## Annex 1. Standard Testing Techniques for Speculative Bubbles ## Source details **Canonical URL:** [Annex 1. Standard Testing Techniques for Speculative Bubbles](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp14208.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp14208.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp14208.pdf.json) --- ### III. Introduction and motivation - Historical context of booms, bubbles and busts cited: Dutch Tulip Mania (1634—1637), French Mississippi Bubble (1719—20), South Sea Bubble (1720), first Latin American debt boom (1820s), railway manias in the United Kingdom (1840s) and United States (1870s), the Great Depression (1929), the Japanese Heisei bubble (late 1980s), emerging market booms and busts (1980s and 1990s), and the equity mania (late 1990s). - Recent policy concern: whether prolonged unusually accommodative monetary policies may be fermenting another asset price bubble. - Two-pillar surveillance approach introduced: - Pricing pillar: below average risk premia and valuation-based signals. - Quantities pillar: issuance, trading volumes, fund flows, and survey-based return projections. - Framework assessment: “currently points to mounting vulnerabilities in the riskier spectrum of credit markets.” ### II. Definitions and theoretical decomposition - Working definition of “bubble” in the paper: - (i) an asset price so high that no reasonable (probability-weighted) future scenario for fundamentals could justify it, and - (ii) expectation of future short-term price gains drives explosive self-fulfilling increases in prices (and possibly transaction volumes). - Typical decomposition of asset prices: - rational intrinsic yield component (discounted future cash flows, stationary), - irrational bubble component (expectations of capital gains independent of fundamentals, non-stationary). - Behavioral/social perspectives emphasize broad participation and feedback mechanisms (Mackay, Keynes, Kindleberger, Shiller). ### II. A review of measurement and inference issues - Core measurement and inference problems: - How large and how persistent must deviations from fundamental-based models be to label “speculative” or “irrational”? - The joint hypothesis problem: uncertainty about correct specification of the fair-value model. - Real-time identification is difficult; certainty about ex-ante projections only available ex-post. - Examples of definitional approaches summarized (Hirshleifer, Harrison and Kreps, Kindleberger, Stiglitz, Flood and Garber, Shiller, Siegel, Asness). ### Box 1 / Box 2 — Problems in formal tests of speculative bubbles (empirical challenges) - Empirical testing limitations and trade-offs: - Threshold selection tradeoff: high thresholds → more Type 1 errors (failing to predict busts); low thresholds → more Type 2 errors (false warnings). - Small sample sizes for rare events. - Stability of estimated coefficients (in-sample vs. out-of-sample). - Difficulty quantitatively accounting for pervasive behavioral/social phenomena. - Misspecified fundamentals may be mistaken for bubbles; for every test finding a bubble another paper may dispute it. - Quotation on skepticism: Eugene Fama: “I don’t even know what a bubble means. These words have become popular... They have to be predictable phenomena... It’s easy to say prices went down, it must have been a bubble, after the fact...” - Concluding empirical assessment (Gurkaynak, 2005): “Bubble tests do not do a good job of differentiating between misspecified fundamentals and bubbles. This is not only a theoretical concern: For every test that ‘finds’ a bubble, there is another paper that disputes it ... The bubble tests teach us little about whether bubbles really exist or not.” ### The Pricing Pillar: empirical findings and diagnostics - Empirical observations: - Time variation in discount rates is a dominant source of asset price variation; stock returns are strongly negatively correlated with contemporaneous changes in risk premia. - Changes in long-term risk-free interest rates or long-term growth expectations have a more muted relationship with stock returns. - Predictive properties of valuation measures: - Based on annual data for 1953-2013, valuation measures have modest explanatory power over one-year returns; explanatory power increases with longer holding periods. - In years preceding the three largest crashes for each major asset class, risk premiums declined to unusually low levels—around 1 to 2 standard deviations below the long-term average. - After busts, risk premiums rapidly reverted to more normal levels over the next two years. - Typical lead times from peak to recession onset after crashes: - Equities: six months. - Housing: eight months. - Credit and Treasuries: around two years. - Definitions and samples: - Crashes defined as largest decline in real (equity and housing) or excess (credit and Treasury) total return terms measured over a three-year observation window (rolling monthly). - Sample periods: stocks and BBB credit commence in 1924; housing and Treasuries commence in 1953; valuation-based asset return predictability sample: 1953-2013. - Recession dating based on NBER classification. - Notable empirical correlation: - Correlation between rent/price ratio and transaction volumes: -0.56 (U.S. housing 1969-2014, Figure 2). ### Cross-country and cross-asset assessments (empirical patterns) - Four broad patterns: 1. Little evidence of a synchronized valuation bubble across world equity markets: - Equity sample: data commence 1989; sample consists of 15 developed and 10 emerging markets. - Market-implied real cost of equity broadly in line with historical norms and considerably above levels that preceded large busts in the late 1990s and mid-2000s. 2. Long-term sovereign bond yields unusually low relative to long-term expectations of real growth and inflation: - Sovereign bond risk premia sample: 15 developed and 9 emerging markets; sample begins 1989. - Average ‘Wicksellian’ bond risk premium (5 year rate, 5 years forward, minus consensus estimates for growth and inflation) is around half a standard deviation below average. - Of the 24 countries examined, 18 have bond risk premiums below their historical average. 3. Simultaneous below-average required returns across major U.S. asset classes: - No single asset class close to two standard deviations, but simultaneous below-average returns has occurred for just 5 percent of the post-1953 sample. 4. Low grade U.S. corporate bonds are the most stretched relative to fundamental anchors and history: - Within high yield securities, pricing is most aggressive at the bottom of the capital structure. - Single-B and triple-C rated corporate bonds have spread cushions that would be entirely wiped out in the event of an average default cycle over the next five years. ### Sensitivity example: discount rates and perpetuity valuation - Gordon Growth Model example (infinitive-lived asset paying cash flow of 100): - Discount rate increases in increments of 50 basis points. - If the discount rate increases 50 basis points, from 1 to 1.5 percent, the asset fair value collapses 33 percent (from 10,000 to 6,667). - Implication: minor decreases in long-run discount rate assumptions can justify elevated asset prices and thus reject the hypothesis that a speculative bubble exists. ### Annex 2 — Two-pillar operational surveillance: quantities pillar findings - Purpose and rationale: - Non-price quantity data capture risk-taking and vulnerabilities that valuations may miss; “top down” pricing models are subject to estimation error. - Quantities pillar components: - (i) Quantity and