1. Empiricism :From Newton to Taleb; & its Relevance for Quants! From Wikipedia: In thephilosophy of science ,empiricismemphasizes those aspects of scientific knowledge that are closely related to experience, especially as formed through deliberate experimental arrangements.It is a fundamental requirement of thescientific methodthat allhypothesesandtheoriesmust be tested againstobservationsof thenatural world , rather than resting solely ona priori reasoning ,intuition , orrevelation . Hence, science is considered to be methodologically empirical in nature. Allegiant Asset Management Company is a SEC-registered investment advisor and a subsidiary of National City Corporation.Wikipedia is a registered trademark of the Wikipedia Foundation, Inc. Steven P. Greiner, Ph.D.[email_address]

2. Newton & Einstein

Thus far I have explained the phenomena of the heavens and of our sea by the force of gravity, but I have not yet assigned a cause to gravity.I have not as yet been able to deduce from phenomena the reason for these properties of gravity.For whatever is not deduced from the phenomena must be called a hypothesis; and hypotheses whether metaphysical or physical, or based on occult qualities or mechanical, have no place in experimental philosophy

Issac Newton

Newton forgive me; you found the only way which in your age was just about possible for a person with the highest powers of thought and creativity.The concepts which you created are guiding our thinking in physics even today, although we now know that they will have to be replaced by others farther removed from the sphere of immediate experience, for we know that science cannot grow out of empiricism alone

Albert Einstein

3. Kelvin & Taleb

When you can measure what you are speaking about, and express it in numbers, you know something about it.But when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science

William Thompson Lord Kelvin

The relationship between the past and the future does not learn from the relationship between the past and the past-previous to it.When we think of tomorrow we just project it as another yesterday.We fail to learn about the difference between our past predictions and the subsequent outcomes

From Black Swan; by Nassim Taleb

4. Empiricism : From Newton to Taleb;

Hypothesis;Model;Theory;Law;

Ahypothesisis a limited statement regarding cause and effect in specific situations; it also refers to our state of knowledge before experimental work has been performed and perhaps even before new phenomena have been predicted.

The wordmodelis reserved for situations when it is known that the hypothesis has at least limited validity. Not necessarily a precise and accurate depiction but it exists to provide a mathematical representation of the hypothesis of interest allowing comparison with experiment.

Ascientific theory or lawrepresents an hypothesis, or a group of related hypotheses, which has been upheld through repeated experimental tests. Theories are often formulated in terms of a few concepts and equations, which are identified with " laws of nature , suggesting their universal applicability. Accepted scientific theories and laws become part of our understanding of the world and the basis for exploring less well-understood areas of knowledge.Theories are not easily discardedbut have earned the right to continue until the evidence is overwhelmingly in favor against it.

TheSCIENTIFIC METHODrequires that a hypothesis be ruled out or modified if its predictions are clearly and repeatedly incompatible with experimental tests.Further, no matter how elegant a theory is, its predictions must agree with experimental results if we are to believe that it is a valid description of the world.In experimental science, " experiment is supreme " and experimental verification of hypothetical predictions is an absolutely necessary (but not necessarily sufficient) condition toward validation and acceptance.

5. Application To Model Building for Use in Stock Selection and Portfolio Construction

Theorize (Einstein) : stock prices are a function of financial statement data

Collect Empirical Evidence (Newton) : form portfolios (stock cohorts) based on financial statement data

Obtain Numbers (Kelvin) : hold these portfolios for a period of time and measure performance statistics on them

Presume True Forward in Time (Taleb-NOT!) : thirsty bubbles internet, credit crunches, LTCM, mortgages, October 1987, August 2007, sustain a competent level of paranoia..

What happened?Whats wrong with this picture?Problem is with understanding models...lets review..

6. Common Mistakes in ApplyingStatistics

Interpreting aregressionR 2 as a measure of statistical significance or using it to gauge whether one regression model is significantly stronger than another.Its really rather poor at that!

Thinkingcorrelationis a general measure ofassociation .If two factors areindependent , they aredefinitely un-correlated , but if two factors areun-correlated , they arenot necessarily independent .Covariance too.

• The correlation ofyandxin:y=x 2 is zero but they sure are dependent!

Ascertaining correlation, then implying causality.

Not usingsingle value decompositionwhen doing regressions.In practice, many similar variables are available and most factors in a model are correlated.This can result in two such factors or combinations of factors to fit the data equally well, yielding no satisfied solution.

• Beware small (abs value) regression coefficients.

Focusing on theaverage(i.e. return), and not being cognizant of thespread or confidence intervaldue to errors or likelihood.

• A measurement without a quoted error is near meaningless.

• Accepting a model with an R 2of 0.05, because theaverage6 month return of a cohort of 200 stocks out of 2,000 over 15 years is 3.12% higher than the benchmark!

