Before buying a stock, examine it in the context of the total portfolio.
After the collateral debt obligations crisis, all the other 'quants' have been lumped together as fast dancers who do amoral complex things. This is an understandable perception, but unfair.
Many, if not most, quants spend their time in programming algorithms that seek to arbitrage price differences between listed securities and their derivatives.
Unfortunately misconceptions about quantitative trading methods act as a psychological barrier against the adoption of all mathematical analysis. Some maths is essential in implementing basic hygiene in portfolio construction.
Everybody with a portfolio should know the alpha, beta, and cross correlations between the stocks held. Before buying a stock, examine it in the context of the total portfolio. That leads to the more rational allocation of weights.
This minimal investigation doesn't make somebody a wild-eyed quantitative trader. There's also strong evidence that somebody with this basic awareness will consistently score higher returns while accepting lower risks.
Every investor should have answers to the following questions. Does each stock he holds, move with, against, or independently of the market index (beta)? Does it have high independent returns (alpha)? How does it interact with other owned stocks (covariance)? How does the entire portfolio behave compared to the market index?
Learning these details about the stuff you own is easier than learning how to crunch balance sheets. There are free programs available to do the calculations and the theory isn't rocket science.
This statistical analysis is a logical extension of holding a portfolio, whether for trading purposes or long-term investment. It can be slotted on top of any investment or trading philosophy an investor or trader may espouse.
The theoretical foundations are not so difficult. Stocks that are negatively correlated to each other tend to cancel out some of the risks without reducing combined returns much. Stocks with high positive alphas generate good returns even when the market is down. Stocks with high betas exaggerate market movements.
The quant magic lies in that a judicious combination of stocks can yield higher returns for lower risk. Once you understand the theory, juggling to find the right weights to hold involves little more than fiddling with number ranges on pre-formatted spreadsheets.
Doing this doesn't impact an ability to pick stocks -- it improves the ability to mix and match optimally. The resulting difference in returns can be compared to the difference between the results produced by a skilled chef compared to an amateur cook using the same ingredients. A portfolio theorist will score more.
Why don't more investors or traders do this? The answers lie in the realms of psychology. What is interesting is that the basic statistical techniques seem to work especially well in India probably because they aren't commonly used.
The few investors or traders who do use Efficient Frontier calculations or simple Alpha-Beta analysis and the Capital Asset Pricing Model usually generate excess returns in India. For example, trading the right weights of the BankNifty, CNXIT and Nifty can beat the simple returns of any of these while reducing the risks of the combined position below that of each individual index.
Once an investor has the hang of basic portfolio analysis, he is also better-placed to analyse likely future risks and returns from a given mutual fund. As we've seen over the past few years, some funds give spectacular returns but only at the cost of high volatility. This usually happens when the fund is heavily overweight in the same type of stock. This flaw shows up as strong covariance between the components of the fund.
For example, during the real estate boom of 2005-07, funds that held a large component of real estate stocks outperformed. They also got slaughtered when the market cycle changed. Ditto for the IT-fanboys during the 1999-2001 period.
Again, if a fund has a beta close to 1, it may be pointless to buy into that instead of into a cheaper index fund. A negative or low beta fund could be a good hedge when the market cycle is bearish.
A trader or investor who does bother to adopt these methods will not necessarily beat the gunslingers during bull markets when results can be distorted by one multi-bagger. But he will consistently outperform during the phases when markets are down.
