# Flights to Safety

Baele, Lieven, Geert Bekaert, Koen Inghelbrecht, and Min Wei, “Flights to Safety,” American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To propose an empirical definition of a “flight to safety” episode, using only stock and bond return data.

Claim:  A “flight to safety” (FTS) is a day on which

• Bond returns are positive.
• Equity returns are negative.
• Bond returns are negatively correlated with stock returns.
• Equity return volatility is large (markets are stressed).

Methods:

• Data covers bond and equity returns for 23 countries from January 1980 through January 2012.
• In the literature, flights to liquidity may be as important as flights to quality.  Therefore, this paper looks at returns on highly-liquid 10-yeargovernment bonds.
• German bonds are the benchmark for Eurozone countries; local government bonds are the benchmark for all others.
• Equity returns are indexes denominated in local currencies, from Datastream International.
• Develop a composite flight-to-safety indicator
• Sort observations by variables that are conceptually increasing in likelihood of flight to safety.
• Assign a ranking to each observation for each sort, then divide each ranking number by the total number of observations–the “ordinal numbers.”
• Identify days that qualitatively appear to be “mild” flight-to-safety episodes:
• bond returns are higher than stock returns,
• bond returns are further above their 250-day average than are stock returns,
• the short-term stock-bond correlation is negative,
• the long-term stock-bond correlation is higher than the short-term correlation (it is less negative or positive),
• equity return volatility is more than one standard deviation above its mean, and
• short-term equity volatility is higher than long-term volatility.
• Observations that fail to meet the qualitative test are given a FTS indicator of zero.
• Observations that pass the test are given an indicator of 1 minus the percentage of observations failing the test that have a higher ordinal number.

Results:

• This methodology identifies major market crashes, including October 1987, the Russia crisis of 1998, and the Lehman bankruptcy.
• In a flight to safety
• Bond returns are 2%-3% higher than equity returns.
• The Yen, US Dollar, and Swiss Franc appreciate.
• The VIX increases.
• Consumer sentiment falls.
• Money-markets, corporate bonds, and non-metal commodities have negative abnormal returns.
• Liquidity suffers in both bond and equity markets.
• Immediately following a flight to safety, economic growth and inflation decline for up to one year.

# Banks as Patient Fixed Income Investors

Hanson, Samuel G., Andrei Schleifer, Jeremy C. Stein, and Robert W. Vishny, “Banks as Patient Fixed Income Investors,” American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To introduce a model explaining why asset holdings differ between traditional banks and so-called “shadow banks.”

Findings:

• Traditional banks have more market share in assets that are illiquid and that are fundamentally safe but have high intertemporal volatility.
• So-called shadow banks hold assets that are either highly liquid or that have low volatility.
• equities, Tresauries, etc.

Motivation:

• Commercial banks’ liability mix is highly homogeneous (mostly customer deposits), both in the cross section and in the time series.
• Bank assets are far more heterogeneous.
• Banks’ scale seems to be driven by their ability to attract deposits (liabilities), rather than by their investment opportunities (assets).
• Banks’ asset portfolio does not appear to be a liquidity buffer; rather, banks hold few Treasury securities and instead hold assets earning a (riskier) spread over Treasuries.
• Bank risk-taking is not likely to be the result of moral hazard due to deposit insurance.

Model:

• There are N risky assets
• Assets are perfectly correlated, differing by their payoff in the bad state of the world.
• The model has three actors: households, banks, and shadow banks.
• Households are risk neutral, and invest in bank claims but do not own assets.
• Banks invest in assets and issue claims on these assets to households. Intermediation is necessary to create safe claims, as no asset is risk-free.
• The model has three time periods:
• At time t=0, banks invest in risky assets and issue claims to households.
• At time t=1, bad news arrives with probability 1-p. Shadow banks must sell assets for less than fundamental value, but traditional banks are not forced to sell.
• At time t=1, payoffs to asset holdings and household claims are realized. If bad news arrived at time t=2, then the bad state occurs with probability 1-q.
• Traditional banks hold assets to maturity, and create safe assets by only issuing claims equal to their asset portfolio payoff in the bad state. The rest of their asset portfolio is financed using costly equity.
• A shadow bank is a dual institution–a highly-leveraged institution (HL) and a money-market fund (MMF).
• The HL purchases risky assets and enters a short-term repo agreement with the MMF.
• The MMF creates safe assets through its ability to seize the HL’s assets and sell them at fire-sale prices if bad news occurs.

Results:

• In an interior equilibrium where both types of banks hold an asset type, the marginal benefit of stable funding (the avoidance of fire-sale losses) equals the marginal cost of stable funding (limits on the amount of claims that can be created—money creation).
• Corner solutions are also possible, where an asset type is held exclusively by one type of institution.
• Traditional bank asset ownership increases in asset illiquidity and in expected bad-state payoff.
• Equities are not suitable for traditional bank ownership (bad-state payoffs are too low) but are suitable for shadow bank ownership (liquidity is high).
• An increase in the premium households pay for safe assets lowers traditional bank ownership for all (risky) assets.
• A decrease in the probability of bad news lowers traditional bank ownership for all assets.