quality of capital market issuance - (ii) Trading volumes and transaction activity - (iii) Investor fund flows - (iv) Investor surveys of return expectations - Key issuance statistics and shifts (U.S. 2000–2005 and related data): - U.S. subprime mortgage issuance rose from $100 billion to more than $600 billion between 2000 and 2005. - Subprime share of total U.S. mortgage origination rose from 6.9 percent to 20.1 percent. - Private-label residential MBS issuance increased from $150 billion to $1.2 trillion between 2000 and 2005. - Private-label RMBS share of total MBS issuance increased from 18 percent to 56 percent. - Between 2000 and the onset of the crisis, global issuance of CDOs increased more than six times to $1 trillion; issuance of CDO-squared increased eleven-fold to $300 billion. - Historical issuance examples: - LBO values: just under $1 billion (1980) → over $60 billion (1988) → below $20 billion (following year). - Trend pace of net dilution: 5 percent (late 1920s), and 3 percent of market capitalization (late 1990s). - Cyclically-adjusted price-earnings multiple peaks: 33 times and 47 times. - Empirical relationships: - High and/or rising share of debt issuance from lower quality firms has strong predictive power over future corporate bond returns (Greenwood and Hanson, 2013). - U.S. data since 1965: required return on stocks (an average of three model outputs) is a statistically significant predictor (at the 1 percent level) of net equity issuance the following year. - Recent issuance patterns (paper timeframe): - High yield bond issuance running at more than double the pace of pre-crisis levels in and outside the U.S.; relative share rising. - U.S. leveraged loan issuance at new highs in absolute terms and approaching new highs in relative terms. - European leveraged finance issuance approaching pre-crisis levels. - Marked increase in covenant-lite loans and payment-in-kind notes. - Sovereign bond issuance by first-time issuers with an average sub-investment grade rating: volumes and number of new issuers over the past four years more than double those in the preceding four years. - Equity markets: aggregate U.S. stock buybacks continue; global IPO and M&A activity remain below previous cyclical highs. ### Trading / Transaction Volumes — Key findings - Elevated trading activity often coincides with large price booms (Roaring 1920s, Japan late 1980s, U.S. technology bubble late 1990s, China mid-2000s). - U.S. mortgage-backed security market: average daily trading volumes increased five-fold in absolute terms between 2000 and 2008, and doubled relative to lower-risk Treasury and corporate bond markets prior to the crisis. - Current patterns (paper timeframe): - Trading volumes most elevated by historical standards for low grade credit securities. - Cash high yield market trading volumes increased substantially in absolute terms and relative to investment grade credit volumes. - ETF trading volumes for high yield and leveraged loan securities have picked up strongly. - Growth in equity trading volumes subdued across world equity markets despite price rallies. ### Investor Fund Flows — Key findings - Fund flows can amplify price moves via performance-chasing and herding, especially in small or illiquid asset classes. - Empirical patterns: - Average z-score of cumulative three-year flows across asset classes tends to rise and peak around one standard deviation above average just prior to a large decline; in the bust, cumulative flows fall to more than one standard deviation below average before trough. - Positive contemporaneous correlation between fund flows and risky asset class returns; returns in emerging market debt and equity tend to lead fund flows by one quarter; these patterns absent for Treasuries. - Fund flows stronger than usual for emerging markets and high yield immediately after strongest-ever returns—suggesting performance chasing; absent in Treasuries. - Post-2008 flow patterns: - Largest proportional increases (relative to assets under management) occurred in emerging market local currency bonds, and long- and short-term investment grade corporate bonds in developed markets. - Sharp increase in flows for emerging market local currency bonds likely exacerbated the ‘taper tantrum’ in spring 2013. - In developed fixed income, flows into short-duration investment grade credit and high yield continue to be strong. - Equity flows: flows into emerging market equities surged 40 percent (relative to assets under management) in the first two years after the crisis, then tracked sideways; only over the past 18 months have fund flows picked up to developed market equities following six years of cumulative decline. - Smallest (least liquid) asset classes received proportionally largest fund flows over the past five years. ### Surveys of Return Expectations — Key findings - Survey data reveal extrapolative expectations that can diverge from objective measures: - At the peak of the 1990s equity bubble, investor return expectations (Survey of Professional Forecasters, Duke CFO Survey) were around three times higher than the cyclically-adjusted earnings yield. - Just prior to late-2007 market peak, survey-based expectations were rising and well above the earnings-yield measure. - Current situation (paper timeframe): - Survey-based estimates of expected returns appear relatively benign; survey-based return expectations are presently near historic lows (latest survey estimates are at Q1-2014). ### Concluding remarks and policy implications - The two-pillar framework (price + quantities) is a promising starting point for early warning surveillance of asset market excesses. - Consistent picture of aggressive risk-taking emerges primarily in lowly-rated U.S. corporates: - Credit spreads for lowly-rated U.S. corporates are below levels required to compensate investors for an average default cycle. - Quantity of issuance is unusually high and composition is deteriorating in quality-adjusted terms. - Relative trading volumes for lowly-rated securities are elevated. - Fund flows for relatively illiquid credit securities have been particularly strong. - Policy recommendations (implied and explicit): - Policy makers and regulators should be attuned to any further deterioration in underwriting standards. - Where possible, take steps to ensure the post-crisis financial infrastructure is braced to accommodate a material re-pricing in credit risk. - Future research agenda: examine appropriate policy responses to bubbles (monetary, macroprudential, and new tools for the asset management industry); explore formal weighting schemes between price and non-price data for early warning (data limitations precluded this here). ### Selected exact empirical points and illustrative spread statistics - Discount rate example increments: 50 basis points. - Discount rate example change: from 1 to 1.5 percent. - Fair value change in example: collapses 33 percent (from 10,000 to 6,667). - Valuation-based asset return predictability sample: 1953-2013. - Stocks and BBB credit data commence: 1924. - Housing and Treasuries data commence: 1953. - Equity cross-country sample: data commence 1989; 15 developed and 10 emerging markets. - Sovereign bond risk premia sample: 15 developed and 9 emerging markets; sample begins 1989. - Simultaneous stretched-valuations occurrence: 5 percent of the post-1953 sample. - Risk premium deviations preceding crashes: around 1 to 2 standard deviations below the long-term average. - Recession lead times after busts: - Equities: six months. - Housing: eight months. - Credit and Treasuries: around two years. - Issuance and