Natural Forecasting, Asset Pricing, and Macroeconomic Dynamics

1. Natural Forecasting, Asset Pricing, and Macroeconomic Dynamics Andreas Fuster David Laibson Brock Mendel Harvard University May 2010

2. Financial crises Key ingredients (Kindleberger 1978) • Improving fundamentals • Rising asset prices • Rising leverage supporting consumption and investment • Falling asset prices and deleveraging • Banking crisis • Recession/Depression

3. Financial crises Key ingredients (Kindleberger 1978) • Improving fundamentals • Rising asset prices (“bubble”) • Rising leverage supporting consumption and investment • Falling asset prices and deleveraging • Banking crisis • Recession/Depression

4. Bubbles • Neo-classical economic view: • Non-rational bubbles don’t exist • Non-rational bubbles only appear to exist because of hindsight bias (fundamentals sometimes unexpectedly deteriorate) • Rational bubbles may exist in special circumstances (Tirole, 1985) • Today: • bubbles are (at least partially) not rational • bubbles explain macro dynamics

6. The Japanese Bubble

7. Dot com bubble Lamont and Thaler (2003) • March 2000 • 3Com owns 95% of Palm and lots of other net assets, but... • Palm has higher market capitalization than 3Com $Palm > $3Com = $Palm + $Other Net Assets

8. -$63 = (Share price of 3Com) - (1.5)*(Share price of Palm)

9. Four classes of explanations for the most recent crisis: • Savings glut (e.g., Bernanke 2003) • But see Laibson and Mollerstrom (2010): worldwide savings did not rise • Rational bubbles (e.g., Caballero et al 2006) • Agency problems • But see Connor, Flavin, and O’Kelly 2010: Ireland did not have exotic mortgages and CMO’s • Non-rational bubbles

10. Housing prices and trade deficits Germany Japan Turkey Laibson and Mollerstrom, 2010

11. Four classes of explanations for the most recent crisis: • Savings glut (e.g., Bernanke 2003) • But see Laibson and Mollerstrom (2010): worldwide savings did not rise • Rational bubbles (e.g., Caballero et al 2006) • Agency problems • But see Connor, Flavin, and O’Kelly 2010: Ireland did not have exotic mortgages and CMO’s • Non-rational bubbles

12. Lehman’s forecasts in 2005HPA = House Price Appreciation Source: Gerardi et al (BPEA, 2008)

13. Alan Greenspan • “While local economies may experience significant speculative price imbalances, a national severe price distortion seems most unlikely in the United States, given its size and diversity.” (October, 2004) • If home prices do decline, that “likely would not have substantial macroeconomic implications.” (June, 2005) • Though housing prices are likely to be lower than the year before, “I think the worst of this may well be over.” (October, 2006)

14. Four classes of explanations for the most recent crisis: • Savings glut (e.g., Bernanke 2003) • But see Laibson and Mollerstrom (2010): worldwide savings did not rise • Rational bubbles (e.g., Caballero et al 2006) • Agency problems • But see Connor, Flavin, and O’Kelly 2010: Ireland did not have exotic mortgages and CMO’s • Non-rational bubbles

15. Bubbles form: 1995-2007 • I’ll focus on the US • Related bubbles existed in many other countries • The US bubble had two main components: • Prices of publicly traded companies • Prices of residential real estate • And many minor contributors: • Prices of private equity • Commodities • Hedge funds

16. Fundamental Catalysts: 1990’s • End of the cold war • Deregulation • High productivity growth • Weak labor unions • Low energy prices ($11 per barrel avg. in 1998) • IT revolution • Low nominal and real interest rates • Congestion and supply restrictions in coastal cities

17. P/E ratio: Cambell and Shiller (1998a,b)Real index value divided by 10-year average of real earningsJan 1881 to April 2010 Dec 1999 Sept 1929 Jan 1966 July 1982 Dec 1920 Average: 16.34 Source: Robert Shiller

18. Real Estate in Phoenix and Las VegasJan 1987 – January 2010

19. Long-run horizontal supply curve Phoenix

20. Long-run horizontal supply curve Phoenix

21. Long-run horizontal supply curve 8miles

22. Long-run horizontal supply curve Bubble Demand SR Supply Price Demand LR Supply Quantity Arbitrage: Buy your house now for $400,000 or in 3 years at $300,000

