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An Introduction to Behavioral Finance
SIP Course on “Stock Market Anomalies and Asset Management”
Professors S.P. Kothari and Jon Lewellen
March 15, 2004
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An Introduction to Behavioral Finance
Efficient markets hypothesis Large number of market participants Incentives to gather and process information about
securities and trade on the basis of their analysis until individual participant’s valuation is similar to the observed market price
Prices in such markets reflect information available to the participants, which means opportunities to earn above-normal rates of return on a consistent basis are limited
Prediction: Stock returns are (almost) impossible to predict Except that riskier securities on average earn higher rates of
returns compared to less risky firms
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An Introduction to Behavioral Finance
Behavioral finance Widespread evidence of anomalies is inconsistent with the
efficient markets theory Bad models, data mining, and results by chance Alternatively, invalid theory
Anomalies as a pre-cursor to behavioral finance Challenge in developing a behavioral finance theory of
markets Evidence of both over- and under-reaction to events
Event-dependent over- and under-reaction, e.g., IPOs, dividend initiations, seasoned equity issues, earnings announcements, accounting accruals
Horizon dependent phenomenon: short-term overreaction, medium-term momentum, and long-run overreaction
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An Introduction to Behavioral Finance
Behavioral finance theory rests on the following three assumptions/characteristics Investors exhibit information processing biases that
cause them to over- and under-react Individual investors’ errors/biases in processing
information must be correlated across investors so that they are not averaged out
Limited arbitrage: Existence of rational investors should not be sufficient to make markets efficient
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Behavioral finance theories
Human information processing biases Information processing biases are generally
relative to the Bayes rule for updating our priors on the basis of new information
Two biases are central to behavioral finance theories
Representativeness bias (Kahneman and Tversky, 1982) Conservatism bias (Edwards, 1968). Other biases: Over confidence and biased self-attribution
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Behavioral finance theories
Human information processing biases Representativeness bias causes people to over-
weight recent information and deemphasize base rates or priors
E.g., conclude too quickly that a yellow object found on the street is gold (i.e., ignore the low base rate of finding gold)
People over-infer the properties of the underlying distribution on the basis of sample information
For example, investors might extrapolate a firm’s recent high sales growth and thus overreact to news in sales growth
Representativeness bias underlies many recent behavioral finance models of market inefficiency
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Behavioral finance theories
Human information processing biases Conservatism bias: Investors are slow to update their
beliefs, i.e., they underweight sample information which contributes to investor under-reaction to news
Conservatism bias implies investor underreaction to new information
Conservatism bias can generate short-term momentum in stock returns The post-earnings announcement drift, i.e., the tendency of
stock prices to drift in the direction of earnings news for three-to-twelve months following an earnings announcement also entails investor under-reaction
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Behavioral finance theories
Human information processing biases Investor overconfidence
Overconfident investors place too much faith in their ability to process information
Investors overreact to their private information about the company’s prospects
Biased self-attribution Overreact to public information that confirms an
investor’s private information Underreact to public signals that disconfirm an investor’s
private information Contradictory evidence is viewed as due to chance Genrate underreaction to public signals
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Behavioral finance theories
Human information processing biases Investor overconfidence and biased self-attribution
In the short run, overconfidence and biased self-attribution together result in a continuing overreaction that induces momentum.
Subsequent earnings outcomes eventually reveal the investor overconfidence, however, resulting in predictable price reversals over long horizons.
Since biased self-attribution causes investors to down play the importance of some publicly disseminated information, information releases like earnings announcements generate incomplete price adjustments.
