Investments - Lecture n°11

1. Investments - Lecture n°11 • Part 4 : Active Portfolio Management (10 hrs) • Case study 2 : manage your own portfolio - requirements • 4.1. Active equity portfolio management (17/11) • 4.2. Value-at-Risk & Asset allocation : Conference by Dr. Frédéric Flament, Dexia BIL, Luxembourg (24/11) • 4.3. Performance analysis of investment portfolios : Conference by Prof. Georges Hübner, Ulg (1/12) • 4.4. Behavioural finance and portfolio management (8/12) • 4.5. Active Credit Portfolio Management : Conference by Mr. Bruno Raüis, ING Bank, Brussels.

2. Performance - Summary • Key performances measures • 1. Averages • Arithmetic Average : for future expected performance • Geometric Average : for past performance • Example : +10% in year 1 and –10% in year 2 • => arithmetic average = 0 • geometric average =

3. Performance - Summary • Key performances measures • 2. Ratios • Sharpe ratio : unit of excess return per unit of risk • good for the performance of an entire portfolio, • or to compare with other portfolios, • and to the market portfolio • the higher the better • if negative : value destruction (an investment in cash would have been better)

4. Performance - Summary • Key performances measures - Ratios • Information ratio : unit of excess return over the benchmark per unit of risk • same concept as the Sharpe ratio • used by practitioners, • highly dependant of the benchmark chosen, • the higher the better • if negative : a passive strategy would have been better

5. Performance - Summary • Key performances measures - Ratios • Treynor ratio : unit of excess return per unit of systematic risk • suited when a well diversified portfolio is mixed with others • allows to compare the performances of several managers of a well diversified portfolio • the higher the better • if negative : value destruction

6. Performance - Summary • Key performances measures - Ratios • Appraisal ratio : unit of Jensen’s alpha return per unit of non diversifiable risk • suited for parts of portfolios • suited for concentrated portfolios • measure the benefit-to-cost of a not well diversified portfolio • capture the benefits of an active stock selection • if negative : a passive strategy would have been better

7. Performance - Summary Key performances measures - Ratios Sharpe ratio of a composite portfolio : unit of excess return per unit of risk for a composite portfolio (C) made of an active part (P) and a passive part (M).

8. Performance - Summary Key performances measures - 3. Example : Which one is : Riskier ? Better diversified ? Outperforming the market? Better if a single fund? Better if part of a larger passive fund? Of an active fund?

9. Performance - Summary • 4. Performance attribution • Determination of excess return produced per active management decision taken. • A. Basis for comparison :Benchmarks (reference portfolios) • Key roles : • point of departure for assessing performance and risk • basis for establishing various management goals or limits: targeted outperformance rates, tracking errors limits, stop-loss procedures etc. • help clarify and communicate the investment objective of a fund.

10. Performance - Summary 4.b. Performance attribution procedure Should fit the investment process, following the top-down procedure of the decisions taken by the management. If return of the benchmark portfolio: where wBi is the weight of the asset class i in the benchmark portfolio, and rBi is the return of that asset class over the period. And return of the active portfolio: The difference in returns writes :

11. Performance - Summary 4.b. Performance attribution procedure It can be rewritten :

12. Performance - Summary • 5. Performance evaluation standards : AIMR norms • Returns must be total returns (income + capital gain). • Annual returns reported for all years individually, and longer periods. • Time-weighted average rates of return and geometric average linked returns. • Performance reported before fees. • Composite results reflect the record of the firm, not of individual managers. • Composite returns reported for at least a 10-year period. • Risk measures such as beta, duration, or standard deviation are encouraged.

13. Behavioral Finance • References : • Barberis, N, Thaler, R., 2002, “A Survey in Behavioral Finance”, NBER Working Paper No. W 9222 forthcoming in Handbook of Finance Economics, 2003. • Definition and scope • Applications : Stock Market - Cross-sections average returns - Funds - Corporate Finance • De Grauwe, P., Grimaldi, M., 2003. “Exchange Rates in a Behavioural Finance Framework, KUL working paper. • Investors are Chartists or Fundamentalists • Learning process, no Rational Expectations Paradigm • Model explains occurrences of bubbles and crashes • Nofsinger, J.R., 2002, The Psychology of Investing, Prentice Hall ed. • Catalogue of psychological biases of Investors

14. Behavioral Finance • Barberis, N, Thaler, R., 2002 • Behavioral Finance : Explanation of phenomena on Financial Markets, when agents are not fully rational • Two building blocks in the literature : • 1. Limits to arbitrage : difficult for rational traders (“fundamentalists”, EMH) to undo the dislocations caused by less rational traders • “no free lunch” (empirically observed)  “prices are right” (then prices may be wrong) • arbitrage may be costly to implement even when prices are wrong • empirical evidence tends to show limited arbitrage (persistent misalignment of prices and fundamentals)

15. Behavioral Finance • Barberis, N, Thaler, R., 2002 • Two building blocks in the literature : • 2. Psychology : catalogues the kinds of deviations from full rationality one might expect to see. • Experimental evidence : bias due to people’s beliefs and preferences. • Examples - beliefs : • Overconfidence (excessive risk-taking) • Optimism and Wishful Thinking • Confirmation bias (insufficient attention paid to new data) • Anchoring (too little review of prior ideas) • Memory biases (more recent events are more salient)

16. Behavioral Finance • 2. Psychology : • Examples - preferences : • Empirical evidence show a systematic violation of classical EU theory when choosing among risky gambles • Development of non-EU theories in the literature: • weighted-utility theory, disappointment aversion, rank-dependent utility theories, prospect theory • most applicable to financial issues : Prospect Theory. Utility function with a kink at the origin, with greater sensitivity to losses than to gains. • Includes the influence of framing, i.e. the way a problem is posed to the decision maker (Ex. Is a subsequent loss, a loss, or a reduction of a gain?), and narrow framing.

17. Behavioral Finance • Barberis, N, Thaler, R., 2002 • Example of Application: cross-section of average returns • Explanation of so-called “market anomalies” based on psychological and preferences biases described above : • The size premium : return of the smallest stock decile 0.74% per month higher than the largest stock • Long-term reversals : 8% average return higher for the “losers portfolio” than the winners, 3 years after their formation. • Momentum : 10% average annual return higher for winners, 6 months after portfolio formation. • Events studies on various corporate events and related investors reaction.