23. “Over-shooting” Bubble Demand SR Supply Price Demand LR Supply DWL Quantity Arbitrage: Buy your house now for $400,000 or in 3 years at $200,000

24. S&P 500 Case-Shiller IndexJanuary 1987-January 2010 226.7 May 2009 April 2006 January 2010 January 1987 January 2000

25. Housing Prices Source: Robert Shiller

26. Household net worth divided by GDP1952 Q1 – 2008 Q4 Source: Flow of Funds, Federal Reserve Board ; GDP, BEA.

27. Today • A formal model of non-rational bubbles • Microfoundations • Testable predictions • Goal: Study non-rational expectations with a parsimonious and generalizable model.

28. Outline • Two building blocks • Natural forecasting • Hump-shaped impulse response • Tree model • Simulations, predictions, empirical evaluation • Counterfactuals • Extensions

29. Related Literature • Barberis, Shleifer, and Vishny (1998): extrapolative dividend forecasts • Barsky and De Long (1993): extrapolation and excess volatility • Benartzi (2001): extrapolation and company stock • Black (1986): noise traders • Campbell and Shiller (1988a,b): P/E ratio and return predictability • Choi (2006): extrapolation and asset pricing • Choi, Laibson, and Madrian (2009): positive feedback in investment • Cutler, Poterba, and Summers (1991): return autocorrelations • De Long, et al (1990): noise traders and positive feedback • Hong and Stein (1999): forecasting biases • Keynes (1936): animal spirits • LaPorta (1996): Growth expectations have insufficient mean reversion • Leroy and Porter (1981): excess volatility in stock prices • Lettau and Ludvigson (1991): W/C correlates negatively with future returns • Lo and MacKinlay (1988): variance ratio tests • Loewenstein, O’Donoghue, and Rabin (2003): projection bias • Piazessi and Schneider (2009): extrapolative beliefs in the housing market • Previterro (2001): extrapolative beliefs and annuity investment • Shiller (1981): excess volatility in stock prices • Summers (1986): power problems in financial econometrics • Tortorice (2010): extrapolative beliefs in unemployment forecasts

30. (a) Natural forecasting bias

31. Natural forecasting • Natural forecasting requires minimal memory • Natural forecasting has no free parameters • Natural forecasting nests: • random walk: • frictionless momentum on a surface:

32. (b) True data generating process with hump-shaped impulse response Impulse response functions

33. Hump-shaped impulse response ARIMA(p,1,q) ARIMA(0,1,Q)

34. Ln(Real GDP)Four-year horizon (quarterly data) ARIMA(0,1,4) ARIMA(1,1,0) ARIMA(0,1,12) ARIMA(0,1,8)

35. UnemploymentFour-year horizon (quarterly data) ARIMA(0,1,4) ARIMA(0,1,8) ARIMA(1,1,0) ARIMA(0,1,12)

36. Ln(Real earnings)Four-year horizon (quarterly data) ARIMA(1,1,0) ARIMA(0,1,4) ARIMA(0,1,12) ARIMA(0,1,8)

37. Ln(S&P Gross Return)Four-year horizon (monthly data) ARIMA(0,1,4) ARIMA(1,1,0) ARIMA(0,1,8) ARIMA(0,1,12)

38. Interacting Natural Forecasting and Hump-Shaped Impulse Responses Data generating process Natural forecasting model Best fit value for φ

39. Impulse response functions: 1 year θ = 1 θ = 0.75 θ = 0.5 θ = 0.25 θ = 0

40. Impulse response functions: 4 years θ = 1 θ = 0.75 θ = 0.5 θ = 0.25 θ = 0

41. 2. Illustrative Model • Equity tree, with dividends: • Labor tree (non-stochastic): yt • Quadratic preferences • Study limit in which curvature → 0 • but do not pass to the limit • Discount factor δ

42. Model continued • Elastic supply of foreign capital with gross return R. • Assume that δR=1. • Assume foreign agents don’t hold domestic capital • Home bias • Moral hazard • Adverse selection • Expropriation risk • Natural forecasting with weighting parameter θ

43. Consumption function

44. Natural forecasting asset pricing

45. Rational expectations asset pricing

46. Calibration

47. Data and Simulations (N=5000)