market structure statistics: - U.S. subprime mortgage issuance: $100 billion → more than $600 billion (2000–2005). - Subprime share of total U.S. mortgage origination: 6.9 percent → 20.1 percent. - Private-label RMBS issuance: $150 billion → $1.2 trillion (2000–2005). - Private-label RMBS share of total MBS issuance: 18 percent → 56 percent. - CDO global issuance: increased more than six times to $1 trillion. - CDO-squared issuance: increased eleven-fold to $300 billion. - LBO values: just under $1 billion → over $60 billion → below $20 billion (1980 → 1988 → following year). - Trend pace of net dilution: 5 percent (late 1920s), 3 percent (late 1990s). - Cyclically-adjusted P/E multiple peaks: 33 times and 47 times. - Example breakeven/current spread statistics (as labeled in figures): - US BBB-rated Corporate Bonds: Current Spread (83bps), Average Spread (172bps), Breakeven Spread in an Average Default Cycle (25bps), Breakeven Spread in the Worst Default Cycle (78bps). - US Leveraged Loans: Current Spread (441bps), Average Spread (583bps), Breakeven Spread in an Average Default Cycle (113bps), Breakeven Spread in the Worst Default Cycle (263bps). - US B-rated Corporate Bonds: Current Spread (293bps), Average Spread (439bps), Breakeven Spread in an Average Default Cycle (392bps), Breakeven Spread in the Worst Default Cycle (763bps). - US CCC-rated Corporate Bonds: Current Spread (673bps), Average Spread (1185bps), Breakeven Spread in an Average Default Cycle (807bps), Breakeven Spread in the Worst Default Cycle (1860bps). *IMF Working Paper — Annex 1. Standard Testing Techniques for Speculative Bubbles (excerpt).* ### Annex 1. Standard Testing Techniques for Speculative Bubbles ...........................................39 ### Annex 1. Standard Testing Techniques for Speculative Bubbles ### III. Introduction and motivation - Financial history is characterized by booms, bubbles and busts, with historical episodes cited including the Dutch Tulip Mania (1634—1637), the French Mississippi Bubble (1719—20), the South Sea Bubble (1720), the first Latin American debt boom (1820s), railway manias in the United Kingdom (1840s) and United States (1870s), the Great Depression (1929), the Japanese Heisei bubble (late 1980s), emerging market booms and busts (1980s and 1990s), and the equity mania (late 1990s). - Recent concerns center on whether prolonged use of unusually accommodative monetary policies may be fermenting another asset price bubble. - The paper introduces a simple two-pillar approach to bubble surveillance based on both price and quantity data in capital markets: - Pricing pillar: below average risk premia and valuation-based signals. - Quantities pillar: issuance, trading volumes, fund flows, and survey-based return projections. - Based on historical comparisons, the framework currently points to mounting vulnerabilities in the riskier spectrum of credit markets. ### II. Definitions and theoretical decomposition - “Bubble” used in this paper in the general sense of: - (i) an asset price so high that no reasonable (probability-weighted) future scenario for fundamentals could justify it, and - (ii) where the expectation of future short-term price gains drives explosive self-fulfilling increases in prices (and possibly transaction volumes). - Bubble models typically decompose asset prices into: - a rational intrinsic yield component (based on discounted future cash flows), and - an irrational bubble component (based on expectations of future capital gains independent of fundamentals). - The fundamentally-derived cash flow yield is characterized as a stationary process; the irrational bubble component is characterized as non-stationary. - Alternative behavioral and social perspectives emphasize broad societal participation and feedback mechanisms (e.g., Mackay, Keynes, Kindleberger, Shiller). ### II. A review of measurement and inference issues - Core measurement and inference problems highlighted: - How large must the deviation from fundamental-based models be to be considered “speculative” or “irrational”? - For how long must the discrepancy between model-predicted and observed prices persist? - The joint hypothesis problem: uncertainty about whether the fair-value model used to label a bubble is correctly specified. - Real-time identification is difficult; absolute certainty about whether optimistic ex-ante projections were justified is only available ex-post. - Definitions and tests vary across literature; examples of definitional approaches: - Hirshleifer (1977): speculation as purchase for later resale rather than use. - Harrison and Kreps (1978): speculative behavior linked to resale option value. - Kindleberger (1987): speculative bubble as a continuous sharp rise that generates expectations of further rises and attracts new buyers. - Stiglitz (1990): price high today only because investors believe selling price will be high tomorrow when fundamentals do not justify it. - Flood and Garber (1994): positive relationship between price and expected rate of change that implies similar relationship with actual rate of change. - Shiller (2003): behavioral feedback where rising prices beget more price increases. - Siegel (2003): ex-post criterion—realized return over future period inconsistent by more than two standard deviations from expected return given historical risk characteristics. - Asness (2014): bubble should indicate a price that no reasonable future outcome can justify. ### Box 1 — Problems in formal tests of speculative bubbles (empirical challenges) - Historical and conceptual context: - Early descriptive accounts emphasize mass participation and “madness of crowds” (Mackay, 1841) and the distinction between forecasting the market’s psychology and forecasting asset yields (Keynes, 1936). - Two historical views of speculation: risk-transference (Keynes, Hicks) and the knowledgeable-forecasting hypothesis (Working, Rockwell). - Empirical testing limitations and trade-offs: - Threshold selection tradeoff between Type 1 and Type 2 errors: - Setting thresholds too high increases likelihood of failing to predict subsequent busts (Type 1 errors). - Setting thresholds too low increases frequency of false warnings that do not materialize (Type 2 errors). - Problems of small sample sizes (relatively rare events). - Stability of estimated coefficients (in-sample vs. out-of-sample performance). - Difficulty quantitatively accounting for pervasive irrational behavioral/social phenomena emphasized in descriptive accounts. - Potential for misspecified fundamentals to be mistaken for bubbles; bubble tests struggle to differentiate between misspecified fundamentals and genuine bubbles. - Quotation illustrating skepticism of bubble definitions and predictability: - Eugene Fama: “I don’t even know what a bubble means. These words have become popular. I don’t think they have any meaning ... They have to be predictable phenomena ... It’s easy to say prices went down, it must have been a bubble, after the fact. I think most bubbles are twenty-twenty hindsight. ... They are typically right and wrong about half the time ... I didn’t renew my subscription to the The Economist because they use the word bubble three times on every page. People have become entirely sloppy.” - Concluding empirical assessment: - “Bubble tests do not do a good job of differentiating between misspecified fundamentals and bubbles. This is not only a theoretical concern: For every test that ‘finds’ a bubble, there is another paper that disputes it ... The bubble tests teach us little about whether bubbles really exist or not.” (Gurkaynak, 2005, p. 27) ### Policy-relevant