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Investor Sentiment Risk Factor and Asset Pricing Anomalies

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  1. Investor Sentiment Risk Factor and Asset Pricing Anomalies Chienwei Ho Massey University Chi-Hsiou Hung Durham University

  2. Motivations • Standard CAPM (Sharpe, 1964; Lintner, 1965)‏ • Expected return is associated with market risk • Unable to explain pricinganomalies • Size effect (Banz, 1981)‏ • Value effect (Chan, Hamao, and Lakonishok, 1991)‏ • Momentum effect (Jegadeesh and Titman, 1993)‏ • Investor sentiment affects stock returns (Black, 1986; De Long, Shleifer, Summers and Waldmann, 1990; Baker and Wurgler, 2006; Yu and Yuan, 2011). • Investor sentiment asa risk factor. • Conditional/Dynamicmodels outperform unconditional/static models (Harvey, 1989; Gibbons and Ferson, 1985; Ferson, Kandel, and Stambaugh, 1987)‏. 2

  3. Research Questions • Is investor sentiment a risk factor? (i.e., Is investor sentiment priced?) • Does investor sentiment, as a risk factor, help to explain pricing anomalies: size, value, liquidity, and momentum effects? • Asset pricing models: CAPM, FF, FFP, FFW, FFPW • Time-varying: default spread, (Size+B/M) 3

  4. Contributions • Constructing a sentiment risk factor, SMN (sensitive minus non-sensitive). • Showing SMN is a priced factor. • Stocks with certain firm characteristics react differently to investor sentiment. • SMN alone can explain the size premium. • Sentiment-augmented asset pricing models can capture the pricing anomalies: size, value, momentum effects. 4

  5. Literature • Sentiment and stock returns • Anegative relationship b/t the consumer confidence level in one month and returns in the followingmonth (Fisher and Statman, 2002). • Highlevels of sentiment result in lower returns over the next 2 to 3 years (Brown and Cliff, 2005). • Changes in consumer sentiment are positively related to excess stock market returns (Charoenrook, 2005). • Investor sentiment has larger effects on stocks whose valuations are highlysubjective and difficultto arbitrage (Baker and Wurgler, 2006). 5

  6. Literature (Cont’d) • Sentiment and firm characteristics • Closed-end fund discount and net mutual fund redemptions predict the size premium (Neal and Wheatley, 1998). • Individual investors who are more prone to sentiment than institutional investors tend to have disproportionally largeholdings on small stocks (Lee, Shleifer, and Thaler, 1991; Nagel (2005). • Difficult-to-arbitrage and hard-to-value stocks (small, young, non-dividend-paying, etc.) are more responsive to investor sentiment (Baker and Wurgler, 2006; Lee, Shleifer, and Thaler; Lemmon and Portniaguina, 2006) 6

  7. Construction of Sentiment Factor – SMN • Using 25-month rolling windows to obtain sentiment beta for each stock, , (Brown and Cliff, 2005 find high sentiment results in lower market returns over the next 2 to 3 years). • In each month, break stocks into 5 groups based on the absolute value of . • Monthly SMN = Sensitive Return – Non-sensitive Return 7

  8. Conditional Sentiment-augmented Models • Conditioning variables • Macro variables: default spread • Firm-specific characteristics: B/M and size 8

  9. Empirical Framework adjustedreturn (second-pass regression)‏ pricing anomalies • conditionalasset pricing model (first-pass regression)‏ Ho: Ct = 0 ? • Indicator of explanatory power of model: adj-R2(lower ==> better)‏ 9

  10. Asset Pricing Models traditional risk factors 10

  11. Time-Varying Beta 11

  12. Beta Specifications Unconditional Model Specification A: function of (SIZE + B/M) Specification B: function of def Conditional Model Specification C: function of (SIZE + B/M)def 12

  13. Two-Pass Framework (using CAPM as an Example)‏ Risk Factors (for CAPM here)‏ Adjusted Return Anomalies 13

  14. Investor Sentiment Indices • Baker and Wurgler, 2006 (∆BW)‏ • ∆BW: A composite sentiment index based on the first principal component of six raw sentiment proxies: NYSE turnover, closed-end fund discount, the number of IPOs, the first-day return on IPOs, the equity share in new issues and the dividend premium. • ∆BWWort: a cleaner sentiment measure that removes business cycle variations from ∆BW. • Investors’ Intelligence Index (II)‏ • Opinions of 150 newsletters: bullish, bearish, neutral. • Proportion of bullish advices. • Directly reflects (professional) investors’ opinions on stock markets. 14

  15. Trading Data and Variables for Anomalies • 8,526NYSE/AMEX/NASDAQ common stocks (1968-2005) from CRSP/COMPUSTAT meeting the specified criteria: • The returns in the current month, t, and over the past 60 months must be available. • Stock prices and shares outstanding have to be available in order to calculate firm size, and trading volume in month t – 2 must be available to calculate the turnover. • Sufficient data has to be available from the COMPUSTAT dataset to calculate the book-to-market ratio as of December of the previous year. • Only stocks with positive book-to-market ratios are included in our sample. • Book-to-market ratio values greater than the 0.995 fractileor less than the 0.005 fractileare set equal to the 0.995 and 0.005 fractile values, respectively. 15

  16. Table 1: Summary Statistics and Cross-Sectional Regressions(8,526 firms: 1968 - 2005)‏ (size effect) (value effect) (momentum effect) 16

  17. Figure 1: Stock Returns by Firm Characteristics and Sentiment Beta

  18. Is the Investor Sentiment Factor (SMN) Priced? Ho: = 0 18

  19. Table 2: Cross-Sectional Regressions of Excess Returns on SMN Beta * indicates significant at the level of 5%; ** indicates significant at the level of 1%. 19

  20. Table 3: Fama-MacBeth Regression Estimate for Unconditional Models 20

  21. Table 4: Fama-MacBeth Regression Estimate with SMN (conditional models) 21

  22. Table 5: Fama-MacBeth Regression Estimate with CAPM + SMN (conditional) 22

  23. Table 6: Fama-MacBeth Regression Estimate with FF + SMN (conditional) 23

  24. Table 7: Fama-MacBeth Regression Estimate with FF + PS + SMN (conditional) 24

  25. Table 8: Fama-MacBeth Regression Estimate with FF + momentum + SMN (conditional) 25

  26. Table 9: Fama-MacBeth Regression Estimate with FF + PS + momentum + SMN (conditional) 26

  27. Summary of Findings • Stocks with certain firm characteristics are more vulnerable to investor sentiment. • Returns on small firms are more sensitive to changes in investor sentiment than large firms. • Value stocks (high B/M) have larger sentiment beta than growth stocks. • A positive relationship between turnover and sentiment beta. • Past winners tend to be more responsive to changes in investor sentiment than past losers. • Stocks with higher sentiment beta earn higher returns. 27

  28. Summary of Findings • Investor sentiment helps to explain the cross-section of stock returns and pricing anomalies. • SMN is a risk factor, i.e., investor sentiment factor is priced. • SMN can always explain the size effect without requiring conditional pricing model. • Conditional versions of the sentiment-augmented FF-based models often capture the size and value effects. • Momentum effect sharply reduces when the factor loadings are conditional on the default spread in the sentiment-augmented models that contain the momentum factor. Hence, investor sentiment is also associated with the momentum profits. 28

  29. Q & A 29