Fund performance is often thought to be the acid test of fund management, and in the institutional context, accurate measurement is a necessity. For that purpose, institutions measure the performance of each fund (and usually for internal purposes components of each fund) under their management, and performance is also measured by external firms that specialize in performance measurement. The leading performance measurement firms (e.g.
Russell Investment Group in the US or BI-SAM in Europe) compile aggregate industry data, e.g., showing how funds in general performed against given
performance indices and peer groups over various periods. In a typical case (let us say an
equity fund), the calculation would be made (as far as the client is concerned) every quarter and would show a percentage change compared with the prior quarter (e.g., +4.6% total return in US dollars). This figure would be compared with other similar funds managed within the institution (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. 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). One effective solution to this problem is to include a minimum evaluation period in the investment management agreement, whereby the minimum evaluation period equals the investment manager's investment horizon. 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.
Risk-adjusted performance measurement 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 returns. 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 over above 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 about 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. 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 the skill and not luck). Portfolio returns 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 a three-factor model to describe portfolio normal returns (
Fama–French three-factor model). Carhart (1997) proposed adding 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. However, certain research indicates that internet data may not necessarily enhance the precision of predictive models. ==Education or certification==