Quan%fying Behavioral Finance
Prof. Robert J. Shiller Yale University Q Group 50th Anniversary Mee?ng Washington DC, April 19, 2016 Google Ngrams 1800-­‐2008
From Textbook Psychology General and Applied Hugo Muensterberg 1915
Comparing Google NGRAMS 1800-­‐2008
Gillian TeL The Silo Effect 2015
•  US Managing Editor Financial Times •  Ph.D. anthropology •  Industrial age organiza?ons organized around func?onal departments, creates biases Shiller Irra%onal Exuberance Three Edi%ons
•  First Edi?on: 2000 at end of what I call the “Millennium Boom” •  Second Edi?on: 2005 during what I call “Ownership Society Boom,” 2003-­‐2007 •  Third Edi?on: 2015 during the ?me of what I call the “New Normal Boom” 2009-­‐2015 Akerlof and Shiller, Animal Spirits, 2009, Phishing for Phools 2015
Monthly Real U.S. Stock Prices and Real Earnings Jan 1871-­‐Mar 2016
U.S. Home Prices and Fundamentals 1890-­‐Dec 2015
The Basic Theory of Specula%ve Bubbles
•  Bubbles are driven ini?ally by an unusual confluence of an array of precipita?ng factors •  Many of these factors are stories, o\en human interest stories (narra?ve basis for human thinking) •  Some of these factors are naïve theories •  Bubbles reach epidemic propor?ons with amplifica?on mechanisms •
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Price-­‐to-­‐price feedback Price-­‐to-­‐GDP-­‐to-­‐price feedback Price-­‐to-­‐corporate-­‐earnings-­‐to-­‐price feedback Price-­‐to-­‐lending-­‐standards-­‐to price feedback Results of Individual Investor Survey: Stocks Are the Best Investment
Results of Home Buyer Survey: Real Estate Is the Best Investment
Individual and Ins%tu%onal Investor Survey: Valua%on Confidence
Results of Individual Investor Surveys: Buy-­‐on-­‐Dips Confidence Correlates with Stock Market Level
Some Relevant Principles of Psychology
•  Overconfidence, WYSIATI •  Wishful thinking •  Nonconsequentalist reasoning •  Selec?ve acen?on: William James 1890, (“ra?onal inacen?on” Sims 2003) •  Iden?ty, ego involvement •  Anchoring •  Representa?veness heuris?c Precipita%ng Factors for Millennium Bubble 1982-­‐2000
•  The World Wide Web •  Triumphalism •  Culture Favoring Business Success •  Republican Congress & Capital Gains Taxes •  Baby Boom •  Media Expansion •  Op?mis?c Analysts •  401(k) Plans •  Rise of Mutual Funds •  Decline of Infla?on •  Expanding Volume of Trade •  Rise of Gambling Opportuni?es Addi%ons and Dele%ons to Precipita%ng Factors for the Ownership Society Boom 2003-­‐7
Addi?ons: •  Ownership Society Theory •  Greenspan put Dele?ons: •  The World Wide Web a\er Dot-­‐Com burst •  Republican Congress (Senate ?ed in 2000 elec?ons, both houses went Democra?c in 2006 elec?on) •  Triumphalism faded with memories of Deng Xaioping (d. 1997) and Boris Yeltsin (d. 2007) Rather Different Precipita%ng Factors for the New Normal Boom 2009-­‐15
Addi?ons: •  End of depression scare •  Extremely loose monetary policy and QE1, QE2, QE3 •  New end-­‐of-­‐career anxie?es with stellar advance in informa?on technology •  Public acen?on drawn to income inequality Dele?ons: •  Democra?c Congress disappears: Republicans take House 2008 in and Senate in 2014 Crash Beliefs From Investor Surveys
William N. Goetzmann
Dasol Kim
Robert J. Shiller
Yale School of Management, Yale University Weatherhead School of Management, Case Western Reserve University
Yale University
News Data
•  ProQuest •  Search on high & low valence terms •  “Crash” and “Boom” & other antonyms •  Count of ar?cles containing these and other terms January 1, 1989 through January 22, 2016 •  Ex: “Good news” vs. “bad news” •  Condi?oning on: •  “stock market” •  “cause” Fear of a Crash
