3
$\begingroup$

I am confused with the useage of the concept "Alpha Model" in quantative investment. According to Qian, Hua & Sorensen (2007), the first thing in the toolbox of quantative investment process is "an alpha model that forecasts excess return of stocks"(Page 5). And on page 81, the author mentioned that "an important component of any successful investment strategy is forecasting expected returns using alpha models".

So, what should an alpha model do? Predict excess return or predict expected return? Given the word "alpha" in the term "alpha model", shouldn't it predict the "alpha"(i.e., the risk-adjusted return, e.g., Jensen's Alpha) for each stock so that we can select stocks with high alphas to construct portfolio?

In addition, a common method to evaluate an "alpha factor" is to calculate the "information coefficient", which is usually defined as "the correlation between the forecasts and the eventual returns"(Grinold & Kahn, 2000; Qian, Hua & Sorensen, 2007). But an "alpha factor" itself is just a "number" calculated for each stock(see this post), not a return forecast, and some people seem to just use the correlation between the raw alpha factor values and stock returns as the "information coefficient". So I wonder which is right for the calculation of information coefficient. If we calculate it as "the correlation between the forecasts and the eventual returns", then how to get the "forecasts" given the raw alpha factor values?

Reference

Grinold & Kahn, 2000, Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk

Qian, Hua & Sorensen, 2007, Quantitative Equity Portfolio Management: Modern Techniques and Applications

$\endgroup$
2
$\begingroup$

Alpha Model:

First and foremost is an alpha model that forecasts excess return of stocks in Investment process. If return distribution is characterized by the expected return and the standard deviation, it is often the expected return that determines whether we buy or sell, overweight or underweight, and the standard deviation that determines the size of the portfolio allocations. It is easier to find random factors that represent non-compensated market risk than to find alpha factors that represents incremental rewards. The alpha model is often proprietary and highly guarded, reflecting creativity as well as superior systems. It is the most important differentiator within the investment firm.

$\endgroup$
  • $\begingroup$ Year, this is from Qian (2007) on page 5. But on page 81, the author mentioned "expected return". That's what I'm confused with. And I also want to konw whether an alpha model should predict the risk adjusted return (the so-called "alpha" in asset pricing theory). After all, there's a word "alpha" in the term "alpha model". $\endgroup$ – Gödel Aug 31 at 1:38
  • $\begingroup$ Please refer to page no. 82 last paragraph; For long-only Portfolios managed against a benchmark, alpha is the portfolio excess return over the benchmark; For Long-Short market neutral portfolio, alpha is the excess return over cash. Please do not get confuse with "Expected return" and "Excess Return", For Expected Return, we have "CAPM" in place. $\endgroup$ – Atul Agarawal Aug 31 at 2:05
  • $\begingroup$ Thanks! So, the goal of an alpha model is to predict the excess return. But why does the author call the excess return "alpha"? As far as I know, excess return is stock/portfolio return minus benchmark return (Strictly speaking, this is active return, because in asset pricing theory, excess return is stock return minus risk-free return. But let's just call it excess return here), and alpha is "stock return minus beta times market prerium return", i.e., R-beta * E(Rm-Rf). It is very confusing. Maybe it is just a difference between acdemic research and practice usage? $\endgroup$ – Gödel Aug 31 at 2:44
  • $\begingroup$ I can understand it's confusing at initial stage. I am working on Projects where we are going to implement all the modles discussed in Qian, Hua & Sorensen book, Actually Grinold & Kahn, 2000; Qian, Hua & Sorensen, 2007 both books go hand in hand. If you are satisfied with the above answer pls vote up and close the question. Thanks. $\endgroup$ – Atul Agarawal Aug 31 at 2:57
  • 2
    $\begingroup$ Hi: I wouldn't bother with the term "excess return" because it assumes that we know the expected return and, in practice, you usually don't. If you can forecast the actual return, you should be pretty happy with that and call it whatever you want !!!!! Excess return is an academic-CAPM type term so not necessary for forecasting returns. $\endgroup$ – mark leeds Aug 31 at 3:51

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.