implications and surveillance approach - Real-time policy challenges: - Authorities must balance false negatives and false positives when setting alert thresholds for asset price misalignments. - Ex-post measures (e.g., Siegel’s two standard deviation rule) cannot provide the real-time certainty policymakers need. - Two-pillar surveillance framework rationale: - Cross-referencing price-based signals (valuation and risk premia) with quantity-based signals (issuance, trading volumes, fund flows, survey-based return projections) can provide richer, complementary information. - Historical evidence suggests some of the largest boom-bust episodes combine signals from both pillars: unusually low risk premia and elevated issuance/trading/fund-flow activity. - Current assessment derived from this framework: - The framework “currently points to mounting vulnerabilities in the riskier spectrum of credit markets.” *IMF Working Paper — Annex 1. Standard Testing Techniques for Speculative Bubbles* ### Box 2. Problems in Formal Tests of Speculative Bubbles ### _wp14208 - Box 2. Problems in Formal Tests of Speculative Bubbles ### Problems in formal hypothesis testing of speculative bubbles - Acceptance or rejection of a speculative bubble is contingent on the model of proposed fundamentals and its embedded assumptions. - Fundamental-based models rely on unobservable estimates and are prone to misspecification (IMF, 2003). - A misspecified model (especially those with omitted variables) may lead to the mistaken conclusion that a bubble exists; the finding of a bubble can be a catch-all for asset price movements not captured by the fundamental model (Hamilton and Whiteman, 1985; West, 1987). - This relates to the joint hypothesis problem: tests for bubbles must assume a baseline equilibrium pricing model for an efficient market; what appears to be a speculative bubble may not be one if the baseline equilibrium pricing model is incorrect (Fama, 1991; Campbell and others, 1997). - The burden of proof may reside with proponents of the speculative bubble thesis to demonstrate that their model of fundamentals is valid (Flood and Garber, 1994). - Rosser (2000, p. 107): “the most fundamental (problem) is determining what is fundamental.” ### Sensitivity example: discount rates and perpetuity valuation - Example based on Gordon Growth Model for a perpetuity where present value = next year’s cash flow / (risk free rate + risk premium – expected growth rate). - Asset paying a cash flow of 100 (infinitive-lived asset) — sensitivity to discount rate increases in increments of 50 basis points: - If the discount rate increases 50 basis points, from 1 to 1.5 percent, the asset fair value collapses 33 percent (from 10,000 to 6,667). - Implication: minor decreases in long-run discount rate assumptions can justify elevated asset prices and thus reject the hypothesis that a speculative bubble exists. ### Operationalizing surveillance: a two-pillar framework - Real-time identification of bubbles is difficult; bubbles can only be identified with certainty ex-post. - Policy makers should survey and cross-validate information from a variety of approaches and metrics; supplemental non-price data can enrich understanding of risk taking behavior. - Proposed surveillance approach: two complementary pillars - Pricing pillar: captures swings in risk premia or required returns. - Quantity pillar: tracks issuance, transaction volumes, investor fund flows, and investor surveys. - Periods where (i) risk premiums have been compressed to abnormally low levels, and (ii) issuance, trading activity, fund flow data, and survey-based return expectations are unusually elevated, warrant particular attention from policy makers. - Framework can be applied to capital markets and real estate markets. - Example: U.S. housing bubble of the mid 2000s was a three standard deviation event in valuation terms and in (non-price) quantity terms. ### The Pricing Pillar: empirical findings and diagnostics - Historical shift: early theories emphasized changes in expected cash flows with constant discount rates; later work shows time variation in discount rates as dominant source of asset price variation. - Empirical determinants of stock returns: - Stock returns are strongly negatively correlated with contemporaneous changes in risk premia. - Relationship between stock returns and changes in long-term risk-free interest rates or long-term growth expectations is considerably more muted. - For surveillance, valuation measures should: - Demonstrate predictive power over subsequent returns. - Display unusual behavior preceding large asset busts. - Based on six decades of annual data (1953-2013), valuation measures have modest explanatory power over one-year returns, increasing with longer holding periods. - In the years preceding the three largest crashes for each major asset class, risk premiums declined to unusually low levels—around 1 to 2 standard deviations below the long-term average (Figures 5 and 6). - After busts, risk premiums rapidly reverted to more normal levels over the next two years. - Crashes were followed by recessions with the following average lead times from peak to recession onset: - Equities: six months after a peak. - Housing: eight months after a peak. - Credit and Treasuries: around two years after a peak. - Definitions and sample notes: - Crashes defined as largest decline in real (equity and housing) or excess (credit and Treasury) total return terms, measured over a three-year observation window (rolling monthly). - Sample periods: stocks and BBB credit commence in 1924; housing and Treasuries commence in 1953. - Recession dating based on NBER classification. ### Cross-country and cross-asset assessments (empirical patterns) - Four broad patterns highlighted from Figures 8-14: 1. Little evidence of a synchronized valuation bubble across world equity markets: - Sample: 15 developed and 10 emerging markets over past 25 years. - Market-implied real cost of equity broadly in line with historical norms and considerably above levels that preceded large busts in the late 1990s and mid-2000s. 2. Long-term sovereign bond yields unusually low relative to long-term expectations of real growth and inflation: - Sample: 15 developed and 9 emerging markets over past 25 years. - Average ‘Wicksellian’ bond risk premium (5 year rate, 5 years forward, minus consensus estimates for growth and inflation) is around half a standard deviation below average. - Of the 24 countries examined, 18 have bond risk premiums below their historical average. 3. Simultaneous below-average required returns across major U.S. asset classes: - No single asset class is egregiously stretched (i.e., close to two standard deviations), but simultaneous below-average returns has occurred for just 5 percent of the post-1953 sample. 