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Behavioral finance theories
In addition to exhibiting information-processing biases, the biases must be correlated across investors so that they are not averaged out
People share similar heuristics Focus on those that worked well in our evolutionary past Therefore, people are subject to similar biases Experimental psychology literature confirms systematic
biases among people
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Behavioral finance theories
Limited arbitrage Efficient markets theory is predicated on the
assumption that market participants with incentives to gather, process, and trade on information will arbitrage away systematic mispricing of securities caused by investors’ information processing biases
Arbitrageurs will earn only a normal rate of return on their information-gathering activities
Market efficiency and arbitrage: EMH assumes arbitrage forces are constantly at work
Economic incentive to arbitrageurs exists only if there is mispricing, i.e., mispricing exists in equilibrium
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Behavioral finance theories
Behavioral finance assumes arbitrage is limited. What would cause limited arbitrage? Economic incentive to arbitrageurs exists only if there
is mispricing Therefore, mispricing must exist in equilibrium Existence of rational investors must not be sufficient Notwithstanding arbitrageurs, inefficiency can persist
for long periods because arbitrage is costly Trading costs: Brokerage, B-A spreads, price impact/slippage Holding costs: Duration of the arbitrage and cost of short
selling Information costs: Information acquisition, analysis and
monitoring
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Behavioral finance theories
Why can’t large firms end limited arbitrage? Arbitrage requires gathering of information about a firm’s
prospects, spotting of mispriced securities, and trading in the securities until the mispricing is eliminated
Analysts with the information typically do not have the capital needed for trading
Firms (principals) supply the capital, but they must also delegate decision making (i.e., trading) authority to those who possess the information (agents)
Agents cannot transfer their information to the principal, so decisions must be made by those who possess information
Agents are compensated on the basis of outcomes, but the principal sets limits on the amount of capital at the agent’s disposal (the book)
Limited capital means arbitrage can be limited
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Behavioral finance theories
Like the efficient markets theory, behavioral finance makes predictions about pricing behavior that must be tested Need for additional careful work in this respect
Only then can we embrace behavioral finance as an adequate descriptor of the stock market behavior
Recent research in finance is in this spirit just as the anomalies literature documents inconsistencies with the efficient markets hypothesis
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Stock Returns, Aggregate Earnings Surprises, and Behavioral Finance
S.P. Kothari, Jonathan Lewellen,
Jerold B. Warner
SIP Course on “Stock Market Anomalies and Asset Management”
March 15, 2004
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Objective of the study
We study the relation between market index returns and aggregate earnings surprises We focus on concurrent and lagged
surprises Do prices react slowly? Is there discount rate information in
aggregate earnings changes?
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At the firm level, post-earnings announcement drift is well-known
The slow adjustment to public information is inconsistent with market efficiency
Slow adjustment is consistent with behavioral finance Barberis/Shleifer/Vishny (BSV, 1998) Daniel/Hirshleifer/Subrahmanyam (DHS, 1998) Hong/Stein (HS, 1999)
Aggregate return-earnings relation serves as an out-of-sample test of the behavioral hypothesis of investor underreaction
Literature concentrates on cross-sectional return predictability
We provide time-series evidence
Motivation
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Main findings Aggregate relation does not mimic the firm-level
relation Market returns do not depend on past earnings surprises Inconsistent with underreaction (or overreaction)
Market returns are negatively (not positively) related to concurrent earnings news
#s seem economically significant Earnings and interest/ discount rate shocks are positively
correlated Good aggregate earnings news can be bad news
Decomposing earnings changes does not fully eliminate the negative correlation between earnings news and returns, a troubling result
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Firm level drift and behavioral models
Drift could occur if investors systematically ignore the time-series properties of earnings.
Bernard/Thomas (1990) show that quarterly earnings changes have positive serial dependence (.34,.19,.06 at the first 3 lags)
If investors underestimate the dependence, prices will respond slowly and they will be surprised by predictable changes in earnings.
Consistent with this, the pattern of trading profits at subsequent earnings announcements matches the autocorrelation pattern.
