Owen P. Hall, Jr., P.E., Ph.D.Owen P. Hall, Jr., P.E., Ph.D.

Pepperdine UniversityPepperdine University

CART ConferenceCART Conference

May, 2012May, 2012

San Diego, CASan Diego, CA

Assessing the Asymmetric Information Associated with the Equity Market:

A CART Based Decision Rule Analysis

Presentation AgendaPresentation Agenda

OverviewOverview Problem StatementProblem Statement Results AnalysisResults Analysis ConclusionsConclusions

Problem StatementProblem Statement

Assess the effectiveness of analytics to Assess the effectiveness of analytics to detect asymmetric information associated detect asymmetric information associated with the equity marketwith the equity market ModelsModels

• Probabilistic Neural netsProbabilistic Neural nets• CARTCART

FactorsFactors• Classic (e.g., Price Momentum)Classic (e.g., Price Momentum)• Tobin’s QTobin’s Q• EntropyEntropy

ChallengeChallenge In an efficient market, the current In an efficient market, the current

prices of securities represent prices of securities represent unbiased estimates of their true or unbiased estimates of their true or fairfair market value at all timesmarket value at all times

This principle suggests that neither This principle suggests that neither technical analysis nor fundamental technical analysis nor fundamental analysis can assist investors in analysis can assist investors in identifying undervalued or identifying undervalued or overvalued stocksovervalued stocks

I'd be a bum in the street with a tin cup if the markets were efficient -- Warren Buffett

Classic FactorsClassic Factors

Price MomentumPrice Momentum Earnings MomentumEarnings Momentum ValuationValuation SystemSystem EconomicEconomic

EntropyEntropy

The basic idea is that more volatile The basic idea is that more volatile securities have a greater entropy state than securities have a greater entropy state than more stable securities more stable securities

Two fundamentally different phenomena Two fundamentally different phenomena exist in which time based securities data exist in which time based securities data deviate from constancy:deviate from constancy: Exhibit larger standard deviationsExhibit larger standard deviations Appear highly irregularAppear highly irregular

The standard deviation measures the extent The standard deviation measures the extent of deviation from centrality while entropy of deviation from centrality while entropy delineating the extent of irregularity or delineating the extent of irregularity or complexity of the data setcomplexity of the data set

EntropyEntropy Two entropy modelsTwo entropy models

Approximate entropy (ApEn)Approximate entropy (ApEn) Sample entropy (SaEn)Sample entropy (SaEn)

Model inputsModel inputs Time seriesTime series Matching template length (M)Matching template length (M) Matching tolerance level (r)Matching tolerance level (r)

Time series length (50 months)Time series length (50 months)

Tobin’s QTobin’s Q Q = Market value / Replacement valueQ = Market value / Replacement value

Reflects the expected current and future Reflects the expected current and future profitability of capital profitability of capital

Q values less than one identify under Q values less than one identify under valued equitiesvalued equities

Q values greater than one suggest than Q values greater than one suggest than capex will increase share holder wealthcapex will increase share holder wealth

Q values less than one suggest making Q values less than one suggest making acquisitions is cheaper than capexacquisitions is cheaper than capex

Tobin’s QTobin’s Q (US Market)(US Market)

Valueline Timeliness RanksValueline Timeliness Ranks (1965 – 2009)(1965 – 2009)

Rank Weekly (%) Yearly (%)

1 15,575 30,778

2 10,727 4,174

3 4,924 252

4 2,846 - 60

5 5,266 -99

DatabaseDatabase

2008 (4) – 2010(1) – 6 Quarters2008 (4) – 2010(1) – 6 Quarters SourcesSources

Value Line Investment SurveyValue Line Investment Survey Ford Equity ResearchFord Equity Research Mergent OnlineMergent Online

Sample Size (100 ~ 400)Sample Size (100 ~ 400) Target Variable – PGQ (binary- lagged)Target Variable – PGQ (binary- lagged)

Two Step Analytic ProcessTwo Step Analytic Process

Screen variables with Screen variables with neutral netsneutral nets

Develop decision rules Develop decision rules using CARTusing CART

Holdout AssessmentHoldout Assessment

Probabilistic Neural NetworksProbabilistic Neural Networks

An extension to the classical backward An extension to the classical backward propagation neural netpropagation neural net

Non-parametricNon-parametric “ “Black Box”Black Box” Results often difficult to interpret and Results often difficult to interpret and

operationalizeoperationalize

Neural NetsNeural Nets

CARTCART Non-parametricNon-parametric Interactive effectsInteractive effects Non-normally distributed variablesNon-normally distributed variables Decision tree logic makes it easier to Decision tree logic makes it easier to

apply model outcomesapply model outcomes Model is extremely robust to the effect of Model is extremely robust to the effect of

outliersoutliers Results easy to interpret and implementResults easy to interpret and implement

CART TreeCART Tree

Neural Net ResultsNeural Net Results

Rank 8-4 9-1 9-2 9-3 9-4 10-1

1 PSS ROA PSS SMO CNE PRM

2 PRM SUE PVA PSS EMO Q

3 PVA PSS SEP ROA SMO ROA

4 ROA SMO PRM EMO VMO VMO

5 SEP EMO SMO SEP PEG EMO

6 VMO SEP Q Q Q SMO

7 EMO PRM VMO PRM SUE PSS

8 SUE PVA PEG EMO PRM PER

9 PEG PEG EMO PEG PSS SEP

10 SMO VMO ROA PVA SEP COM

Classification AnalysisClassification Analysis(9/4 -> 10/1)(9/4 -> 10/1)

Actual

Predicted 1 0

1 31 15 67% PPV1

0 16 33 67% NPV2

Total 47 48

66% 69%

Sensitivity Specificity

1PPV = ratio of the number of winners classified correctly divided by the total number of securities classified as winners.2NPV = ratio of the number of losers classified correctly divided by the total number of securities classified as losers.

ResultsResults(Modified Sharp Ratio)(Modified Sharp Ratio)

Case Qtrs./Sample

Size

Quarter Value Line Ones

Going Long

NSI Selling Short

NSI

1 1/89 9-2 0.289 0.392 38 0.210 53

2 1/91 9-3 0.775 0.853 51 -0.022 37

3 1/88 9-4 1.177 0.771 53 -0.043 40

4 1/93 10-1 0.513 0.553 38 0.485 56

5 1/94 10-2 -0.580 -0.328 46 -0.583 49

6 2/180 9-3 0.775 0.800 23 0.789 65

7 2/179 9-4 1.177 0.598 62 0.749 31

8 2/181 10-1 0.513 0.514 49 0.512 45

9 2/187 10-2 -0.580 -0.498 59 -0.728 36

10 4/361 10-1 0.513 0.613 49 0.418 45

11 4/366 10-2 -0.580 -0.493 70 -0.605 25

ConclusionsConclusions Modeling approach generally Modeling approach generally

performed as well or better than performed as well or better than Valueline 100Valueline 100

CART results provide an CART results provide an operational strategyoperational strategy

Adding transaction costs reduces Adding transaction costs reduces model effectivenessmodel effectiveness

Portfolio size based on binary Portfolio size based on binary target variable remains target variable remains problematicalproblematical

Future ResearchFuture Research

Expand data set from 6 to 12 Expand data set from 6 to 12 quartersquarters

Ternary classification targetTernary classification target Variable selection optimizationVariable selection optimization Add economic factorsAdd economic factors

CPICPI UEMUEM

Explore “super” factorsExplore “super” factors Q / ApEnQ / ApEn PRM / SpEnPRM / SpEn

Thanks for Listening!Thanks for Listening!