1. Forecasting

2. “Forecasting is very difficult, especially if it’s about the future.”
Niels Bohr

3. Introduction How do we move from signals to forecast returns?
How do we combine signals?
What is our intuition about the magnitude of our forecasts?
What “real world” issues must we take into account?

4. From signals to forecasts
Signals don’t even have the right units
Basic forecasting formula
Best linear unbiased estimator (BLUE)

5. Basic Forecasting of Active Returns
Replace r with d.
Assume that unconditional (consensus) expected active returns are zero, and remember that alphas are conditional expected active returns:

6. Forecasting Intuition
One asset, one signal, linear estimate:
Expectations:

7. Intuition, cont. Use regression result for coefficient, b
Assume that the unconditional expected active return is zero, and that alpha is the conditional expected active return:

8. Rule of Thumb
Remember that:
Hence:

9. Forecast controls for:
Skill
If IC=0, alpha=0
Expectations
If g=E{g}, alpha=0
Volatility
If two stocks have the same score, we expect the more volatile stock to rise more.

10. Portfolio Construction
Assume uncorrelated active returns. (This isn’t required,
it just facilitates intution.)

11. IC Intuition
Simple model: only care about being directionally correct on active return.
N Stocks
d = ±1
g = ±1

12. IC Intuition Roulette wheel Active equity management
fr=52.6%, hence IC=0.052
Active equity management
IC=0.05 is quite good
IC=0.10 is fantastic
IC=0.00 is average
Many investors significantly over-estimate available levels of skill.
“A good forecaster is not smarter than everyone else, he merely has his ignorance better organized.” Anon

13. IC Intuition, cont. What do these IC’s look like?
An IC=0.1 implies a regression R2 of 0.01
Let’s look at an example of a promising idea.

14. Information Coefficient = 8.7%

15. Modeling Alphas Binary Model Elements are ±1 variables Example:
Monthly active returns
Monthly forecast alphas

16. Building Alphas in the Binary Model
We can see that monthly stock volatility is 8%, monthly signal volatility is 4%, and expected signal value is 1.
What is the IC:
What is the Alpha:

17. Rule of Thumb Examples Stock Tip Stock volatility is 20%
Rule of thumb provides structure in classic unstructured situation

18. Rule of Thumb Examples Broker BUY/SELL recommendations
Assume overall IC=0.05
Note the substantial scaling to account for skill.

19. How do we handle multiple signals?
Go back to basic forecasting formula.

20. Multiple signals Substitute into basic forecasting formula
We need to know the IC of each signal, as well as the correlation of the signals.

21. Multiple Signals
What happens with two signals?

22. Multiple Signals Check limiting cases.
This becomes more interesting with 3 signals.
What happens to the overall IC?
Is this useful, or just lots of algebra?

23. Example

24. Rule of Thumb confronts Real World: Cross-sectional Scores
Our analysis so far implicitly focused on time-series analysis:
Active returns for 1 stock over time
Signal(s) for 1 stock over time
Score, z, is a time-series score; involving time-series expectations and standard deviations
But the typical active management problem involves multiple assets at one time, with managers looking to pick relative winners and losers.

25. Cross-sectional Scores: Example
Stocks in S&P 500
Calculate FY1 E/P for each stock
Calculate mean and standard deviation across stocks
Cross-sectional score:

26. Dilemna Do we “volatility adjust” these scores?
Is alpha proportional to the cross-sectional scores, or to the cross-sectional scores, times volatility?
Or, to put it more generally:
What is g?

27. Analysis
The rule of thumb provides alpha as a function of time-series scores.
We need to relate these two statistics.
Ignore mean signal, either time-series or cross-sectional.
Ignore signal correlations across stocks.

28. Two Cases Compare StdTS{g} across stocks Case 1: Same for each stock
Case 2: Proportional to stock volatility

29. Case 1

30. Case 2

31. Practical Experience
Anecdotal evidence: Case 2 is more prevalent than Case 1.
Case 1 mainly happens with 0/1 signals.
Empirically we do see the connection between volatility scaling and modeling the time-series signal standard deviations.

32. Summary
Forecasting analysis controls raw signals for expectations, skill, and volatility.
It also tells us how to combine signals.
Rule of thumb provides intuition, and structure in unstructured situations.
Skill level is low in stock selection.
Don’t confuse cross-sectional with time-series scores.