Empirical Evidence

• Banks with stickier liabilities hold more illiquid assets.
• “Sticky” liabilities mean that depositors are less likely to withdraw them upon bad news.
• Banks hold very little of either Treasury securities or equities.

# Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns

Bali, Turin G., Stephen J. Brown, and Yi Tang, “Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns,” The American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To show that economic uncertainty is an economically and statistically significant driver of the cross-section of stock returns.

Motivation:  In the ICAPM world, investors care not only about the expected payoff of their investments, but also about their portfolios’ covariances with state variables affecting both future consumption and opportunities for investment.

Data/Methods:

• Measure economic uncertainty using
• the dispersion of forecasts from the Survey of Professional Forecasters
• real GDP growth and real GDP level
• log (75th pctl forecast / 25th pctl forecast) * 100
• cross-sectional dispersion in forecasts for output, inflation, and unemployment
• Fama-MacBeth regressions
• Sort into deciles based on market beta.
• Find time-varying “uncertainty betas” of stocks using rolling regressions of stock excess returns on the uncertainty measure, and sort into subdeciles.
• Economic Uncertainty Index
• Use Principal Component Analysis to find the common component among seven different proxies for economic uncertainty.

Results:

• Covariance with economic uncertainty is significantly negatively correlated with higher returns, after controlling for market beta, size, book-to-market, momentum, short-term reversal, illiquidity, co-skewness, idiosyncratic volatility, and the dispersion of analyst forecasts.
• The beta of the proposed “uncertainty index” appears able to significantly predict future stock returns.

# What do Private Equity Firms Do?

Gompers, Paul, Steven N. Kaplan, and Vladimir Mukharlyomov, “What do Private Equity Firms Do?” The American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To describe the investment behavior of private equity firms (not including venture capital), and compare that behavior to academic theory.

Findings:

• P/E firms use internal rates of return and multiples of invested capital, rather than discounted cash flows and the CAPM, to value acquisition targets.
• They usually look for internal rates of return between 20% and 25%,
• P/E firms use comparable company multiples to calculate exit value, instead of discounted cash flows.
• P/E firms choose the capital structure of their portfolio companies based on
• The company’s industry (100% of P/E firms)
• current interest rates (100%)
• the tradeoff between the debt tax shield and default risk (67%)
• the maximum amount of debt the market will buy (67%)
• the ability of debt to force operational improvements (40%)
• They plan to improve the operations of their acquisitions, but do so more by increasing growth than by cutting costs.
• management incentives
• P/E firms give 8% of company equity to the CEO, and 9% to the remaining managers and employees.
• Governance
• P/E firms prefer boards of directors of between 5 and 7 members, with the P/E firm supplying 3 of those.
• 58% supply their own management teams after the acquisition.
• Deal Selection
• Of 100 opportunities, P/E firms deeply investigate 24 and close on 6.
• Almost half of closed deals are “proprietary,” meaning the P/E executives sourced the deal themselves.
• Criteria for evaluating investment opportunities, in order of importance, are
• the business model and competitive position
• the management team
• the P/E firm’s ability to add value
• the target’s valuation
• Value Creation
• Increasing revenue is important in 70% of deals.
• Follow-on acquisitions are important in 50%.
• Reducing costs is important in 36% of deals.
• Changing company’s business model or strategy is key in 33%

Conclusions

• P/E firms in the 1980s focused on cost-cutting and decreasing agency costs through very high leverage.
• P/E firms today prefer to increase revenue and improve governance, and do not use so much leverage.
• P/E firms are more industry-focused today than they were in the 1980s.
• They devote significant resources to improving operations in their portfolio companies.
• P/E firms have outperformed benchmarks for 30 years, and their executives tend to be educated at the best business schools, so their behavior likely identifies best-practices.
• They do not use DCF valuation techniques, suggesting that DCF methods are either made redundant by IRR-based valuation, or that they are deficient.
• P/E firms’ limited partners care more about absolute return than performance relative to a benchmark.

Data is from a survey of 79 private equity firms (64 of whom responded in full, representing \$600 billion in assets).

# The “Greatest” Carry Trade Ever? Understanding Eurozone Bank Risks

Acharya, Viral V. and Sascha Steffen, “The “Greatest” Carry Trade Ever?  Understanding Eurozone Bank Risks,” The American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To investigate the risks assumed by Eurozone banks as a form of carry trade, where banks loaded positively on GIPSI (Greece, Ireland, Portugal, Spain, and Italy) bonds and negatively on German government bonds.