4. Low grade U.S. corporate bonds are the most stretched relative to fundamental anchors and history: - Within high yield securities, pricing is most aggressive at the bottom of the capital structure. - Single-B and triple-C rated corporate bonds have spread cushions that would be entirely wiped out in the event of an average default cycle (blue line) over the next five years. ### Key numeric and sample facts (preserved exactly) - Discount rate increase example increments: 50 basis points. - Discount rate example change: from 1 to 1.5 percent. - Fair value change in example: collapses 33 percent (from 10,000 to 6,667). - Sample periods and data windows: - Valuation-based asset return predictability: 1953-2013. - Stocks and BBB credit data commence: 1924. - Housing and Treasuries data commence: 1953. - Equity sample for cross-country cost of equity: data commence 1989; sample consists of 15 developed and 10 emerging markets. - Sovereign bond risk premia sample: 15 developed and 9 emerging markets; sample begins 1989. - Simultaneous stretched-valuations occurrence: 5 percent of the post-1953 sample. - Risk premium deviations preceding crashes: around 1 to 2 standard deviations below the long-term average. - Recession lead times after busts: - Equities: six months. - Housing: eight months. - Credit and Treasuries: around two years. - Empirical correlations and R2 findings referenced (figures and regression results summarized in text): - R2 for valuation regressions increase with holding periods (charted for 1 yr to 5 yrs across equities, housing, investment grade credit, Treasuries). - Correlation between rent/price ratio and transaction volumes: -0.56 (U.S. housing 1969-2014, Figure 2). *Source: _wp14208 - Box 2. Problems in Formal Tests of Speculative Bubbles (excerpt).* ### Annex 2. ### Annex 2. ### Overview - Proposes a two-pillar surveillance framework combining: (i) price-based asset valuation models; and (ii) a “quantities pillar” using non-price data (quantity and quality of issuance, trading volumes, investor fund flows, investor surveys) as a complement to valuation-based surveillance. - Argues asset pricing models should be the place to begin, not finish, surveillance work, because forward-looking inputs are unobservable and small discount rate changes can have large valuation effects. ### The Quantities Pillar — Purpose and Rationale - Motivations: - Non-price quantity data can capture risk-taking and vulnerabilities that asset valuations may miss. - “Top down” asset pricing models are subject to considerable estimation error; quantity indicators can cross-reference and enrich surveillance. - Components of the proposed quantities pillar: - (i) Quantity and quality of capital market issuance - (ii) Trading volumes and transaction activity - (iii) Investor fund flows - (iv) Investor surveys of return expectations ### Quantity and Composition of Capital Market Issuance — Key Findings and Evidence - Pre-crisis U.S. securitization and mortgage market shifts (2000–2007): - U.S. subprime mortgage issuance rose from $100 billion to more than $600 billion between 2000 and 2005. - Subprime share of total U.S. mortgage origination rose from 6.9 percent to 20.1 percent. - Private-label residential MBS issuance increased from $150 billion to $1.2 trillion between 2000 and 2005. - Private-label RMBS share of total MBS issuance increased from 18 percent to 56 percent. - Between 2000 and the onset of the crisis, global issuance of CDOs increased more than six times to $1 trillion; issuance of CDO-squared increased eleven-fold to $300 billion. - Historical examples linking issuance composition to future stress: - LBO values: grew from just under $1 billion in 1980 to over $60 billion in 1988, then collapsed below $20 billion in the credit market bust the following year. - Trend pace of net dilution: 5 percent in the late 1920s, and 3 percent of market capitalization in the late 1990s; cyclically-adjusted price-earnings multiple peaked at 33 times and 47 times respectively. - Empirical relationships: - High and/or rising share of debt issuance from lower quality firms has strong predictive power over future corporate bond returns (Greenwood and Hanson, 2013). - U.S. data since 1965: the required return on stocks (an average of three model outputs) is a statistically significant predictor (at the 1 percent level) of net equity issuance the following year. - Recent patterns (as of the paper’s data): - High yield bond issuance is running at more than double the pace of pre-crisis levels in and outside the U.S. market, and the relative share of high yield issuance is also rising. - U.S. leveraged loan issuance is at new highs in absolute terms and approaching new highs in relative terms. - European leveraged finance issuance (leveraged loans and high yield) approaching pre-crisis levels. - Marked increase in covenant-lite loans and payment-in-kind notes. - Sovereign bond issuance by first-time issuers with an average sub-investment grade rating: both issuance volumes and number of new issuers over the past four years has been more than double that recorded in the preceding four years. - Equity markets: companies in the U.S. continue to buy back stock on aggregate; global IPO and M&A activity remain below previous cyclical highs. ### Trading / Transaction Volumes — Key Findings - Elevated trading activity often coincides with large price booms; classical theory would predict low volume if beliefs and information were homogeneous. - Historical episodes linking price booms and trading frenzies: - Roaring 1920s, Japan late 1980s, U.S. technology bubble late 1990s, China mid-2000s — all show strong co-movement of real price growth and trading volume growth. - U.S. mortgage-backed security market: average daily trading volumes increased five-fold in absolute terms between 2000 and 2008, and doubled relative to lower-risk Treasury and corporate bond markets prior to the crisis. - Current (paper’s timeframe) patterns: - Trading volumes are most elevated by historical standards for low grade credit securities. - Cash high yield market trading volumes have increased substantially in absolute terms and relative to investment grade credit volumes. - ETF trading volumes for high yield and leveraged loan securities have picked up strongly. - Growth in equity trading volumes has been subdued across world equity markets despite stock price rallies — a contrast with the late 1990s. ### Investor Fund Flows — Key Findings - Fund flows can amplify price moves via performance-chasing and herding, particularly in small or illiquid asset classes. - Empirical patterns: - Average z-score of cumulative three-year flows across asset classes tends to rise over years and peak around one standard deviation above average just prior to a large decline in asset prices; in the bust, cumulative flows fall to more than one standard deviation below average before markets trough. - Positive contemporaneous correlation between fund flows and risky asset class returns; returns in emerging market debt and equity tend to lead fund flows by one quarter. These patterns are absent for Treasuries. - Fund flows have been considerably stronger than usual for emerging markets and high yield credit immediately after those asset classes recorded their strongest ever returns — suggesting performance chasing; absent in Treasuries. - Post-2008 fund flow patterns: - Largest proportional increases (relative to assets under management) occurred in emerging market local currency bonds, and long- and short-term investment grade corporate bonds in developed markets. - Sharp increase in flows for emerging market local currency bonds likely exacerbated the ‘taper tantrum’ in spring 2013. - In developed fixed income markets, flows into short-duration investment grade credit and high yield continue to be strong. - Equity flows: flows into emerging market equities surged 40 percent (relative to assets under management) in the first two years after the crisis, then tracked sideways; only over the past 18 months have fund flows picked up to developed market equities following six years of cumulative decline. - The smallest (least liquid) asset classes received the proportionally largest fund flows over the past five years, raising the risk of disorderly movements if liquidity shocks occur. ### Surveys of Return Expectations — Key Findings - Survey data can reveal extrapolative investor