7.

Focusing on theaverage return , and not being cognizant of thespread or confidence intervaldue to errors or likelihood.

EXAMPLE 1:

8. Average Decile Model XS Returns Seemingly Looks Good! Source: Allegiant proprietary information 9. Spread of Decile Model XS Returns Okay Still! Source: Allegiant proprietary information 10. Raw XS Return as function of Model Stock Rank argh.. Source: Allegiant proprietary information 11.

Focusing on theaverage return , and not being cognizant of thespread or confidence intervaldue to errors or likelihood.

EXAMPLE 2:

12. Source: Allegiant proprietary information 13. Source: Allegiant proprietary information 14. Source: Allegiant proprietary information 15. The 95% Confidence Interval about the Mean! Theres some overlap, so its possible they have the same mean! Top Decile Mod_1 (-0.74) Top Decile Mod_2 (5.92) Source: Allegiant proprietary information 16. Source: Allegiant proprietary information 17. Common Mistakes in ApplyingEmpiricism

The most fundamental error is to mistake thehypothesisfor an explanation of a phenomenon, without performing experimental tests. Sometimes " common sense " and " logic " tempt us into believing that no test is needed.

Ignore or rule out data which do not support thehypothesis( discounting pesky cases ). Anchoring often is an over-riding human condition were unable to remove ourselves from ( i.e. buying value stocks always works )

The failure toestimate quantitativelysystematic errors. At the minimum, we should be able to articulate their order of magnitude.

• Error intrinsic to production of measurement. Because this type of error has equal probability of producing a measurement higher or lower numerically than the " true " value, it is called random error.

• Non-random or systematic error, due to factors which bias the result in one direction. No measurement, and therefore no experiment, can be perfectly precise. At the same time, in science we have standard ways of estimating and in some cases reducing errors. Thus it is important to determine the accuracy of a particular measurement and, when stating quantitative results, to quote the measurement error.

• A measurement without a quoted error is near meaningless.

18. A Value Model Top (red) & Bottom (dots) Decile XS Return Source: Allegiant proprietary information 19. Examine a Valuation Component of the Model at Different Times.. Working Working Working Source: Allegiant proprietary information ICs vary wildly through time. 20. CONCLUSION :These data are representative of when factors lose their efficaciousness at various points in history.One would expect the slopes of return vs factor rank to fall off during the bubble, 2003 and 2006 to the present,and indeed they do.The chart below shows the R 2 s of regressions of the factors vs excess return for each of the sixtime periods.Notice that the largest R 2 s are in the early years, after the bubble and in 2004 and 2005, while the smallerrelationships hold for the problematic years during the bubble, 2003 and 2006 to the present F1F2 F4F5F6F7F8F9 Source: Allegiant proprietary information 21. [ ] nICTC= IR Model TC(i.e. optimization) [ ] Portfolio [ ] Bench + TC: Effectiveness of Portfolio Construction Process IC: Effectiveness of Alpha Model 22. Implications of Empirically ConstructedModels on Portfolio Construction

One can have all top decile stocks in the portfolio and still under-perform the benchmark ( why?, errors )

• due to volatility dynamics of models IC(-ve IC )

• due to TC being overly constrained by risk model or constraints

• due to hard constraints not allowing feasible solution

• due to too strong a reliance on the calculated alpha

The above can conspire to select the stocks which are under-performers

of the top decile, killing your IR ---------------------------------------------

Remember, just because your optimizer gives you a solution, it may not be a solution thats actually on the efficient Frontier ( most optimizers in practice will ALWAYS return a portfolio ).You have to look to see if its a feasible solution and an efficient portfolio!

23. Black Swan Mitigation, is it Possible??

• Usually We Employ the Following:

• Out-of-Sample Testing

• More/Better Factors ( reduce each model to its simplest set and no simpler )

• Tighter or Looser Tolerance to Bench in Optimization or Portfolio Construction

24. Common Mistakes in ApplyingStatistics -II Out-of-Sample Testing is Over-Rated

• Sample splittingmust occur, which results in aloss of informationandlower predictive powerdue tosmaller sample sizesand simultaneously,may fail to detect predictabilitythat thein-sampletest would havefound .

• The claim thatout-of-sample testingisvoid of data miningis factually bankrupt becauseout-of-sample testingis mostly used in across validation construct .

• • The researcher keeps iterating over different models with the in-sample data, until they obtain a preferred out-of-sample result.

• Often, there are periods of time during thein-samplewhere performance of the model would even be worse than theout-of-sampletime period .

• Lastly, out-of-sample testing usually leads to over/under confidence.

