cross-sectional performance and investor sentiment

8/10/2019 Cross-sectional Performance and Investor Sentiment

1/36

CROSS-SECTIONAL PERFORMANCEAND INVESTOR SENTIMENT IN A

MULTIPLE RISK FACTOR MODEL

Dave BerguerH.J. Turtle


2/36

Objective

Are opaque securities more sensitive tomeasures of market sentiment?

Is ex-ante known investor sentiment relatedto marginal performance of opaque andtranslucent securities?


3/36

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;


4/36


5/36

Measuring attributes of

sentiment-prone stocks


6/36


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;


7/36



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.


8/36


9/36


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.


10/36



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.


11/36



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.


12/36


13/36



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.


14/36



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.


15/36


16/36


17/36



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.


18/36

Performance conditional on

investor sentiment


19/36


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.


20/36


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.


21/36


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.


22/36


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;


23/36


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.


24/36

Table 6

Suggests the expected contrarian nature ofsentiment as a conditioning variable foropaque firms;


25/36

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.


26/36

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


27/36


size portfolios


28/36

Conditional alpha across age

portfolios


29/36


earnings portfolios


30/36


dividend portfolios


31/36


prop, plant & eqp portfolios


32/36

Conditional alpha across R&D

portfolios


33/36

Conditional alpha across B/M

portfolios


34/36


sales growth portfolios


35/36

Table 8

Robustness test

Expands the model to account for other riskfactors;

Results are robust.


36/36

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.

cross-sectional performance and investor sentiment

Documents