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Evidence
Time-series properties of earnings Stock returns and aggregate earnings
surprises Returns, earnings, and discount rates
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Earnings series Compustat Quarterly database, 1970 – 2000 NYSE, Amex, and NASDAQ stocks with …
Earnings before ext. items, quarter t and t – 4 Price, quarter t – 4 Book value, quarter t – 4
Plus … December fiscal year end Price > $1 Exclude top and bottom 0.5% based on dE/P
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Sample
Quarterly returns (%), 1970 – 2000
Returns N VW EW
CRSP avg. 6,062 3.34 3.82 std. deviation -- 8.79 12.60 Sample avg. 2,423 3.26 3.42 std. deviation -- 8.38 11.40
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E/P, 1970 – 2000
-0.04
-0.02
0.00
0.02
0.04
0.06
1970.1 1974.1 1978.1 1982.1 1986.1 1990.1 1994.1 1998.1
E/P-agg
E/P-ew
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Firms w/ positive earnings, 1970 – 2000
0
1000
2000
3000
4000
5000
1970.1 1974.1 1978.1 1982.1 1986.1 1990.1 1994.1 1998.1
0.0
0.2
0.4
0.6
0.8
1.0
Number of firms (left scale)
Fraction E > 0 (right scale)
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Quarterly earnings changes (%),
1970 – 2000 Aggregate VW EW
dE/P dE/B dE/E dE/P dE/P Full sample avg 0.15 0.25 8.26 0.10 0.30 stdev 0.39 0.59 18.58 0.36 0.55 Small stocks avg 0.42 0.39 -- 0.56 0.86 stdev 1.18 1.14 -- 0.90 1.13 Large stocks avg 0.14 0.25 7.90 0.10 0.08 stdev 0.37 0.58 17.60 0.35 0.38 Low B/M avg 0.17 0.54 12.11 0.16 0.60 stdev 0.23 0.73 16.69 0.22 0.69 High B/M avg 0.19 0.11 -- 0.09 0.22 stdev 1.13 0.81 -- 1.02 1.21
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Aggregate earnings growth, 1970 – 2000
-0.60
-0.40
-0.20
0.00
0.20
0.40
0.60
1970.1 1974.1 1978.1 1982.1 1986.1 1990.1 1994.1 1998.1
dE/E-AGG
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dE scaled by lagged price, 1970 – 2000
-.015
-.010
-.005
.000
.005
.010
.015
1970.1 1974.1 1978.1 1982.1 1986.1 1990.1 1994.1 1998.1
dE/P-VWdE/P-EW
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Autocorrelations
Seasonally-differenced earnings (dE = Et – Et-4)
Estimation dE/St = 0 + k dE/St-k + t
dE/St = 0 + 1 dE/St-1 + 2 dE/St-2 + ….. +
5 dE/St-5 + t
Market: Time-series regressions Firms: Fama-MacBeth cross-sectional
regressions
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Autocorrelations, dE/P, 1970 – 2000
Simple regressions Multiple regressions
Lag Slope T-stat Adj. R2 Slope T-stat Adj. R2
Firms 1 0.38 18.48 -- 0.40 18.39 -- 2 0.22 14.58 -- 0.14 11.20 3 0.08 5.67 -- 0.06 6.47 4 -0.28 -16.82 -- -0.42 -22.83 5 -0.11 -7.03 -- 0.16 12.93
EW 1 0.64 8.81 0.39 0.61 6.33 0.43 2 0.40 4.62 0.14 0.11 1.05 3 0.14 1.49 0.01 0.00 0.01 4 -0.15 -1.62 0.01 -0.30 -2.76 5 -0.21 -2.26 0.03 0.04 0.40 VW 1 0.73 11.54 0.52 0.73 7.75 0.57 2 0.52 6.65 0.26 0.22 1.93 3 0.23 2.55 0.04 -0.22 -1.92 4 -0.00 -0.03 -0.01 -0.18 -1.62 5 -0.12 -1.30 0.01 0.07 0.80
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Implications
Basic message Pattern similar for firms and market Persistence stronger for market – good for tests
Specifics Transitory, idiosyncratic component in firm
earnings Aggregate earnings changes are permanent Earnings changes predictable but volatile ( = 18.6%)
AR1 similar to AR5
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Returns and earnings surprises
Rt+k = + dE/Pt + et+k
k = 0, …, 4 Changes and surprises Market: Time-series regressions Firms: Fama-MacBeth cross-sectional
regressions
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Returns and earnings, 1970 – 2000
Earnings change Earnings surprise
k Slope T-stat Adj. R2 Slope T-stat Adj. R2
Firms 0 0.53 26.94 -- 1 0.58 28.70 -- 2 0.20 10.66 -- 3 0.09 5.24 -- 4 0.00 0.03 --
