robust portfolio construction: modelling
           30
               30
         12 –13                                in portfolio construction        uncertainty, regret minimization
           Lunch break                       •  Time-varying risk characteristics,
               00
           30
         13 –17                              empirical risk anomalies         WEDNESDAY, OCTOBER 22
           Expected Returns                  Autocorrelation and volatility   09 –12 30
                                                                                00
          •  The importance of expected return in   clustering, GARCH models    Portfolio Construction Beyond Mean
          portfolio construction and challenges:    The positive relationship between   and Variance
          estimation risk                      equity risk and return over time   •  Risk measurement for non-normal
          •  Do optimizers need expected returns?    The relative importance of   assets: LPM/UPM, VaR/CVaR,
          Spoiler alert: no, they don’t        volatilities and correlations    Drawdown risk
          •  Approaches to forecasting expected    •  Estimation of the covariance matrix   •  Higher Moments: interpretation, uses
          returns                            Sample covariances, EWMA and   and challenges
                                               GARCH estimators

               16                      Hybrid course – both classroom and online training available.
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