expectations that diverge from objective measures; after run-ups subjective expectations are often high while objective expected returns are low. - Historical comparisons: - At the peak of the 1990s equity bubble, investor return expectations (Survey of Professional Forecasters, Duke CFO Survey) were around three times higher than the cyclically-adjusted earnings yield. - Just prior to late-2007 market peak, survey-based expectations were rising and well above the earnings-yield measure. - Current (paper’s timeframe) situation: - Survey-based estimates of expected returns appear relatively benign; survey-based return expectations are presently near historic lows (latest survey estimates are at Q1-2014). ### Concluding Remarks and Policy Implications - The two-pillar framework (price + quantities) provides a promising starting point for early warning surveillance of asset market excesses. - Current consistent picture of aggressive risk-taking emerges primarily in lowly-rated U.S. corporates: - Credit spreads for lowly-rated U.S. corporates are below levels required to compensate investors for an average default cycle. - Quantity of issuance is unusually high and composition is deteriorating in quality-adjusted terms. - Relative trading volumes for lowly-rated securities are elevated. - Fund flows for relatively illiquid credit securities have been particularly strong. - Policy recommendations (implied and explicit): - Policy makers and regulators should be attuned to any further deterioration in underwriting standards. - Where possible, take steps to ensure the post-crisis financial infrastructure is braced to accommodate a material re-pricing in credit risk. - Future research agenda: examine appropriate policy responses to bubbles (monetary, macroprudential, and new tools for the asset management industry); explore formal weighting schemes between price and non-price data for early warning (data limitations precluded this here). ### Selected Exact Empirical Points and Statistics Reported - U.S. subprime mortgage issuance: $100 billion → more than $600 billion (2000–2005). - Subprime share of total U.S. mortgage origination: 6.9 percent → 20.1 percent. - Private-label RMBS issuance: $150 billion → $1.2 trillion (2000–2005). - Private-label RMBS share of total MBS issuance: 18 percent → 56 percent. - CDO global issuance: increased more than six times to $1 trillion (2000 → crisis onset). - CDO-squared issuance: increased eleven-fold to $300 billion. - LBO values: just under $1 billion (1980) → over $60 billion (1988) → below $20 billion (following year). - Trend pace of net dilution: 5 percent (late 1920s), 3 percent (late 1990s). - Cyclically-adjusted price-earnings multiple peaks: 33 times and 47 times. - Required return on stocks is a statistically significant predictor at the 1 percent level of net equity issuance the following year. - Example breakeven/current spread statistics shown for illustrative assets (as labeled in figures): - US BBB-rated Corporate Bonds: Current Spread (83bps), Average Spread (172bps), Breakeven Spread in an Average Default Cycle (25bps), Breakeven Spread in the Worst Default Cycle (78bps). - US Leveraged Loans: Current Spread (441bps), Average Spread (583bps), Breakeven Spread in an Average Default Cycle (113bps), Breakeven Spread in the Worst Default Cycle (263bps). - US B-rated Corporate Bonds: Current Spread (293bps), Average Spread (439bps), Breakeven Spread in an Average Default Cycle (392bps), Breakeven Spread in the Worst Default Cycle (763bps). - US CCC-rated Corporate Bonds: Current Spread (673bps), Average Spread (1185bps), Breakeven Spread in an Average Default Cycle (807bps), Breakeven Spread in the Worst Default Cycle (1860bps). *Source: Annex 2 of the document titled "_wp14208 - Annex 2."* ### Annex 1. Standard Testing Techniques for Speculative Bubbles ### Annex 1. Standard Testing Techniques for Speculative Bubbles ### Overview of empirical testing approaches - Variance-bounds tests: - Examine whether the ex-post rational price is at least as variable as observed prices, as the latter are based on expected cash flows and do not have the variation introduced by future forecast errors which the ex-post price includes (see Shiller, 1981; Grossman and Shiller, 1981; and Blanchard and Watson, 1982). - Two-step tests: - Explicitly attempt to distinguish between model specification errors (with prices modeled as an autoregressive function of cash flows) and pricing behavior that would only be consistent with time-varying discount rates, and most likely bubbles (West, 1987). - Integration/cointegration tests: - Tests for bubbles are based on whether an unobserved bubble process is stationary after differencing, and where the level of prices and cashflows are cointegrated (Diba and Grossman, 1988). - Regime switches: - Incorporate infinite holding periods and the low probability but high impact event of a policy change, such as to a tax law or currency regime (Flood and Hodrick, 1986; Flood and others, 1994). - Expected regime switches, especially those that fail to materialize, pose a major problem for bubble detection because their observed impact on stock prices is similar to bubbles. - Non-linear models that explicitly allow for regime-switching in observed fundamentals similarly raise the bar for establishing the presence of bubbles (Driffill and Sola, 1998). - Time-varying discount rates and systemic or macroeconomic risk: - Examine the extent to which risk aversion covaries with aggregate consumption. - Discount rates are mapped onto estimates of the business cycle and systemic risk. - These models imply required returns are rationally lower when economic conditions are strong and vice versa (Fama and French, 1989; Campbell and Cochrane, 1999; Lettau and Ludvigson, 2001, Cochrane 2011). ### Annex 2. Estimates of Required/Expected Returns — terminology and measurement - Terminology: - ‘Expected’ or ‘required’ returns: applied to valuation of housing and stock markets (both real assets with an undefined maturity that can be modeled as a perpetual running yield). - ‘Excess returns’: refer to corporate bond spreads and risk premia in the government bond markets (i.e. nominal assets with a fixed maturity). - Valuation estimates are derived as follows: - Housing: - Constructed as a quarterly index of the ratio of rents to home prices. - The rental series is the ‘rent of primary residence’ published by the Bureau of Labor Statistics. - Nominal home price data are based on the series published in Shiller (2000a), with updates available in Haver. - Footnote: "Gallin (2008) finds the rent/price ratio to be useful for forecasting future U.S. home prices. While it is beyond the scope of the current paper, more elaborate ‘user cost’ models of home prices (incorporating other variables such as interest rates, running costs, and substitute rental costs) also offer promise in deriving long-run ‘fair value’ estimates for housing. The rent/price ratio should be a stationary series if it is to be meaningful." (footnote 40) - Stocks: - Expected real returns (into perpetuity) are defined as an equally weighted average of three models for the real discount rate backed out of current prices: (footnote 41) - (i) The cyclically-adjusted earnings yield: - Defined as the reciprocal of the ratio of prices to the seven year moving average of annual earnings per share for the MSCI country indices (aside from the longer U.S. series, which is based on S&P500 data back to 1914 from the Robert Shiller website). - (ii) The forward-looking single stage Gordon growth dividend model: - Price = dividend per share / (long-term