EW 0 -1.30 -0.90 0.00 1.54 0.85 0.04 1 -3.75 -2.60 0.05 -3.70 -2.04 0.05 2 -2.81 -1.97 0.02 -3.03 -1.65 0.01 3 -1.36 -0.95 0.00 1.15 0.63 0.03 4 -3.14 -2.23 0.03 -4.48 -2.43 0.03 VW 0 -4.98 -2.31 0.03 -2.59 -0.83 0.04 1 -5.23 -2.41 0.04 -10.10 -3.34 0.07 2 -0.80 -0.37 -0.01 0.51 0.16 -0.01 3 -1.34 -0.63 -0.01 -1.41 -0.45 -0.01 4 -0.90 -0.42 -0.01 -3.05 -0.97 -0.01
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Contemporaneous relation
Explanatory power: 4 – 8% Fitted values: dE/P-vw
Std. dev. of earnings surprises = 0.25% Slope = –10.10 Two std. deviation shock –5% drop in prices
Historical Earnings change in top 25%: return 1% (s.e. =
1.7%) Earnings change in bottom 25%: return 7% (s.e. =
1.6%)
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Contemporaneous relation
Early overreaction No theory Not in firm returns
Movements in discount rates
Rt = d,t – r,t
Cash flow news vs. expected-return news
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Returns and past earnings Zero to negative No evidence of under-reaction Inconsistent with behavioral theories Results are robust
Alternative definitions of earnings Subperiods Annual returns and earnings Subsets of stocks (size, B/M terciles)
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Summary observations
Large portfolio Earnings more persistent Initial market reaction more negative Puzzling from a cashflow-news perspective
Small portfolio Reversal at lag 4 Negatively related to CRSP, but not own
returns
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Earnings and discount rates
Rt = d,t – r,td,t = cashflow newsr,t = expected-return news = discount-rate news
Returns and earningscov(dEt, Rt) = cov(dEt, d,t) – cov(dEt, r,t)cov(dEt, r,t)?
inflation and interest rates (+) consumption smoothing (–) changes in aggregate risk aversion (–)
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Earnings and the macroeconomy, 1970 – 2000: Correlations
Nominal dE Real dE
EW VW EW VW
TBILL 0.35 0.60 0.27 0.50 TERM -0.35 -0.52 -0.33 -0.52 DEF -0.59 -0.37 -0.66 -0.49
SENT 0.37 0.13 0.39 0.20
GDP 0.40 0.54 0.61 0.67 IPROD 0.67 0.65 0.72 0.74 CONS 0.29 0.42 0.53 0.52
dE = seasonally-differenced earnings Macro = annual changes or growth rates, ending in qtr t
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Earnings and the macroeconomy, 1970 – 2000
dEt = + TBILLt + TERMt + DEFt + dEt-1 + Nominal dE Real dE
EW VW EW VW
TBILL 0.04 0.04 0.02 0.03 1.39 2.72 0.73 1.78
TERM 0.00 -0.01 -0.01 -0.02 0.09 -0.29 -0.23 -0.69
DEF -0.55 -0.22 -0.64 -0.26 -4.95 -3.96 -5.70 -4.79
dEt-1 0.39 0.53 0.35 0.53 4.62 7.53 4.29 7.71
Adj. R2 0.49 0.62 0.53 0.62
Adj. R2 w/o AR1 0.41 0.44 0.46 0.43
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Controlling for discount rates
Two-stage approach
dEt = + TBILLt + TERMt +
DEFt + dEt-1 + Rt+k = + Fitted(dEt) + Residual(dEt) + et+k
Timing?
Rt Rt+1 Rt+2 Rt+3 Rt+4
dEt
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Returns and earnings, 1970 – 2000
Rt+k = + Fitted(dEt) + Residual(dEt) + et+k, Fitted dE Residual dE
k Slope T-stat Slope T-stat Adj. R2
EW 0 -6.86 -3.44 3.57 1.89 0.10 1 -5.01 -2.51 -3.02 -1.55 0.05 2 -2.93 -1.45 -2.44 -1.23 0.01 3 -4.20 -2.09 1.47 0.75 0.02 4 -1.55 -0.76 -4.53 -2.28 0.03 VW 0 -9.08 -3.27 0.76 0.23 0.07 1 -2.58 -0.95 -9.27 -2.84 0.05 2 -2.84 -1.02 2.30 0.69 0.00 3 -1.09 -0.39 -1.65 -0.49 -0.01 4 0.29 0.10 -2.53 -0.75 -0.01
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Annual dE/P, 1970 – 2000 Rt+k = + Fitted(dEt) + Residual(dEt) + et+k, Fitted dE Residual dE
k Slope T-stat Slope T-stat Adj. R2
EW 0 -4.49 -2.03 -2.30 -1.15 0.11 1 -0.64 -0.26 1.29 0.58 -0.06 2 2.19 0.88 0.71 0.32 -0.04 3 1.11 0.45 -0.27 -0.13 -0.07 VW 0 -5.86 -2.04 -3.97 -1.23 0.11 1 -1.19 -0.40 7.74 2.29 0.11 2 2.95 0.91 -1.75 -0.48 -0.04 3 1.41 0.44 0.71 0.20 -0.07
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How big are the effects?