• • if a model out-performs above expectation during the out-of-sample test period one typically would over-estimate its capability and if it under-performs one rejects the model, or at the very least, change it to get better performance in the out-of-sample time period (i.e. data mining).

• Theexpert literatureon the subjectwould contendthere isno evidencethat model distortions due todata miningare more prevalent for in-sample tests than out-of-sample tests[below]and conclude thatresultsofin-sample testsof predictability will typically be morecrediblethan results ofout-of-sampletests.

• IN-SAMPLE OR OUT-OF-SAMPLE TESTS OF PREDICTABILITY: WHICH ONE SHOULD WE USE? European Central Bank Working Paper Series No. 195; by Atsushi Inoue & Lutz Kilian; November 2002

25. Black Swan Storms Cont

• One Cannot Predict Extreme Events .Best you can do is familiarize yourself with what your model did during the last Black Swan event and create disaster recovery strategies

• More factors only serves to model normal events more closely

• Tighter/Looser Benchmark Constraints will move your portfolio proportionally with the index during the next Black Swan Storm

• The past is not prologue, chaotic nature of markets means onlytrendsarelikely calculable , accurate market trajectory isnt a viable objective and should be avoided as a goal in modeling.

• Individual stock returns are not predicted at all by empirical models, onlygroups of stocks most likely direction is.In a Black Swan Storm, the fundamental data used to create your model, disconnects from stocks for a time so that a model isuseless until stocks return to reflect financial statement causes .True of risk models too!

• This is the difference between stocks and companies that we dont learn until years in the market.Stocks are not companies but have other phenomena influencing their movements.Accounting data is not comprehensive.At times OTHER DATA is dominant.

26. Hints on a Models Usefulness.

• Trust the models trends & direction, not its precise levels

• • when ranking 2,000+ stocks, a model has a hard time discriminating between a stock ranked 42 and a stock ranked 63( i.e. stock alphas of 2.5 +/-12 and 2.4 +/-12)

• Emphasize std-dev of return reduction rather than increasing average decile return ( see next slide. )

• If you can utilize a plurality of models that are lowly correlated, do so..

• Goods stocks and good companies can be mutually exclusive.Act this way, dont just talk a talk (model regresses returns not balance sheets)

• Understand a trading position can be just as profitable as an investing situation, different model factors operate at different frequencies

• Marrying bottoms up with top down is easy

• • The stock alpha model selects the stocks, the weighting or portfolio leaning can be driven by country, sector or industry models

• Models do not model extreme Black Swan events.

• • Try to keep risk management separated from alpha predictionUNTILyou put the portfolio together

• • Keep your expectations from models/technology low

27. Lowering the Std-Dev of Returns can be a More Fruitful Research Objective then Raising the Average Return of a decile. Source: Allegiant proprietary information Impact Of Lower Deviation of Returns. Our Customers feel the Variance, Not the Mean .. 28. Hints on a Models Usefulness.

• Maintain a healthy dose of mistrust of a model, rather than a mistrusting of your quants...

• Avoid extrapolating, but include interpolating.....

• Not checking other methods of model construction, can lead to blaming the methodology for results (good or bad)

• As above, but your fundamentally trained PMs will blame the model constructor, not the methodology.CIOs please be aware

• Differentiating luck from skill need another full seminar to discuss this topic, but its important!!!

• • Keep your expectations from models/technology low

29. Common-Sense Additionsto Quant Strategies

• Thematic Investing

• • demand for energy.. Chin-Dia: buy Oil stocks, E&P, commodities

• • falling dollar, rising world economies: buy Large Cap Stocks where the companies get a larger percentage of revenue in FX

• Maximize the serendipity around you

• • buy more stocks than are necessary for diversification alone

• Invest in preparedness not in prediction

• • think about the worst case scenario, than develop plans for what to do

• • (SIPDE: scan, interpret, predict, decide, execute)

• Put yourself in situations where favorable consequences are much larger than unfavorable ones

• • if institutional buying is moving strongly into materials, dont bet

• • against it

30.

• The views expressed in this investment report represent the opinions of the author and are not necessarily the views of Allegiant Asset Management Company (Allegiant) or any affiliate. They are not intended to predict or depict performance of any investment. All information contained herein is for informational purposes and should not be construed as investment advice. It does not constitute an offer, solicitation or recommendation to purchase any security. The information herein was obtained by various sources; we do not guarantee its accuracy or completeness.Past performance does not guarantee future results .These views are as of the date of this publication and are subject to change based on subsequent developments. Allegiant is SEC-registered investment advisor and a subsidiary of National City Corporation.

Required Financial Homily.. Steven P. Greiner, Ph.D. Allegiant Asset Mgmt Group One North Franklin; Ste 750 Chicago, IL 60606 (312) 384 8254 [email_address] Source: Allegiant proprietary information