bond yield + equity risk premium – long term GDP growth). - Prices, (cyclically adjusted) dividends, and the long-term risk free rate can be directly observed. - Long-term inflation and real GDP growth expectations are based on survey data reported by Consensus Economics beginning in 1989. - Prior to this, expectations for nominal growth are proxied by the 10-year moving average of inflation and real GDP growth. - (iii) The forward-looking multi-stage ‘H-model’ of Fuller and Hsia (1984): - Growth rate of earnings per share in the first seven years is based on matching year GDP growth expectations, with an upward or downward adjustment based on the current level of profit margins so as to stabilize the profit to GDP ratio in the steady state. - A constant (60 percent) payout ratio is applied to (cyclically adjusted) earnings to ameliorate cross-country differences in dividend taxation policies. - The yield curve out to seven years is used to discount the initial set of cash flows. - Cash flows beyond seven years are modeled as a constant growth-rate perpetuity based on long-term growth and inflation expectations, and long term bond yields. - Using observable spot prices, the pricing equation is solved iteratively such that observed market prices are consistent with future cash flows and discount rates. - Long-term inflation and real GDP growth expectations are based on survey data reported by Consensus Economics from 1989. Prior to this, expectations for nominal growth are proxied by the 10-year average of inflation and real GDP growth. - Footnote context: "Welch and Goyal (2008) and Campbell and Thompson (2008) run a series of horse races for competing models of equity valuation. Although not the primary purposes of this paper, like these studies, it was difficult to find any specification which could rival the forecasting power of the simple cyclically-adjusted earnings yield, despite it containing no forward looking inputs." (footnote 41) - Credit: - For U.S. investment grade bonds, duration matched spreads are based on the difference between Moody’s seasoned Baa and Aaa bond yield series available via the St Louis Federal Reserve FRED service. - For high yield bonds, duration-matched spreads are derived from the Bank of America Merrill Lynch BB+ corporate bond yield series available from Global Financial Data, and US Treasury bond yields. - Government Bonds: - The ‘Wicksellian’ bond risk premium: - Defined as the spread between the 5 year 5 year forward bond yield, and the equilibrium (or neutral) rate, which is proxied by long term consensus expectations of real growth and inflation over the same period. - A negative Wicksellian risk premium suggests bond yields are too low relative to the neutral rate, as proxied by long-run estimates of growth and inflation. - Characterized as essentially a special case of the Taylor rule for the bond market, where the neutral rate is captured by long term consensus expectations of real growth and inflation, and the inflation and output gap are closed. - Wherever possible, zero-coupon bond yields are used (in their absence, the yield to maturity). - Footnote: "While conventional bond risk premium models typically compare prevailing bond yields to model-derived estimates of future monetary policy expectations, these models have nothing to say directly about whether monetary policy settings are consistent with long-term growth and inflation." (footnote 42) ### Data sources and rationale - Real GDP growth and inflation expectations: - Measured on an annual basis out to ten years via a survey of professional forecasters compiled by Consensus Economics. - Advantages noted: - Not subject to revisions or serious time lags as is the case for many macrofinancial time series. - Available on a consistent cross-country basis. - Can capture market expectations of structural breaks or changes in macro variables, a particular issue for historical studies of emerging markets. - The authors state: "To the best of the author’s knowledge, this is the first time such data have been used in a comparable study." *Annex title: _wp14208 - Annex 1. Standard Testing Techniques for Speculative Bubbles* ### 2010. Available via the Internet: ### _wp14208 - 2010 ### Major themes in the referenced literature - Asset price bubbles, speculative behavior, and historical manias: - Chancellor, Edward, 2000, Devil Take the Hindmost: A History of Financial Speculation, (New York: Farrar, Straus and Giroux). - Diba, Behzad, and Herschel Grossman, 1988, “Explosive Rational Bubbles in Stock Prices?” American Economic Review, Vol. 78 (June), pp. 520-30. - Flood, Robert P., and Peter M. Garber, 1994, Speculative Bubbles, Speculative Attacks, and Policy Switching, (Cambridge, MA: MIT Press). - Garber, Peter M., 2001, “Famous First Bubbles: The Fundamentals of Early Manias,” The Journal of Political Economy, Vol. 109 (October), No. 5, pp. 1150-1154. - Kindleberger, Charles P., 1978 (2011), Manias, Panics and Crashes (New York: Macmillan). - Mackay, Charles, 1841 (1852), Memoirs of Extraordinary Popular Delusions, 3 Vols., R. Bentley, 1841, Revised version, under the title Memoirs of Extraordinary Popular Delusions and the Madness of Crowds (London: Office of the National Illustrated Library). - Thomas, Gordon, and Max Morgan-Witts, 1979, The Day the Bubble Burst (New York: Doubleday & Company). - Sorrel works: Minsky, Hyman, 1986, Stabilizing an Unstable Economy (New York: Columbia University Press); Minsky, Hyman, 1992, “The Financial Instability Hypothesis.” Working Paper No. 74, The Jerome Levy Economics Institute of Bard College, New York. - Theoretical foundations of market efficiency, speculation, and expectations: - Fama, Eugene F., 1970, “Efficient capital markets: A review of theory and empirical work,” Journal of Finance, Vol. 25, 383–417. - Fama, Eugene F., 1991, “Efficient Capital Markets: II,” Journal of Finance, Volume 46, Issue 5, pp. 1575–1617. - Hirshleifer, Jack, 1977, “The Theory of Speculation under Alternative Regimes of Markets,” The Journal of Finance, Volume 32 (September), No. 4, pp. 975-999. - Harrison, Michael J., and David Kreps, 1978, “Speculative Investor Behavior in a Stock Market with Heterogeneous Expectations,” Quarterly Journal of Economics, Vol. 92, No. 2, May, pp. 323–336. - Hamilton, James D., and Charles H. Whiteman, 1985, “The Observable Implications of Self-Fulfilling Expectations,” Journal of Monetary Economics, Vol. 16, pp. 353–373. - Scheinkman, Jose and Wei Xiong, 2003, “Overconfidence and Speculative Bubbles,” Journal of Political Economy, Vol. 111, No. 6, pp.1183-1219. - Grossman, Sanford J., and Robert J. Shiller, 1981, “The Determinants of the Variability of Stock Market Prices,” American Economic Review, Vol. 71 (May), Issue 2, pp. 222-227. - Shiller, Robert J., 1981, “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends,” American Economic Review, June, Vol. 71, pp. 421-436. - West, Kenneth, 1987, “A Specification Test for Speculative Bubbles,” The Quarterly Journal of Economics 102 (August), pp. 553-80. - Empirical studies on bubbles, market behavior, and asset pricing: - Ofek, Eli, and Matthew Richardson, 2003, “Dotcom Mania: The Rise and Fall of Internet Stock Prices,” Journal of Finance Vol. 58, pp. 1113-1137. - Diba and Grossman (1988) as above. - Gurkaynak, Refet S., 2005, “Econometric Tests of Asset Price Bubbles: Taking Stock,” Finance and Economics Discussion Series, The Federal Reserve Board Working Paper 2005-04, Washington D.C. - Hong, Harrison, Jose Scheinkman, and Wei Xiong, 2006, “Asset Float and Speculative Bubbles,” Journal of Finance, Vol. 61, No.3 (June), pp. 