The assessment of the performance of the portfolio should be done from time to time. It helps the investor to realize if the portfolio return is in proportion with its risk exposure. Along with this it is also necessary to have a benchmark for comparison with other portfolios that have a similar risk exposure.
In an institutional context, accurate fund performance measurement is a necessity. The fund performance is usually measured both internally, with access to each component of each fund under management, and externally by firms that specialize in performance evaluation. The leading performance measurement firms, such as Frank Russell in the USA and BI-SAM in Europe, compile aggregate industry data that shows how funds in general performed against given indices and peer groups over various time periods.
External performance evaluation of for example an equity fund, is usually made quarterly and would show a percentage change compared with the prior quarter and the return for other similar funds managed by other institutions.
... for purposes of monitoring internal controls), with performance data for peer group funds, and with relevant indices (where available) or tailor-made performance benchmarks where appropriate. The specialist performance measurement firms calculate quartile and decile data and close attention would be paid to the (percentile) ranking of any fund.
Generally speaking, it is probably appropriate for an investment firm to persuade its clients to assess performance over longer periods (e.g., 3 to 5 years) to smooth out very short term fluctuations in performance and the influence of the business cycle. This can be difficult however and, industry wide, there is a serious preoccupation with short-term numbers and the effect on the relationship with clients (and resultant business risks for the institutions).
An enduring problem is whether to measure before-tax or after-tax performance. After-tax measurement represents the benefit to the investor, but investors' tax positions may vary. Before-tax measurement can be misleading, especially in regimens that tax realised capital gains (and not unrealised). It is thus possible that successful active managers (measured before tax) may produce miserable after-tax results. One possible solution is to report the after-tax position of some standard taxpayer.
Performance measurement should not be reduced to the evaluation of fund returns alone, but must also integrate other fund elements that would be of interest to investors, such as the measure of risk taken. Several other aspects are also part of performance measurement: evaluating if managers have succeeded in reaching their objective, i.e. if their return was sufficiently high to reward the risks taken; how they compare to their peers; and finally whether the portfolio management results were due to luck or the manager’s skill. The need to answer all these questions has led to the development of more sophisticated performance measures, many of which originate in modern portfolio theory. Modern portfolio theory established the quantitative link that exists between portfolio risk and return. The Capital Asset Pricing Model (CAPM) developed by Sharpe (1964) highlighted the notion of rewarding risk and produced the first performance indicators, be they risk-adjusted ratios (Sharpe ratio, information ratio) or differential returns compared to benchmarks (alphas). The Sharpe ratio is the simplest and best known performance measure. It measures the return of a portfolio in excess of the risk-free rate, compared to the total risk of the portfolio. This measure is said to be absolute, as it does not refer to any benchmark, avoiding drawbacks related to a poor choice of benchmark. Meanwhile, it does not allow the separation of the performance of the market in which the portfolio is invested from that of the manager. The information ratio is a more general form of the Sharpe ratio in which the risk-free asset is replaced by a benchmark portfolio. This measure is relative, as it evaluates portfolio performance in reference to a benchmark, making the result strongly dependent on this benchmark choice.
Portfolio alpha is obtained by measuring the difference between the return of the portfolio and that of a benchmark portfolio. This measure appears to be the only reliable performance measure to evaluate active management. In fact, we have to distinguish between normal returns, provided by the fair reward for portfolio exposure to different risks, and obtained through passive management, from abnormal performance (or outperformance) due to the manager’s skill (or luck), whether through market timing, stock picking, or good fortune. The first component is related to allocation and style investment choices, which may not be under the sole control of the manager, and depends on the economic context, while the second component is an evaluation of the success of the manager’s decisions. Only the latter, measured by alpha, allows the evaluation of the manager’s true performance (but then, only if you assume that any outperformance is due to skill and not luck).
Portfolio return may be evaluated using factor models. The first model, proposed by Jensen (1968), relies on the CAPM and explains portfolio returns with the market index as the only factor. It quickly becomes clear, however, that one factor is not enough to explain the returns very well and that other factors have to be considered. Multi-factor models were developed as an alternative to the CAPM, allowing a better description of portfolio risks and a more accurate evaluation of a portfolio's performance. For example, Fama and French (1993) have highlighted two important factors that characterize a company's risk in addition to market risk. These factors are the book-to-market ratio and the company's size as measured by its market capitalization. Fama and French therefore proposed three-factor model to describe portfolio normal returns (Fama-French three-factor model). Carhart (1997) proposed to add momentum as a fourth factor to allow the short-term persistence of returns to be taken into account. Also of interest for performance measurement is Sharpe’s (1992) style analysis model, in which factors are style indices. This model allows a custom benchmark for each portfolio to be developed, using the linear combination of style indices that best replicate portfolio style allocation, and leads to an accurate evaluation of portfolio alpha.