The economic and statistical significance of spread forecasts: Evidence from the London Stock Exchange

This paper measures the economic and statistical significance of econometric forecasts of bid–ask spreads. The economic importance of these forecasts is assessed by considering the benefits of scheduling trades based on these forecasts. The unrestricted vector autoregression (VAR) model of Huang and Masulis [Rev. Financial Studies 12 (1999) 61] and the two-equation structural model of Huang and Stoll [Rev. Financial Studies 7 (1994) 179] are used to generate intraday h-step ahead forecasts of spreads for 50 stocks listed on the London Stock Exchange (LSE). The period corresponding to the minimum expected spread is then scheduled into the trading activity of the investor. The results indicate that when the unrestricted VAR model is used, the spreads incurred are around 35% lower than the spreads incurred by investors who do not schedule their trades. By contrast, spread discounts of only 5% are obtained when the two-equation structural model is used. The heterogeneity of the economic importance of the spread forecasts generated by the models is confirmed by tests of the statistical significance of the forecasts.     

Financial restatements by Canadian firms cross-listed and not cross-listed in the U.S.

Canadian financial restatements announced during 1997–2006 lower market quality and signal to market participants that expected future cash flows and their uncertainty are diminished and increased, respectively. Abnormal returns are related to downward revisions in consensus earnings forecasts, and become more negative for U.S. cross-listings, and for revenue recognition and company-initiated restatements. Total residual volatility, its information-based permanent component and the adverse selection spread component increase following such announcements. Relative spreads and a spread-depth market-quality index increase following such announcements and are lower for U.S. cross-listings. Relative spreads (unlike the market-quality index) remain higher post-announcement, and are lower post-Sarbanes-Oxley Act.     

Can credit spreads help predict a yield curve?

In this paper we investigate whether information in credit spreads helps improve the forecasts of government bond yields. To do this, we propose and estimate a joint dynamic Nelson–Siegel (DNS) model of the U.S. Treasury yield curve and the credit spread curve. The model accounts for the possibility of regime changes in yield curve dynamics and incorporates a zero lower bound constraint on yields. We show that our joint model produces more accurate out-of-sample density forecasts of bond yields than does the yield-only DNS model. In addition, we demonstrate that incorporating regime changes and a zero lower bound constraint is essential for forecast improvements.     

Yield Spread and Term to Maturity: Default vs. Liquidity

The aim of this paper is the analysis of the yield spreads between Treasury and non–Treasury Spanish fixed income assets and its relationship with the term to maturity. We find a downward sloping term structure of yield spreads for investment–grade bonds that seems to be contrary to the 'crisis at maturity' theory. However, we claim that this outcome is caused mainly by the effect of liquidity on yield spreads. Once the effect of liquidity and other factors are removed we find that there is a positive relationship between default premiums and term to maturity. That result is now consistent with the existing literature.     

证券分析师VS统计模型:证券分析师盈余预测的相对准确性及其决定因素

本文使用2005年35家券商对我国上市公司做出的每股盈余预测数据,考察了证券分析师盈余预测相对于统计模型的相对准确性及其决定因素。我们发现,我国证券分析师做出的盈余预测,同以年度历史数据为基础的统计模型得出的盈余预测相比,预测误差较小,证券分析师盈余预测具有一定的优势;但同某些以季度历史数据为基础的统计模型得出的盈余预测相比,预测误差较大,证券分析师盈余预测不具有优势。我们同时考察了决定证券分析师盈余预测相对准确性的决定因素。我们发现,公司每股盈余的波动性越大,公司上市越晚,跟踪公司的分析师越多,证券分析师的优势就越大。我们的研究对证券分析师以及投资者都有一定的启示作用。     

The relative contributions of equity and subordinated debt signals as predictors of bank distress during the financial crisis

Bank supervisors utilize early warning signals to predict which banks are likely to become distressed. Previous research has found that market discipline signals do not significantly improve out-of-sample forecasts relative to accounting-based signals. Most of that evidence, however, comes from periods in the 1990s when the U.S. economy and banking system were healthy, potentially neutralizing an advantage of market signals to incorporate new information quickly. For the period between the fourth quarters of 2006 and 2012, we assess the accuracy of two market signals – expected default frequency (EDF) and subordinated note and debenture (SND) yield spreads – relative to accounting-based signals in forecasting which publicly traded BHCs would become distressed. In 2008, EDF signals were relatively more accurate, but they did not lead to economically significant reductions in missed distress events relative to other signals. Supervisors would have been better off devoting slack resources to monitor BHCs with high commercial real estate concentrations. As the crisis subsided, a failure probability model developed from bank failures in the 1980s and early 1990s was consistently the most accurate signal. For the two dozen BHCs with actively traded SNDs, yield spreads over Treasuries were extremely poor predictors of distress because the spreads were distorted by too-big-to-fail subsidies. The Tier 1 leverage ratio was the most accurate distress signal for these large BHCs. In sum, the evidence to justify systematic reliance on market signals by supervisory agencies to forecast bank distress remains weak.     

The sensitivity of the credit default swap market to financial analysts’ forecast revisions

We examine the impact of analysts’ earnings per share (EPS) and cash flow per share (CPS) forecast revisions on the market for credit default swaps. We find that while the issuance of both EPS and CPS forecast revisions are inversely associated with changes in credit default swap (CDS) spreads, cash flow forecast revisions have a larger effect. We demonstrate that the relationship between CPS forecast revisions and CDS spreads tends to be stronger in cases of financial distress. We provide evidence that cash flow forecasts dominate earnings forecasts in some situations and that participants in the CDS market discriminate between analysts' forecast revisions and recommendation changes.     