•  “What do you think is the probability of a catastrophic stock market crash in the U. S., like that of October 28, 1929 or October 19, 1987, in the next six months, including the case that a crash occurred in the other countries and spreads to the U. S.? (An answer of 0% means that it cannot happen, an answer of 100% means it is sure to happen.) Probability in U. S.:_______________%” 40% 35% 30% 25% 20% 15% 10% 5% 0% 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 SUBJECTIVE PROBABILITY OF A CRASH & ANNUALIZED VOLATILITY Average s urvey r esponses a bout t he probability o f a c rash i n t he n ext s ix months o n t he s cale o f O ctober 1 9, 1 987 or O ctober 2 8, 1 929 Ins?tu?onal 6 month crash probability Individual 6 month crash probability 40% 25% 35% 20% 30% 25% 15% 20% 10% 15% 10% 5% 5% 0% 0% annualized vola?lity minimum daily return in the year Ins?tu?onal 6 month crash probability Individual 6 month crash probability WORSTDAILY PERCENTAGE DROP IN THE DJIA IN THE YEAR SUBJECTIVE PROBABILITY OF A CRASH & ANNUALIZED VOLATILITY Average survey responses about the probability of a crash in the next six months on the scale of October 19, 1987 or October 28, 1929 Adding in extreme returns and volaTlity Behavioral Research
• Availability heuris?c • Tversky & Kahneman (1973), Akhtar, Faff, Oliver and Subrahmanyam, A. (2012) • Risk and affect • Johnson and Tversky (1983), Paul Slovic -­‐-­‐ the affect heuris?c (many) • Media influence on acen?on • Barber & Odean (2008), Tetlock (2007) • Nega?vity bias • Baumeister, Bratslavsky, Finkenauer and Vohs (2001) Number of ArTcles PosiTve/NegaTve 2182 Nega?ve market_crash_AND_stock_market 2268 Nega?ve stock_market_crash 1991 Nega?ve bad_news_AND_stock_market_AND_cause 195 Nega?ve market_crash_AND_stock_market_AND_cause 74 Nega?ve stock_market_crash_AND_cause 182 Nega?ve good_news_AND_stock_market 2342 Posi?ve market_boom_AND_stock_market 322 Posi?ve stock_market_boom 275 Posi?ve good_news_AND_stock_market_AND_cause 219 Posi?ve market_boom_AND_stock_market_AND_cause 18 Posi?ve stock_market_boom_AND_cause 14 Posi?ve Keyword bad_news_AND_stock_market “Crash”
•  Widely used in the 1920’s un?l now •  A high nega?ve affect word? •  “Boom” antonym? Google Ngram frequencies for "stock market boom" and “stock market crash" 1920 through 2008 stock market crash stock market boom 1.40 1.20 1.00 0.80 0.60 0.40 2005 2000 1995 1990 1985 1980 1975 1970 1965 1960 1955 1950 1945 1940 1935 1930 0.00 1925 0.20 1920 frequency in the Ngram American English 2012 corpus Tmes e15 1.60 “Stock Market Crash” Google NGRAMS 1800-­‐2008
“Stock Market Crash” Proquest News & Newspapers 1901-­‐2015
Newspaper reports “fears” •  Nega?vity bias for market-­‐associated ar?cles •  Posi?ve words less emphasized •  Cf. Diego Garcia (2015) PredicTng ArTcle Valence Dependent Variable: NetCount NetCount Count – (t) Count – (t) Count + (t) Count + (t) Return(t-­‐1) 3.239*** -­‐2.012*** 1.227** Return(t-­‐1))<10%) -­‐0.084** 0.095*** 0.011 Return(t-­‐1))>90%) 0.053 0.008 0.061** Return(t-­‐2) Return(t-­‐31,t-­‐2) VolaTlity(t-­‐31,t-­‐2) Count + (t-­‐31,t-­‐2) Count – (t-­‐31,t-­‐2) Day of Week FEs Month FEs N Adjusted R2 0.991 0.867 -­‐0.684 0.404* 0.374* -­‐0.584*** 4.277** 4.431** -­‐2.749* 0.628*** 0.633*** -­‐0.099** -­‐0.737*** -­‐0.736*** 0.677*** (0.045) YES YES 3430 12.17% (0.045) YES YES 3430 12.03% (0.034) YES YES 3430 13.32% -­‐0.513 0.306 0.355 -­‐0.536*** -­‐0.179 -­‐0.162 -­‐3.765** 1.528 0.666 -­‐0.107** 0.529*** 0.526*** 0.680*** -­‐0.060** -­‐0.056* (0.034) YES YES 3430 13.40% (0.030) YES YES 3430 6.60% (0.030) YES YES 3430 6.63% Bad News on the Front Page
Dependent Variable: Return(t-­‐1) NegaTve Front Page News Count Front,– (t) -­‐2.428*** NegaTve Back Pages News Count NotFront,– (t) -­‐0.237 PosiTve Front Page News PosiTve Back Pages News Count Front,