Findings:

• Banks’ stock returns were positively correlated with GIPSI bond returns, and negatively correlated with German bond returns, for the period January 2007 to June 2012.
• Banks were effectively financing long-term GIPSI bond holdings with short-term German debt.
• This carry trade behavior was more notable at large banks, banks with lower capital ratios, and banks with riskier asset portfolios.
• Banks increased the magnitude of their carry trade between March and December 2010, ruling out the possibility that they were passively caught up in the crisis.
• Banks’ carry trade exposure was related to actual bond holdings rather than to holdings of other asset classes.
• Both GIPSI and non-GIPSI banks were involved, so the carry trade is not a case of GIPSI banks suffering from the weakness of their home countries.
• Regulatory arbitrage was a likely contributor to the carry trade of both GIPSI and non-GIPSI banks.
• Governments had incentives to maintain the Basel II zero risk-weighting on sovereign bonds so they could continue to borrow.
• Banks with low capital ratios were incentivized to buy these zero risk-weighted sovereign bonds.
• Risk-shifting by GIPSI banks may also have been a contributor to the carry trade of GIPSI banks.
• An Italian bank, say, might have wanted to shift risk to a state of the world where they would be in trouble anyway (an Italian default) by buying Italian bonds.
• Moral hazard may have also played a role, where banks in strong sovereigns might assume the risk of the carry trade with an implicit expectation of being bailed out in a worst-case scenario.
• Moral suasion may have occurred where weak sovereigns convinced their home banks to buy own-sovereign debt.
• U.S. money-market funds played an important role in providing or not providing liquidity to European banks.
• After November 2010, they withdrew 60% of their investment in weakly capitalized banks and doubled their investments in well-capitalized banks.

Data:

• Data on stock prices, bond yields, and CDS credit spreads comes from Bloomberg.
• Banks’ portfolio holdings data is from the European Banking Authority (EBA).

Methods:

• Regress each bank’s daily stock returns on the daily returns of 10-year GIPSI government bonds (all five countries), the daily return on 10-year German government bonds, and the daily return of bank’s home equity market (orthogonalized to the sovereign bond returns of Germany and of the home country).
• $\beta_{Greece}$ estimates each bank’s exposure to Greek sovereign debt, etc.
• $\beta_{Germany}$ is the estimate of each bank’s exposure to German bonds (a negative value indicates the bank is “short” German government debt).

Conclusions:

• European regulators should not consider the sovereign debt of all Euro countries risk-free (or even high-liquidity).
• ECB lending should probably be used to recapitalize banks rather than to guarantee their debt and encourage them to increase asset exposures.
• While this props up the financial markets, especially in the banks’ home countries (due to home bias), it potentially makes a future sovereign debt crisis even more dangerous.
• When banks hold long-term risky assets and rely on short-term funding, they are likely to encounter liquidity shortages.

# Stock Market Valuations across U.S. States

Bekaert, Geert, Campbell R. Harvey, Christian T. Lundblad, and Stephan Siegel, “Stock Market Valuations across U.S. States” American Finance Association, 75th Annual Meeting, Boston (2015).

Purpose:  To show that state-specific regulatory environment affects valuation, and to estimate the marginal impact of regulation.

Findings:

• After controlling for leverage and earnings growth volatility, PE ratios vary across states within the same industry (segmentation).
• State-specific financial deregulation decrease segmentation.
• Increased labor laws increase segmentation.
• Higher state-specific unemployment is linked with higher segmentation.
• Higher population density is linked with lower segmentation.
• Segmentation has been decreasing since the mid-1970s.
• Distance between a given state’s capital and New York’s capital is a statistically significant, but economically small, determinant of segmentation

Methods:

• Calculate the absolute difference in P/E ratios between an industry in a given state and the same industry in New York (the financial center of the U.S.)
• price data comes from CRSP, with earnings data from Compustat
• noise biases the measure upwards, so the measure is smaller for years or states with smaller firms
• A state’s level of segmentation is given by the value-weighted sum of the measure for all industries in the state.
• Regress the segmentation measure on variables
• difference in leverage between industries in the given state and the same industries in New York
• difference in earnings growth
• difference in return volatility
• number of firms in the state
• time
• Classify a number of regulation changes that were made during the time sample, and conduct difference in differences tests.

# Asset Prices and Business Cycles with Financial Shocks

Nezefat, Mahdi, and Ctirad Slavik, “Asset Prices and Business Cycles with Financial Shocks,” American Finance Association, 75th Annual Meeting, Boston (2015).

Purpose:  This paper introduces a DSGE asset pricing model in which shocks to firm productivity and to firm financial constraints lead to asset price volatility.

