In my experience in the investment business, I have found three basic choices. You can trust Wall Street (financial media, investment banks, insurance companies, brokerage houses, mutual fund families), Main Street (your neighbors, friends, relatives, colleagues, etc.), or University Street (i.e. the academic community).
Wall Street claims to be filled with know-it-all investors and investment managers; they typically boast that they have the best research, best analysts, best funds, and best resources of all kinds. In this sense, Wall Street has an obvious agenda – they want your business, your trust, and your assets.
The risk management solutions of Wall Street institutions were designed to address pricing risk, latency/position risk (i.e. the risk due to increasing volume and volume spikes), market risk (including liquidity risk, funding risk, and the risk due to movements in rates and volatilities), counter-party risk, compliance risk, and operational risk.
From 1987 to 1992, a small group of Wall Street quants discovered a new way of managing risk to maximize investment success. This secret later on helped some quants become hedge fund billionaires, and it also facilitated a powerful modern global derivatives economy. In the wake of the 2008-2009 financial crisis, the structure, methodologies, and process of risk management have undergone significant change, which prompted investment management firms to renew their focus on more and better risk resources and risk managers across the board.
However, Wall Street institutions are riddled with conflicts of interest along with hidden and undecipherable fees. As a risk-averse investor, you want someone who can direct you to the products that will meet your needs in the best and most efficient way, but Wall Street want to sell you high-commission products. Your broker, the one you like and trust, may deserve your trust at the individual level, but as a matter of fact, he works for companies that were established to generate profits. In another word, when Wall Street gets its way, a little less of your money is working for you, and in the meantime, you are taking more risk without even really understanding it.
Main Street is of people whose egos want us to believe that they know it all, even though it is fairly easy to find the limits of their knowledge and skills. Main Street has less obvious agenda – our friends, neighbors, colleagues, and relatives probably do not ask for our assets; instead, these people share their investing success, their hot tips, and their newest insights. The trouble is, they are amateurs who can rarely show us the proof of their investment philosophy. It might be true that Main Street is a great source to find out about hotels, wine bars, and dry cleaners, but when it comes to investing and risk management practice, I don’t think it would work.
“An investment in knowledge pays the best interest.” — Benjamin Franklin
Compared with Wall Street and Main Street, University Street is probably the smartest and best educated group, so it has the most interesting agenda – one that does not actually involve investors. Professors and graduate students want their peers to realize how brilliant they are in terms of figuring out the intricacies of investing and risk management. They are only interested in what does work and what does not, and where the lies are in Wall Street’s marketing pitches or presentation books, which, in my opinion, makes them more trustworthy.
Here is a good example. Morningstar created Morningstar Style Box, a nine-square grid that provides a graphical representation of the investment style (value, growth, blend) of stocks and mutual funds. For stocks and stock funds, it classifies securities according to market capitalization (the vertical axis) and growth and value factors (the horizontal axis); for fixed income funds, the style box classifies them according to credit quality (the vertical axis) and sensitivity to changes in interest rates (the horizontal axis). This proprietary measure makes it easier for investors to put funds together into portfolios that take advantage of the valid research.
Think about another example. Two professors, Eugene Fama and Kenneth French, cast doubt on the validity of the Capital Asset Pricing Model (CAPM). They demonstrated three measurable factors which can explain more than 90 percent of the difference between the return of any equity portfolio and the overall market, namely, the size of the companies, the book-to-market ratio of the companies, and the volatility/beta of the stocks in the portfolio.
These are examples of how solid academic research (I call it University Street) can move the investing and risk management process from intuitive to scientific. However, on the negative side, unlike Wall Street, University Street are not regulated by government authorities, nor are they subject to the discipline of the market place. What they are concerned about is the opinion of their peers – peer review. Most academic papers are reviewed by experts in the subject matter before they are published.
Risk Measurement Systems – Returns-Based vs. Position-Based
Traditionally, risk has been measured from returns-based information, i.e., time series of historical returns of the portfolio. A returns-based risk measurement system is easy and cheap to implement, but it suffers from severe drawbacks. First, they offer no information for new instruments and markets. For instance, they are completely ineffective for emerging asset managers or funds that have short track records. In addition, most returns-based measures do not capture style drift.
Ideally, market risk should be measured with a position-based risk measurement system. This involves several steps:
- The risk manager must collect all the current positions in the portfolio and map them on the market risk factors via factor exposures.
- The risk manager must construct the statistical distribution of risk factors from market data.
- The risk manager must use the risk engine to derive the distribution of profits and losses on the portfolio.
Position-based risk systems can be technologically challenging to implement, and they require more resources to implement. Besides, position-based risk measurement systems are susceptible to errors and approximations in data and models, as they require modeling all positions and repricing instruments as a function of movements in the risk factors, which will inevitably lead to modeling risk.
Conventional risk measures also include factor exposure measures, portfolio exposure measures, statistical risk measures, and stress tests. I will discuss these approaches later.
Financial Fitness Forever by Paul A. Merriman 2012