Count NotFront,
+ (t) + (t) 0.271 1.028** Placement Outcomes Depend on Sign of Returns 1.028 1.5 0.271 1 PREVIOUS DAY RETURN 0.5 0 -­‐0.5 Front Page -­‐0.237 Back Page -­‐1 -­‐1.5 -­‐2 -­‐2.5 -­‐2.428 Nega?ve News Posi?ve News Bad Day + “Crash” in news
•  Individual investors are more pessimis?c about a future crash when the market is down AND crash is in the news. Investor Subsample: Return Variable: Dependent Variable: Return(t) × Count – (t) Return(t) × Count + (t) Return(t) Count – (t) Count + (t) Return(t-­‐30,t-­‐1) VolaTlity(t-­‐30,t-­‐1) VIX(t-­‐1) Crash(t-­‐30,t-­‐1) Day of Week FEs Month FEs N Adjusted R2 Indiv. All Crash(t) -­‐0.877* -­‐0.978 0.160 0.004 -­‐0.030*** -­‐0.133** 0.354 0.001 0.302*** YES YES 4286 2.21% Inst. All Crash(t) -­‐0.154 0.263 -­‐0.186 0.000 -­‐0.017*** -­‐0.033 -­‐1.561* 0.002*** 0.290*** YES YES 5667 1.74% Front Page Emphasis
•  “Crash” on the front page explains the significance of the result. Investor Subsample: Return Variable: Dependent Variable: Return(t) × FRONT PAGE NEGATIVE COUNT Return(t) × BACK PAGES NEGATIVE COUNT Return(t) × FRONT PAGE POSITIVE COUNT Return(t) × BACK PAGES POSITIVE COUNT Return(t) FRONT PAGE NEGATIVE COUNT BACK PAGES NEGATIVE COUNT FRONT PAGE POSITIVE COUNT BACK PAGES POSITIVE COUNT Day of Week FEs Month FEs Control Variables N Adjusted R2 Indiv. All Crash(t) -­‐1.357* -­‐0.642 -­‐0.262 -­‐1.762* 0.168 0.004 0.005 -­‐0.029** -­‐0.024** YES YES YES 4286 2.15% Crash(t) -­‐0.883 0.742 -­‐0.580 0.725 -­‐0.209 0.003 -­‐0.004 -­‐0.023*** -­‐0.007 YES YES YES 5667 1.77% -­‐1.451** -­‐0.157 -­‐0.282 -­‐0.213 Using only “crash” and “boom” Return(t) × SPECIFIC “CRASH” WORD Return(t) × GENERIC “BAD” WORD Inst. All Indiv. Inst. S&P500 S&P500 Crash(t) -­‐1.610** -­‐0.649 0.065 -­‐1.741* 0.166 0.004 0.005 -­‐0.030** -­‐0.024** YES YES YES 4286 2.14% Crash(t) -­‐0.877 0.636 -­‐0.619 0.748 -­‐0.210 0.003 -­‐0.003 -­‐0.023** -­‐0.007 YES YES YES 5667 1.78% -­‐1.305* -­‐0.287 -­‐0.335 -­‐0.173 Indiv. DJIA Crash(t) -­‐1.947** -­‐0.923 0.550 -­‐1.511 0.076 0.003 0.005 -­‐0.029** -­‐0.023** YES YES YES 4286 2.20% -­‐1.393** -­‐0.598 Inst. DJIA Crash(t) -­‐0.611 0.542 -­‐0.688 0.955 -­‐0.303 0.004 -­‐0.002 -­‐0.023** -­‐0.008 YES YES YES 5667 1.78% -­‐0.230 -­‐0.113 A Sudden Shock
•  Earthquakes also give people nega?ve thoughts •  Johnson and Tversky (1983) effect •  Fear spills over contexts •  Affects probability assessment EARTHQUAKES
Investor Subsample: (1) Indiv. (2) Indiv. (3) Indiv. (4) Inst. (5) Inst. (6) Inst. Dependent Variable: Crash(t) Crash(t) Crash(t) Crash(t) Crash(t) Crash(t) Weak Magnitude(t-­‐30,t) 0.034** Strong Magnitude (t-­‐30,t) Control Variables Day-­‐of-­‐Week FEs Month FEs N Adjusted R2 YES YES YES 2961 1.73% 0.032** -­‐0.013 0.180 YES YES YES 2961 1.62% 0.153 YES YES YES 2961 1.77% YES YES YES 3352 0.77% -­‐0.012 -­‐0.034 YES YES YES 3352 0.75% -­‐0.023 YES YES YES 3352 0.74% GKS Conclusions
•  10% -­‐ 20% crash probabili?es are extreme •  Par?cipa?on, equity premium •  Media asymmetry in repor?ng •  Investor probabili?es respond to interac?on of event and emphasis •  Consistent with availability & affect heuris?cs •  Spillover from other events Overall Conclusions
•  Behavioral finance has many opportuni?es for quan?ta?ve research •  Growth of behavioral finance reflects a trend toward greater integra?on of social sciences •  Public’s thinking is heavily influenced by narra?ves, like the stories of one-­‐day events in 1929 and 1987 •  Psychologists’ evolving theories, like the nega?vity bias, availability heuris?c and affect heuris?c, are of demonstrated importance for finance