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CROSS-SECTIONAL PERFORMANCEAND INVESTOR SENTIMENT IN A
MULTIPLE RISK FACTOR MODEL
Dave BerguerH.J. Turtle
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Objective
Are opaque securities more sensitive tomeasures of market sentiment?
Is ex-ante known investor sentiment relatedto marginal performance of opaque andtranslucent securities?
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Investor sentiment and firm-
characteristic data
Sentiment is considered broadly as general
optimism os pessimism towards future stockreturns;
Sentiment is measured using the monthlysentiment index of Baker and Wurgler;
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Measuring attributes of
sentiment-prone stocks
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Measuring attributes of
sentiment-prone stocks The regression model used was:
Forj= 1, 2, ..., N; For t= 1, 2, ..., N; Where:
N = number of cross-sectional observations; T = number of time series;
Rj,t= excess return of asset j during period t;
Rm,t = excess market return during period t;
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Measuring attributes of
sentiment-prone stocks
Based on j,sent, stocks are grouped into 10
portfolios;
A typical firm in the high sentiment
sensitivity grouping is expected to displayvolatile returns, a small equity base, lowearnings, low dividends, high distress risk andhave a relatively intangible assets.
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Measuring attributes of
sentiment-prone stocks Results in table 2 support the hypothesis that
firms with high sensitivity to investor sentiment
tend to be relatively opaque;
Table 2 document a strong relation between thefirms that we estimate to have the highestsensitivity to investor sentiment, and the opaquefirm characteristics that Baker and Wurgler(2006) hypothesize, after controlling for marketrisk.
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Measuring attributes of
sentiment-prone stocks
To provide robustness results regarding the
relation between firm characteristics andsentiment sensitivities documented in table2, the authors expand their model to controlfor multiple risk sources.
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Measuring attributes of
sentiment-prone stocks
Augmented regression model:
Where:
Rsmb, Rhml, and Rmom, represent the small minus
big, high minus low, and momentum risk factors,respectively.
Risk factor data comes from Ken Frenchsdatalibrary.
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Measuring attributes of
sentiment-prone stocks
Again, results strongly support the
hypothesis that sentiment-prone stocksdisplay opaque firm characteristics;
However, the difference in sample averagesof firm characteristics across portfolios aredampened when additional risk factors areconsidered.
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Measuring attributes of
sentiment-prone stocks
Table 2 and 3 corroborates the hypothesis
that sentiment-prone stock portfolios aresmall, intangible and volatile;
To verify that sentiment-sensitivities exhibitsimilar patterns across firm characteristicswith ex ante available information, eq 1 and 2are reestimated using roling windows.
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Measuring attributes of
sentiment-prone stocks
As expected, younger firms, and firms with
larger root mean square error, are moreopaque, and more sensitive to sentiment;
Results are congruent with prior analysis,except for BM in table 5 that loses itssignificance.
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Performance conditional on
investor sentiment
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Performance conditional on
investor sentiment
Analysis to this point documents a robust
relation between opacity and sentiment;
Analysis is now shifted to whether ex anteknown sentiment result in positive portfolioperformance.
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Performance conditional on
investor sentiment
Subsequent analysis considers expected
marginal performance during period t, givenonly information available in t-1;
Conditional alphas provide the marginalperformance of a given sentiment portfoliofor a given level of systematic risk.
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Performance conditional on
investor sentiment
Conditional alpha is estimated directly from
the folowing unconditional regression:
Where the conditioning information instrument,Sent, is known at the beginning of eachinvestment interval.
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Performance conditional on
investor sentiment
Previous analysis indicates strong relation
between sentiment sensitivities and firmcharacteristics;
Firm characteristics of previous analysis arenow used to form 10 portfolios based on afirmsranking of the specific characteristic atthat point in time;
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Performance conditional on
investor sentiment Given a known investor sentiment realization,
the conditional alpha may be written as:
The resultant conditional alpha measuresmarginal performance from the conditional
regression of the portfolio return against the riskfactors where excess returns for all portfoliosand factors are linearly related to the underlyinginformation instruments.
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Table 6
Suggests the expected contrarian nature ofsentiment as a conditioning variable foropaque firms;
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Table 7
We observe large variation in marginalperformance for opaque firms across levels ofinvestor sentiment, with little variation inmarginal performance for translucent firmsacross the same levels of investor sentiment.
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Conditional alpha across
volatility portfolios
Variation in the conditional alpha, acrossortfolios for a iven sentiment level
Variation in conditional alpha, across portfolios, for a given level ofsentiment, for a given portfolio
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Conditional alpha across
size portfolios
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Conditional alpha across age
portfolios
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Conditional alpha across
earnings portfolios
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Conditional alpha across
dividend portfolios
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Conditional alpha across
prop, plant & eqp portfolios
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Conditional alpha across R&D
portfolios
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Conditional alpha across B/M
portfolios
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Conditional alpha across
sales growth portfolios
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Table 8
Robustness test
Expands the model to account for other riskfactors;
Results are robust.
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Conclusion
Most sentiment-prone stocks tend to exhibitopaque characteristics (volatile, small, youngand intangible);
Opaque portfolios offer the greatest marginalperformance when sentiment levels arelowest;
Opaque portfolios exhibit much greatervariation in conditional alpha estimatesacross levels of investor sentiment relative totranslucent portfolios.
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