The modern portfolio management process is not anymore a simple activity. It has developed into a quantitatively complex process that can be described using five steps:

Specification of investment objectives and constraints

Investment needs to be guided by a set of objectives. The main objectives taken into consideration by investors are capital appreciation, current income and safety of principal. The relative importance of each of these objectives needs to be determined. The main aspect that affects the objectives is risk. Some investors are risk takers while others try to reduce risk to the minimum level possible. Identification of constrains arising out of liquidity, time horizon, tax and special situations need to be addressed.

Choice of the asset mix

In investment management the most important decision is with respect to the asset mix decision. It is to do with the proportion of equity shares or shares of equity oriented mutual funds i.e. stocks and proportion of bonds in the portfolio. The combination on the number of stocks and bonds depends upon the risk tolerance of the investor. This step also involves which classes of asset investments will be placed and also determines which securities should be purchased in a particular class.

Formulation of portfolio strategy

After the stock – bond combination is chosen, it is important to formulate a suitable portfolio strategy. There are two types of portfolio strategies. The first is an active portfolio strategy which aims to earn greater risk adjusted returns depending on the market timing, sector rotation, security selection or a mix of these. The second strategy is the passive strategy which involves holding a well diversified portfolio and also maintaining a pre-decided level risk.

Selection of securities

Investors usually select stocks after a careful fundamental and technical analysis of the security they are interested in purchasing. In case of bonds credit ratings, liquidity, tax shelter, term of maturity and yield to maturity are factors that are considered. Portfolio Execution This step involves implementing the formulated portfolio strategy by buying or selling certain securities in specified amounts. This step is the one which actually affects investment results. Portfolio Revision Fluctuation in the prices of stocks and bonds lead to changes in the value of the portfolio and this calls for a rebalancing of the portfolio from time to time. This principally involves shifting from bonds to stocks or vice-versa. Sector rotation and security changes may also be needed.

Performance Evaluation

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.

Model Valuation and Pricing (MVP) takes the quantitative models and methods quantitaive analysts and determines their validity and correctness. The MVP group can be seen as a superset of the quantitative function in a pension fund, mutual fund, hedge fund or other financial institution, since such quantitative analysts must deal with internally and externally developed quantitative models.

in most firms, MVP quantitative analysts typically earn a fraction of quantitative analysts in other groups, with similar length of experience. This compensation structure is such that MVP groups struggle to attract and retain adequate quantitative analysts, and talented quantitative analysts often leave at the first opportunity. This gravely impacts corporate ability to manage model risk, or to ensure that the positions being held are correctly valued.

The Portfolio Rebalancing step involves implementing the formulated portfolio strategy by buying or selling certain securities in specified amounts. This step is the one which actually affects investment results. Portfolio Revision Fluctuation in the prices of stocks and bonds lead to changes in the value of the portfolio and this calls for a rebalancing of the portfolio from time to time. This principally involves shifting from bonds to stocks or vice-versa. Sector rotation and security changes may also be needed.

Algorithmic Quantitative Analysts (AQA), our highest paid quanttitative analysts, use methods taken from market micro structure, econometrics, time series analysis, but also game theory, machine learning and signal processing. Quantpool offers AQAs with extensive Algorithmic trading experience of statistical arbitrage, event arbitrage and merger arbitrage strategies. Some of our most successful ATQs modify hardware and Linux kernels to achieve ultra low latency, because the techniques are largely based upon speed of response.

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.

The importance of risk management has grown in importance in recent years, as the financial crisis exposed holes in the mechanisms. A core technique is value at risk (VaR), which in practice is backed up with various forms of stress testing and direct analysis of the positions and models used by traders.

The maybe greatest benefit of VaR lies in the imposition of a structured methodology for critically thinking about risk. Institutions that go through the process of implementing a VaR system are forced to confront their exposure to financial risks and to set up a proper risk management function. Thus the process of getting to VaR in place may be as important as the VaR metric itself.

Publishing a daily VaR metric in time and with adequate statistical properties holds most parts of a financial institution to a high objective standard, although maybe focusing too much on expected market risk and too little on expected return. Reported positions and activities stand out, as do model, price, data feed and other technical inaccuracies or late reports. Anything that affects profit and loss that is left out of other reports will show up either in inflated VaR or excessive VaR breaks. A risk-taking institution that does compute a daily VaR might escape a disaster, but an institution that cannot compute a daily VaR will probably not.