Model:

• Setup
• two consumers (entrepreneurs and laborers)
• two goods (a consumption good and a capital good)
• infinite and discrete time, with two subperiods in each period
• in subperiod 1, all entrepreneurs hire labor and produce using the same technology.
• in subperiod 2, a fraction of entrepreneurs are randomly presented with investment opportunities (i.e. the ability to transform the consumption good one-to-one into capital, without adjustment costs).
• Firms not investing in new projects can purchase equity in other firms.
• Equity is the only asset traded in the market (incomplete markets).
• Firms’ financial constraint (the financial friction) is that there is a limit on how much of each new project can be sold as equity.
• This limit changes over time, which is a theoretical contribution of this model.
• Entrepreneurs and laborers maximize the present value of their consumption subject to a budget constraint, and wages and return on equity are determined competitively, and markets clear.
• Productivity and financial shocks:
• Productivity shocks affect the wealth of all firms, changing how much they can spend on equity.
• Financial shocks affect the funding of firms with investment projects, and determine how much equity is available to the market.
• These two shocks directly influence the amount of equity traded and investors’ budget constraints, and so directly contribute to fluctuations in asset prices.

Results:

• After calibrating the model to the U.S. economy, productivity shocks alone explain little of the observed volatility.
• With both types of shocks, modeled asset price volatility is about 80% of the observed volatility of the stock market.
• The model explains 70% of the observed equity premium.
• This model also generates the volatility in investment that is observed in the data.
• Unlike previous models, the equilibrium here is not Pareto optimal.  The government could increase all agents’ welfare by relaxing the financial constraints of entrepreneurs with investment projects by extending loans to them.

# The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response

Budish, Eric, Peter Cramton, and John Shim, “The High-Frequency Trading Arms Race:  Frequent Batch Auctions as a Market Design Response,” SSRN, December 23, 2013, <http://ssrn.com/abstract=2388265>.

Purpose:  To argue that the continuous limit order book design of current securities markets is socially wasteful, reduces trading depth (large trades are less available), and increases trading spreads; and to propose batch auctions as a better market design.

Motivation:  High-frequency trading firms (HFTs) spend hundreds of millions of dollars to increase their communication speeds with financial markets by just a few thousandths of a second.  Is this “arms race” a healthy competition, or does it reveal a flaw in the design of our financial markets?

Findings:

• Very high correlations that are observed between securities traded on different exchanges break down over very short time intervals (a few milliseconds).
• Arbitrage opportunities available to the fastest traders may amount to billions of dollars annually.
• Time horizons for HFT have shrunk over time, but competition has not reduced the size of the opportunity
• The continuous limit order book (first come, first served) market design increases bid-ask spreads and penalizes liquidity providers who would offer large trades, thus keeping markets thin
• The arms race hurts social welfare by incentivizing investment in expensive high-speed technology
• Frequent batch auctions (such as once per second) would eliminate these shortcomings
• By greatly reducing the advantage of high speed
• By forcing HFTs to compete on price instead of on speed alone
• Batch auctions may also improve both market stability and regulators’ ability to oversee trading activity

Data/Methods:

• Direct-feed data is from the NYSE and the CME for the period 1/1/2005 to 12/31/2011, including all activity on the exchanges’ limit order books with millisecond-level time stamps. This is the same data that HFTs use.
• Compute correlation between % changes in the bid-ask midpoints of highly correlated securities.
• Calculate arbitrage profits by assuming the ability to instantaneously trade and summing the profits from arbitrage opportunities over a period.
• Model: investors, (quantity) N HFTs, and security x perfectly correlated (with latency) with a public signal y.
• 1 HFT acts as the “liquidity provider” and N-1 act as “stale-quote snipers.”
• When the public signal moves, the liquidity provider adjusts its prices, and the snipers simultaneously try to buy or sell at the old prices; snipers are successful with probability 1/N.
• The liquidity provider builds the probability of getting sniped into its bid-ask spreads.

Conclusions:

• In equilibrium, the cost of HFTs investments in speed, the total profits to be made by HFTs’ technical arbitrage, and the revenue extracted from investors by the liquidity provider’s bid-ask spreads are all equal.
• In the model, a positive bid-ask spread exists even in the case of perfect information, so investors lose out.
• Prisoners’ Dilemma: HFTs would be better off mutually agreeing not to invest in speed
• Both empirically and theoretically, the size of the arms-race price does not depend on the speed of HFTs.
• The HFT arms race hurts both investors and society, and could be corrected by moving to batch auctions.

The figure below plots the time series of the bid-ask midpoints of two highly correlated securities:  the E-mini S&P 500 future (ES, in blue) and SPDR S&P 500 ETF (SPY, in green).  The series are shown for an ordinary trading day (08/09/2011), using four different time horizons.  Note the very high correlation between the lines in (a) and the low correlation in (d).  At 10 milliseconds, there is virtually zero correlation.  For details, see original paper.