Over the last 30 years, CRSP VWT portfolio Increased 6.5% in value in the quarters with
negative earnings growth Increased 1.9% in value in quarters with
positive earnings growth
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Conclusions
Market’s reaction to earnings surprises much different at the aggregate level Negative reaction to good earnings news Past earnings contain little (inconsistent) information
about future returns Investment strategy: Long in quarters when aggregate
earnings changes are negative Open questions
Do earnings proxy for discount rates? Is there a coherent behavioral story for the patterns?
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Richardson and Sloan (2003): External
Financing and Future Stock Returns Prior evidence: Market is sluggish in rationally
incorporating information in managers’ market timing motivation for external financing
Market timing: Raise funds when the firm is overvalued and repurchase shares when the firm is undervalued.
Slow assimilation of the information can be because of investors’ information processing biases
Sluggish reaction means opportunities for abnormal returns
How large are the returns to a trading strategy? What is the source of the abnormal returns? Is it related to the
use of proceeds from external financing? Richardson and Sloan: Examine returns to a trading rule
based on net external financing (not individual decisions like share repurchasing)
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Returns following external financing
Prior evidence Low returns following equity offerings, debt offerings,
and bank borrowings High returns following share repurchases Managers seem to time external financing
transactions to exploit mispricing Market’s immediate reaction to the financing decisions
is incomplete (underreaction to public announcements of voluntary decisions)
Market gradually reacts over the following one-to-three years – inconsistent with market efficiency and consistent with some of the information-processing biases
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Returns following external financing
Richardson and Sloan show that Net external financing generates a 12-month
abnormal return of about 16% (Table 5) The return is on long-minus-short position that has
a zero initial investment Long position is in firms that raise the least external
financing (i.e., repurchase shares or retire debt) Short position is in firms that raise the most
external financing – issue equity or debt or borrow from a bank
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Returns following external financing
Richardson and Sloan show that Use of the proceeds from external financing
matters (Table 6) Investment in operating assets generates highest
return on the zero-investment portfolio Suggests managers over-invest in assets Market fails to fully assimilate information in accruals
What are accruals? Earnings (X) = CF + Accruals (A) When you sell on credit, earnings increase, cash flow
does not, but accruals in the form of accounts receivables increase
Investment in operating assets is a form of accrual
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Returns following external financing
Acrobat Document
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Returns following external financing
External financing decisions as well as exceptional corporate performance (high sales growth or extreme decline) are all associated with large accruals A large increase in sales translates into a large
increase in receivables, so an accrual increase is associated with increased sales
Accruals also present opportunities to the management to manipulate them and/or create them fictitiously A fictitious dollar of sales and receivables accruals
contributes dollar for dollar to earnings before taxes and also enhances profit margin (because the cost of goods sold is not increased with fictitious sales)
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Returns following external financing
Since extreme performance or financing activities or fictitious sales are typically not sustainable, accruals revert
If investors suffer from information processing biases, do they recognize the time-series properties of accruals and its implications for future earnings?
In particular, does the market recognize that “The persistence of current earnings is decreasing in the magnitude of accruals and increasing in cash flows?”
Market overvalues accruals (i.e., fails to recognize that accruals-based earnings are not permanent)
Trading strategy implication: Long in low accrual stocks and short in high accrual stocks to generate above-normal performance.
Trading strategy based on external financing is based on accruals – raise capital means high accruals means go short
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Conclusions
Investors exhibit many behavioral biases If the biases are similar across individuals and arbitrage
forces are limited, then the behavioral biases can cause prices to deviate systematically from economic fundamentals
Recent attempts to test the effects of behavioral biases in stock price data
Aggregate earnings data and stock returns Individual firms’ financial data and stock returns
Stock returns associated with external financing decisions Stock returns due to investors’ alleged inability to process
information in accounting accruals Next set of issues
How large is the mispricing? Can it be exploited? What are the barriers to implementation and what are the implications for asset management?