The second claimed benefit of VaR is that it separates risk into two regimes. Inside the VaR limit, conventional statistical methods are reliable. Relatively short-term and specific data can be used for analysis. Probability estimates are meaningful, because there are enough data to test them. In a sense, there is no true risk because you have a sum of many independent observations with a left bound on the outcome. A casino doesn't worry about whether red or black will come up on the next roulette spin. Risk managers encourage productive risk-taking in this regime, because there is little true cost. People tend to worry too much about these risks, because they happen frequently, and not enough about what might happen on the worst days.[19] Outside the VaR limit, all bets are off. Risk should be analyzed with stress testing based on long-term and broad market data.[20] Probability statements are no longer meaningful.[21] Knowing the distribution of losses beyond the VaR point is both impossible and useless. The risk manager should concentrate instead on making sure good plans are in place to limit the loss if possible, and to survive the loss if not.[1] One specific system uses three regimes.[22] 1.One to three times VaR are normal occurrences. You expect periodic VaR breaks. The loss distribution typically has fat tails, and you might get more than one break in a short period of time. Moreover, markets may be abnormal and trading may exacerbate losses, and you may take losses not measured in daily marks such as lawsuits, loss of employee morale and market confidence and impairment of brand names. So an institution that can't deal with three times VaR losses as routine events probably won't survive long enough to put a VaR system in place. 2.Three to ten times VaR is the range for stress testing. Institutions should be confident they have examined all the foreseeable events that will cause losses in this range, and are prepared to survive them. These events are too rare to estimate probabilities reliably, so risk/return calculations are useless. 3.Foreseeable events should not cause losses beyond ten times VaR. If they do they should be hedged or insured, or the business plan should be changed to avoid them, or VaR should be increased. It's hard to run a business if foreseeable losses are orders of magnitude larger than very large everyday losses. It's hard to plan for these events, because they are out of scale with daily experience. Of course there will be unforeseeable losses more than ten times VaR, but it's pointless to anticipate them, you can't know much about them and it results in needless worrying. Better to hope that the discipline of preparing for all foreseeable three-to-ten times VaR losses will improve chances for surviving the unforeseen and larger losses that inevitably occur. "A risk manager has two jobs: make people take more risk the 99% of the time it is safe to do so, and survive the other 1% of the time. VaR is the border." VaR risk measurement The VaR risk measure is a popular way to aggregate risk across an institution. Individual business units have risk measures such as duration for a fixed income portfolio or beta for an equity business. These cannot be combined in a meaningful way.[1] It is also difficult to aggregate results available at different times, such as positions marked in different time zones, or a high frequency trading desk with a business holding relatively illiquid positions. But since every business contributes to profit and loss in an additive fashion, and many financial businesses mark-to-market daily, it is natural to define firm-wide risk using the distribution of possible losses at a fixed point in the future. In risk measurement, VaR is usually reported alongside other risk metrics such as standard deviation, expected shortfall and “greeks” (partial derivatives of portfolio value with respect to market factors). VaR is a distribution-free metric, that is it does not depend on assumptions about the probability distribution of future gains and losses.[18] The probability level is chosen deep enough in the left tail of the loss distribution to be relevant for risk decisions, but not so deep as to be difficult to estimate with accuracy. VaR can be estimated either parametrically (for example, variance-covariance VaR or delta-gamma VaR) or nonparametrically (for examples, historical simulation VaR or resampled VaR).[4][6] Nonparametric methods of VaR estimation are discussed in Markovich [24] and Novak.

Modern portfolio theory approaches the main objectives in consideration by investors and portfolio managers quantitatively, they are capital appreciation, current income and safety of the principal, which must be specificated as explicit quantitative return and risk expectations and constraints.

One main consideration is risk, a complexity in itself because some investors are risk lovers while other investors are risk averse. Key problems include how to sustain above average performance, how to keep impatient investors during times of poor performance; how to keep successful fund managers which could be headhunted by competitors; the ability of few individuals relative firm-wide success, attributable to a single philosophy and internal discipline; analysts who generate above-average returns often become sufficiently wealthy that they avoid corporate employment in favor of managing their personal portfolios.

Identification of constrains arising out of liquidity, time horizon, tax and special situations need to be addressed. Choice of the asset mix In investment management the most important decision is with respect to the asset mix decision. It is to do with the proportion of equity shares or shares of equity oriented mutual funds i.e. stocks and proportion of bonds in the portfolio. The combination on the number of stocks and bonds depends upon the risk tolerance of the investor. This step also involves which classes of asset investments will be placed and also determines which securities should be purchased in a particular class. Formulation of portfolio strategy After the stock – bond combination is chosen, it is important to formulate a suitable portfolio strategy.

There are two types of portfolio strategies. The first is an active portfolio strategy which aims to earn greater risk adjusted returns depending on the market timing, sector rotation, security selection or a mix of these. The second strategy is the passive strategy which involves holding a well diversified portfolio and also maintaining a pre-decided level risk. Selection of securities Investors usually select stocks after a careful fundamental and technical analysis of the security they are interested in purchasing. In case of bonds credit ratings, liquidity, tax shelter, term of maturity and yield to maturity are factors that are considered.

An investment analyst has three areas of responsibility: determine the value of the current investment, create advice reports, and research new investments. They typically have a university degree in finance, accounting, or related field, and can find employment opportunities in investment firms, large banks, and pension funds. The role of investment analyst is expected to have a lower than average growth in the next five to ten years, as the number of investment opportunities decreases.

People who enjoy working with numbers, are interested in finance, and have an analytical thought process find this type of work rewarding. The primary function of an investment analyst is to analyze market activity and advise the firm which action will produce the best yield and minimize the risk of loss. The investment market trades in a wide range of financial instruments, including a mix of short- and long-term bonds.

The primary role of the investment analyst is to determine the value of a wide range of investment instruments available for purchase, terms and conditions, ability to meet internal requirements, and compliance with the overall focus of the firm. Companies that trade in investment instruments are almost always financial and investment firms. Although the rate of return on these instruments can be quite high, few companies become involved in this as a side line to their primary business function, as the risk is also very high.

Virtually all asset managers and hedge funds rely to quantitative methods. Asset Managers use Quantitative Investment Analysts extensively during the whole investment process, from Investment Startegy Backtesting, to Portfolio and Risk Management as well as Performance Evaluation. Somea sset management firms, such as AQR and Barclays, rely almost exclusively on quantitative strategies while others, such as Pimco, Blackrock or Citadel mix quantitative and fundamental methods.


Huanxiao Zhang
Project Manager
+86 13826586703
hzhang@quantpool.com


Wei Ni
Project Manager
+86 18922903932
nwei@quantpool.com