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Nature of Liabilities

admin — Sun, 09 Oct 2011 16:41:38 +0000

The nature of an institutional investor’s liabilities will dictate the general investment strategy to pursue. Depository institutions, for example, seek to generate income by the spread between the return that they earn on their assets and the cost of their funds. Life insurance companies are in the spread business. Pension funds are not in the spread business, in that they themselves do not raise funds in the market. Certain types of pension funds seek to cover the cost of pension obligations at a minimum cost to the plan sponsor. Most investment companies face no explicit costs for the funds they acquire and must satisfy no specific liability obligations, the exception being target-term trusts.
A liability is a cash outlay that must be made at a specific time to satisfy the contractual terms of an obligation. An institutional investor is concerned with both the amount and timing of liabilities, because its assets must produce the cash flow to meet any payments it has promised to make in a timely way. In fact, liabilities are classified according to the degree of certainty of their amount and timing.
The descriptions of cash outlays as either known or uncertain are undoubtedly broad. When we refer to a cash outlay as being uncertain, we do not mean that it cannot be predicted. There are some liabilities where the “law of large numbers” makes it easier to predict the timing and/or amount of cash outlays. This work is typically done by actuaries, but even actuaries have difficulty predicting natural catastrophes such as floods and earthquakes.
In our description of each type of risk category, it is important to note that, just like assets, there are risks associated with liabilities. Some of these risks are affected by the same factors that affect asset risks.
A Type I liability is one for which both the amount and timing of the liabilities are known with certainty. An example would be when an institution knows that it must pay $8 million six months from now. Banks and thrifts know the amount that they are committed to pay (principal plus interest) on the maturity date of a fixed-rate certificate of deposit (CD), assuming that the depositor does not withdraw funds prior to the maturity date. Type I liabilities, however, are not limited to depository institutions. A product sold by life insurance companies is a guaranteed investment contract, popularly referred to as a GIC. The obligation of the life insurance company under this contract is that, for a sum of money (called a premium), it will guarantee an interest rate up to some specified maturity date.

INDUSTRY’S EVALUATION OF MODELING TOOLS

admin — Wed, 24 Aug 2011 15:51:20 +0000

A recent study by The Intertek Group tried to assess how the use of financial modeling in asset management had changed over the highly volatile period from 2000 to 2002. Participants in the study included 44 heads of asset management firms in Europe and North America; more than half were from the biggest firms in their home markets.
The study found that the role of quantitative methods in the investment decision-making process had increased at almost 75% of the firms while it had remained stable at about 15% of the firms; five reported that their process was already essentially quantitative. Demand pull and management push were among the reasons cited for the growing role of models. The head of risk management and product control at an international firm said, “There is genuinely a portfolio manager demand pull plus a top-down management push for a more systematic, robust process.” Many reported that fund managers have become more eager consumers of modeling. “Fund managers now perceive that they gain increased insights from the models,” the head of quantitative research at a large northern European firm commented.
In another finding, over one half of the participants evaluated that models had performed better in 2002 than two years ago; some 20% evaluated 2002 model performance to be stable with respect to the previous two years while another 20% considered that performance worsened. Performance was widely considered to be model-dependent. Among those that believed that model performance had improved, many attributed better performance to a better understanding of models and the modeling process at asset management firms. Some firms reported having in place a formal process in which management was systematically trained in modeling and mathematical methods.
The search for a silver bullet typical of the early days of “rocket science” in finance has passed; modeling is now widely perceived as an approximation, with the various models shedding different light on the same phenomena. Just under 60% of the participants in the 2002 study indicated having made significant changes to their modeling approach from 2000 to 2002; for many others, it was a question of continuously recalibrating and adapting the models to the changing environment.
Much of the recent attention on quantitative methods has been focused on risk management—a relatively new function at asset management firms. More than 80% of the firms participating in the Intertek study reported a significant evolution of the role of risk management from 2000 to 2002. Some of the trends revealed by the study included daily or real-time risk measurement and the splitting of the role of risk management into two separate functions, one a support function to the fund managers, the other a central control function reporting to top management.
In another area which is a measure of an increasingly systematic process, more than 60% of the firms in the 2002 study reported having formalized procedures for integrating quantitative and qualitative input, though half mentioned that the process had not gone very far and 30% reported no formalization at all. One way the integration is being handled is through management structures for decision-making. A source at a large player in the bond market said, “We have regularly scheduled meetings where views are expressed. There is a good combination of views and numbers crunched. The mix between quantitative and qualitative input will depend on the particular situation. For example, if models are showing a 4 or 5 standard deviation event, fundamental analysis would have to be very strong before overriding the models.”
Many firms have cast integration in a quantitative framework. The head of research at a large European firm said, “One year ago, the integration was totally fuzzy, but during the past year we have made the integration extremely rigorous. All managers now need to justify their statements and methods in a quantitative sense.” Some firms are prioritizing the inputs from various sources. A business manager at a Swiss firm said, “We have recently put in place a scoring framework which pulls together the gut feeling of the fund manager and the quantitative models. We will be taking this further. The objective is to more tightly link the various inputs, be they judgmental or model results.”
Some firms see the problem as one of model performance evaluation. “The integration process is becoming more and more institutionalized,” said the head of quantitative research at a big northern European firm. “Models are weighted in terms of their performance: if a model has not performed so well, its output is less influential than that of mod- els which have performed better.”
In some cases, it is the portfolio manager himself who assigns weights to the various inputs. A source at a large firm active in the bond markets said, “Portfolio managers weight the relative importance of quantitative and qualitative input in function of the security. The more complex the security, the greater the quantitative weighting; the more macro, long-term, the less the quantitative input counts: Models don’t really help here.” Other firms have a fixed percentage, such as 50/50, as corporate policy. Outside of quantitatively run funds, the feeling is that there is a weight limit in the range of 60–80% for quantitative input. “There will always be a technical and a tactical element,” said one source.
Virtually all firms reported a partial automation in the handling of qualitative information, with some 30% planning to add functionality over and above the filtering and search functionality now typically provided by the suppliers of analyst research, consensus data and news. About 25% of the participants said that they would further automate the handling of information in 2003. The automatic summarization and analysis of news and other information available electronically was the next step for several firms that had already largely automated the investment process.

