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Assessing the Asymmetric Information Associated with the Equity Market: A CART Based Decision Rule Analysis Owen P. Hall, Jr., P.E., Ph.D. Pepperdine University CART Conference May, 2012 San Diego, CA
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Presentation Agenda Overview Problem Statement Results Analysis Conclusions
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Problem StatementAssess the effectiveness of analytics todetect asymmetric information associatedwith the equity market Models • Probabilistic Neural nets • CART Factors • Classic (e.g., Price Momentum) • Tobin’s Q • Entropy
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Challenge In an efficient market, the current prices of securities represent unbiased estimates of their true or fair market value at all times This principle suggests that neither technical analysis nor fundamental analysis can assist investors in identifying undervalued or overvalued stocks Id be a bum in the street with a tin cup if the markets were efficient -- Warren Buffett
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Entropy The basic idea is that more volatile securities have a greater entropy state than more stable securities Two fundamentally different phenomena exist in which time based securities data deviate from constancy: Exhibit larger standard deviations Appear highly irregular The standard deviation measures the extent of deviation from centrality while entropy delineating the extent of irregularity or
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Entropy Two entropy models Approximate entropy (ApEn) Sample entropy (SaEn) Model inputs Time series Matching template length (M) Matching tolerance level (r) Time series length (50 months)
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Tobin’s Q Q = Market value / Replacement value Reflects the expected current and future profitability of capital Q values less than one identify under valued equities Q values greater than one suggest than capex will increase share holder wealth Q values less than one suggest making acquisitions is cheaper than capex
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Database 2008 (4) – 2010(1) – 6 Quarters Sources Value Line Investment Survey Ford Equity Research Mergent Online Sample Size (100 ~ 400) Target Variable – PGQ (binary- lagged)
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Two Step Analytic Process Screen variables with neutral nets Develop decision rules using CART Holdout Assessment
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Probabilistic Neural Networks An extension to the classical backward propagation neural net Non-parametric “Black Box” Results often difficult to interpret and operationalize
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CART Non-parametric Interactive effects Non-normally distributed variables Decision tree logic makes it easier to apply model outcomes Model is extremely robust to the effect of outliers Results easy to interpret and implement
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Neural Net ResultsRank 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
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Classification Analysis (9/4 -> 10/1) Actual Predicted 1 0 1 31 15 67% PPV1 0 16 33 67% NPV2 Total 47 48 66% 69% Sensitivity SpecificityPPV = ratio of the number of winners classified correctly divided by the total number of securities classified as winners.1NPV = ratio of the number of losers classified correctly divided by the total number of securities classified as losers.2
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Conclusions Modeling approach generally performed as well or better than Valueline 100 CART results provide an operational strategy Adding transaction costs reduces model effectiveness Portfolio size based on binary target variable remains problematical
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Future Research Expand data set from 6 to 12 quarters Ternary classification target Variable selection optimization Add economic factors CPI UEM Explore “super” factors Q / ApEn PRM / SpEn
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