Assessing the Asymmetric Information Associated with the Equity Market A CART Based Decision Rule Analysis
 

Assessing the Asymmetric Information Associated with the Equity Market A CART Based Decision Rule Analysis

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Assessing the Asymmetric Information Associated with the Equity Market A CART Based Decision Rule Analysis Assessing the Asymmetric Information Associated with the Equity Market A CART Based Decision Rule Analysis Presentation Transcript

  • 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
  • Presentation Agenda Overview Problem Statement Results Analysis Conclusions
  • 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
  • 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
  • Classic Factors Price Momentum Earnings Momentum Valuation System Economic
  • 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
  • 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)
  • 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
  • Tobin’s Q (US Market)
  • Valueline Timeliness Ranks (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
  • 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)
  • Two Step Analytic Process Screen variables with neutral nets Develop decision rules using CART Holdout Assessment
  • Probabilistic Neural Networks An extension to the classical backward propagation neural net Non-parametric “Black Box” Results often difficult to interpret and operationalize
  • Neural Nets
  • 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
  • CART Tree
  • 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
  • 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
  • Results (Modified Sharp Ratio)Case Qtrs./ Quarter Value Going NSI Selling NSI Sample Line Long Short Size Ones 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
  • 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
  • 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
  • Thanks for Listening!