Inputs Required

admin — Tue, 12 Jul 2011 15:42:31 +0000

To construct an efficient portfolio, the investor must be able to quantify risk and provide the necessary inputs. As will be explained in the next series of posts, there are three key inputs that are needed: future expected return (or simply expected return), variance of asset returns, and correlation (or covariance) of asset returns.
There are a wide range of approaches to obtain the expected return of assets. Investors can employ various analytical tools that will be discussed throughout this blog to derive the future expected return of an asset. For example, we will see that there are various asset pricing models that provide expected return estimates based on factors that historically have been found to systematically affect the return on all assets. Investors can use historical average returns as their estimate of future expected returns. Investors can modify historical average returns with their judgment of the future to obtain a future expected return. Another approach is for investors to simply use their intuition without any formal analysis to come up with the future expected return.
This input can be obtained for each asset by calculating the historical variance of asset returns. There are sophisticated time series statistical techniques that can be used to improve the estimated variance of asset returns. Some investors calculate the historical variance of asset returns and adjust them based on their intuition.
The covariance (or correlation) of returns is a measure of how the return of two assets vary together. Typically, investors use historical covariances of asset returns as an estimate of future covariances. But why is a covariance of asset returns needed? As will be explained, the covariance is important because the variance of a portfolio’s return depends on it and the key to diversification is the covariance of asset returns.

Measuring and Evaluating Performance

admin — Sun, 26 Jul 2009 15:45:14 +0000

The measurement and evaluation of investment performance is the last step in the investment management process. Actually, it is misleading to say that it is the last step since the investment management process is an ongoing process. This step involves measuring the performance of the portfolio and then evaluating that performance relative to some benchmark.
Although a portfolio manager may have performed better than a benchmark, this does not necessarily mean that the portfolio manager satisfied the client’s investment objective. For example, suppose that a financial institution established as its investment objective the maximization of portfolio return and allocated 75% of its funds to common stock and the balance to bonds. Suppose further that the manager responsible for the common stock portfolio realized a 1-year return that was 150 basis points greater than the benchmark. Assuming that the risk of the portfolio was similar to that of the benchmark, it would appear that the manager outperformed the benchmark. However, sup- pose that in spite of this performance, the financial institution cannot meet its liabilities. Then the failure was in establishing the investment objectives and setting policy, not the failure of the manager.