1073-1117. - Hong, Harrison and Jeremy C. Stein, 2007, “Disagreement and the Stock Market,” Journal of Economic Perspectives, Vol. 21, No. 2, Spring 2007, pp.109-128. - Hong, Harrison and David Sraer, 2013, “Quiet Bubbles,” Journal of Financial Economics, Vol. 110, pp. 596-606. - Greenwood, Robin, and Samuel G. Hanson, 2013, “Issuer Quality and Corporate Bond Returns,” Review of Financial Studies, Vol. 26 (June), No. 6, pp. 1483–1525. - Greenwood, Robin, and Andrei Shleifer, 2013, “Expectations of Returns and Expected Returns,” NBER Working Paper No. 18686, November, Cambridge, MA. - Vayanos, Dimitri and Paul Woolley, 2013, “An Institutional Theory of Momentum and Reversal,” The Review of Financial Studies, Vol. 26, No. 5, pp.1087-1145. - Lettau, Martin, and Sydney Ludvigson, 2001, “Consumption, Aggregate Wealth and Expected Stock Returns,” Journal of Finance Vol. 56, pp. 815–849. - Welch, Ivo, and Amit Goyal, 2008, “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” Review of Financial Studies, Vol. 21, Issue 4, pp. 1455-1508. - Corporate finance, issuance, and market timing studies: - Loughran, Tim and Jay R. Ritter, 1995, “The New Issues Puzzle,” Journal of Finance, Vol. 50, Issue 1, pp. 23-51. - Loughran, Tim and Jay R. Ritter, 1997, “The Operating Performance of Firms Conducting Seasoned Equity Offerings,” Journal of Finance, Vol. 52, Issue 5, pp. 1823-1850. - Ritter, Jay, 1991, “The Long-run Performance of Initial Public Offerings,” Journal of Finance, Vol XLVI, No. 1, pp. 3-27. - Korajczyk, Robert A., Deborah Lucas, and Robert L. McDonald, 1990, “Understanding Stock Price Behavior around the Time of Equity Issues” in Asymmetric information, Corporate Finance and Investment, edited by R. G. Hubbard (Chicago: NBER and University of Chicago Press). - Kaplan, Steven, and Jeremy C. Stein, 1993, “The Evolution of Buyout Pricing and Financial Structure in the 1980s,” Quarterly Journal of Economics, Vol. 108 (May), pp.313-357. - Myers, Stewart C., and Nicholas S. Majluf, 1984, “Corporate Financing Decisions when Firms have Information Investors Do Not Have,” Journal of Financial Economics, Vol. 13, pp. 187-211. - Teoh, Siew Hong, Ivo Welch and T. J. Wong, 1998, "Earnings Management and the Underperformance of Seasoned Equity Offerings," Journal of Financial Economics, Volume 50, Issue 1, pp. 63-99. - Liquidity, credit, and financial stability perspectives; policy and central bank remarks: - Diamond, Douglas, and Philip Dybvig, 1983, “Bank Runs, Deposit Insurance, and Liquidity,” Journal of Political Economy, Vol. 91, pp. 401-19. - Feroli, Michael, Anil K. Kashyap, Kermit Schoenholtz, and Hyun Song Shin, 2014, “Market Tantrums and Monetary Policy,” Working Paper presented at the 2014 U.S. Monetary Policy Forum, New York, February 28. Available via the Internet: http://research.chicagobooth.edu/igm/usmpf/2014.aspx?source=igm-em-usmpf14-20140221-initial. - International Monetary Fund, 2003, When Bubbles Burst, World Economic Outlook, Chapter 2, April (Washington D.C.: International Monetary Fund). - International Monetary Fund, 2014, “ Global Liquidity—Issues for Surveillance,” IMF Policy Paper, March 11 (Washington D.C.: International Monetary Fund). - Segoviano, Miguel, Bradley Jones, Peter Lindner, and Johannes Blankenheim, 2013, “Securitization: Lessons Learned and the Road Ahead,” IMF Working Paper 13/255, (Washington D.C: International Monetary Fund). - Stein, Jeremy C., 2013, “Overheating in Credit Markets: Origins, Measurement, and Policy Responses,” At the "Restoring Household Financial Stability after the Great Recession: Why Household Balance Sheets Matter" Research Symposium, sponsored by the Federal Reserve Bank of St. Louis, St. Louis, Missouri, February 7. - Stein, Jeremy C., 2014, “Incorporating Financial Stability Considerations into a Monetary Policy Framework,” Remarks at the International Research Forum on Monetary Policy, Washington DC, March 21. - Williams, John, 2013, “Bubbles Tomorrow and Bubbles Yesterday, but Never Bubbles Today?,” Speech to the National Association for Business Economics, San Francisco, CA, September 9. Available via the Internet: http://www.frbsf.org/our-district/press/presidents-speeches/williams-speeches/2013/september/asset-price-bubbles-tomorrow-yesterday-never-today/. - Measurement, econometrics, and detection of misalignments: - Gurkaynak, Refet S., 2005, “Econometric Tests of Asset Price Bubbles: Taking Stock,” Finance and Economics Discussion Series, The Federal Reserve Board Working Paper 2005-04, Washington D.C. - Gerdesmeier, Dieter, Hans-Eggert Reimers and Barbara Roffia, 2009, “Asset Price Misalignments and the Role of Money and Credit,” ECB Working Paper Series, No. 1068, July. - Siegel, Jeremy J., 2003, “What is an Asset Price Bubble? An Operational Definition,” European Financial Management, Vol. 9, No. 1, pp. 11-24. - Gallin, Joshua, 2008, “The Long-Run Relationship between House Prices and Rents,” Real Estate Economics, Vol. 36, Issue 4, pp. 635–658. - Muellbauer, John, 2012, “When is a Housing Market Overheated Enough to Threaten Stability?” in Property Markets and Financial Stability, edited by Alexandra Heath, Frank Packer and Callan Windsor, Proceedings of a conference, Reserve Bank of Australia, Sydney, pp. 73–105. - Market microstructure, mutual funds, and investor incentives: - Chevalier, Judith, and Glenn Ellison, 1997, “Risk Taking by Mutual Funds as a Response to Incentives,” The Journal of Political Economy, Volume 105 (December), No.6, pp. 1167-1200. - Chevalier, Judith, and Glenn Ellison, 1999, “Career Concerns of Mutual Fund Managers,” Quarterly Journal of Economics, Vol. 114, No. 2, pp. 389–432. - Chen, Qi, Itay Goldstein and Wei Jiang, 2010, “Payoff Complementarities and Financial Fragility: Evidence from Mutual Fund Outflows,” Journal of Financial Economics, Vol. 97, Issue 2, pp. 239-262. - Haldane, Andrew G., 2014, “The Age of Asset Management?” Speech at the London Business School, London, April. - Greenwood, Robin, and Andrei Shleifer, 2013, “Expectations of Returns and Expected Returns,” NBER Working Paper No. 18686, November, Cambridge, MA. - Behavioral, psychological, and historical perspectives: - Shiller, Robert J., 2000a, Irrational Exuberance (New Jersey: Princeton University Press). - Shiller, Robert J., 2000b, “Measuring Bubble Expectations and Investor Confidence,” The Journal of Psychology and Financial Markets, Vol. 1, No. 1, pp. 49–60. - Shiller, Robert J., 2003, “Diverse Views on Asset Bubbles,” in Asset Price Bubbles: The Implications for Monetary, Regulatory and International Policies, edited by Hunter, William, George Kaufman, and Michael Pomerlano (Cambridge, MA: MIT Press.) - Keynes, John M., 1930, A Treatise on Money (New York: Harcourt, Brace and Co.). - Keynes, John M, 1936, The General Theory of Employment, Interest, and Money (New York: Harcourt Brace). - Mackay and other historical accounts listed above. - Scheinkman and Xiong (2003) on overconfidence and speculative bubbles. ### Representative citation details (selected by topic) - Asset pricing and bubbles: Fama, Eugene F., 1970; Shiller, Robert J., 1981; Diba and Grossman, 1988; West, Kenneth, 1987. - Market behavior and investor incentives: Chevalier and Ellison, 1997 and 1999; Chen, Goldstein and Jiang, 2010. - Monetary policy, liquidity, and stability: IMF, 2003; IMF, 2014; Feroli et al., 2014; Stein, Jeremy C., 2013 and 2014. - Corporate finance and issuance timing: Loughran & Ritter, 1995 and 1997; Ritter, 1991; Kaplan & Stein, 1993. *Excerpted reference list from _wp14208 - 2010 (source PDF content).* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp14208.pdf_