An evaluation of the professional forecasts of U.S. long-term interest rates

This paper evaluates the multiperiod forecasts of Moody's Aaa corporate and the 10-year Treasury bond rates from the Survey of Professional Forecasters (SPF). We show that the SPF forecasts are not rational since they fail to be unbiased and, in some cases, do not fully incorporate the information in the past actual rates. These forecasts, however, are useful, since they are able to accurately predict the direction of change in the actual series. We also formulate a model that utilizes the information in the SPF forecasts of the unemployment rate. Comparable four-quarter-ahead forecasts of the two interest rates from this model are shown to be significantly more accurate than the corresponding SPF forecasts for 2001.1-2004.4.     

超预期股本扩张与分析师每股盈余预测

卖方分析师的每股盈余预测在实证文献中被广泛使用。该指标同时依赖于分析师对目标公司未来净利润的预测和对目标公司未来股本数量的预测。因此,如果在分析师发布预测后,目标公司的股本数量发生超出分析师预期的扩张,则每股盈余预测将无法代表分析师对目标公司未来基本面的预测。本文构建了“调整后每股盈余预测”指标,该指标可剔除超预期股本扩张对每股盈余预测的影响,真实反映分析师对目标公司未来基本面的预测。本文应用该指标,在两个具体的实证研究场景中证明了,忽略超预期股本扩张的影响可能得出错误的实证结论。本文还进一步指出了三个忽略超预期股本扩张的影响可能导致错误实证结果的研究场景。     

超预期股本扩张与分析师每股盈余预测

卖方分析师的每股盈余预测在实证文献中被广泛使用。该指标同时依赖于分析师对目标公司未来净利润的预测和对目标公司未来股本数量的预测。因此,如果在分析师发布预测后,目标公司的股本数量发生超出分析师预期的扩张,则每股盈余预测将无法代表分析师对目标公司未来基本面的预测。本文构建了“调整后每股盈余预测”指标,该指标可剔除超预期股本扩张对每股盈余预测的影响,真实反映分析师对目标公司未来基本面的预测。本文应用该指标,在两个具体的实证研究场景中证明了,忽略超预期股本扩张的影响可能得出错误的实证结论。本文还进一步指出了三个忽略超预期股本扩张的影响可能导致错误实证结果的研究场景。     

Nonlinear equilibrium adjustment dynamics and predictability of the term structure of interest rates

We analyze money market dynamics under a long-run equilibrium framework where commonly-monitored spreads serve as error correction terms, derived from a structural model incorporating autocorrelated risk premia, interest rate smoothing and monetary policy feedback. Using a dataset of monthly observations of the spot next and four-, thirteen-, twenty six- and fifty two-week Treasury Bills rates for the United States, Germany and United Kingdom from January 1999 to April 2016, we investigate the power of the expectations hypothesis theory of interest rates taking into account long-run deviations from equilibrium and inherent nonlinearities. We reveal short-run dynamic adjustments for the term structure of the USA, Germany and the UK, which are subject to regime switches. When forecastability is tested during May 2016–October 2017, the MSIH-VECM outperforms systematically the VECM. This is the first attempt to explore the possibility of parameter instability as a crucial factor in deriving the rejection of the restricted version of the cointegration space. Moreover, we investigate the dynamic out-of-sample forecasts of the term structure to assess the effectiveness of nonlinear MS-VECM modeling in capturing the after-effects of the global crisis. Overall, our results suggest that regime shifts in the mean and variance of the term structure may be intertwined with changes in fundamentals, that play a role in driving interest rate regimes, in particular business cycle and inflation fluctuations.     

Dynamics of credit spread moments of European corporate bond indexes

Traditional quantitative credit risk models assume that changes in credit spreads are normally distributed but empirical evidence shows that they are likely to be skewed, fat-tailed, and change behaviour over time. Not taking into account such characteristics can compromise calculation of loss probabilities, pricing of credit derivatives, and profitability of trading strategies. Therefore, the aim of this study is to investigate the dynamics of higher moments of changes in credit spreads of European corporate bond indexes using extensions of GARCH type models that allow for time-varying volatility, skewness and kurtosis of changes in credit spreads as well as a regime-switching GARCH model which allows for regime shifts in the volatility of changes in credit spreads. Performance evaluation methods are used to assess which model captures the dynamics of observed distribution of the changes in credit spreads, produces superior volatility forecasts and Value-at-Risk estimates, and yields profitable trading strategies. The results presented can have significant implications for risk management, trading activities, and pricing of credit derivatives.     

Forecasting the term structure of Chinese Treasury yields

This paper is the first to study the forecasting of the term structure of Chinese Treasury yields. We extend the Nelson–Siegel class of models to estimate and forecast the term structure of Chinese Treasury yields. Our empirical analysis shows that the models fit the data very well, and that more flexible specifications dramatically improve in-sample fitting performance. In particular, the model which enhances slope fitting is the best in capturing the Chinese yield curve dynamics. We also demonstrate that time-varying factors of the models may be interpreted as the level, slope and curvature of the yield curve. Furthermore, we use five dynamic processes for the time-varying factors to forecast the term structure at both short and long horizons. Our forecasts are much more accurate than the random walk, the Cochrane–Piazzesi regression and the AR(1) benchmark models at long horizons.     

Are analysts’ earnings forecasts more accurate when accompanied by cash flow forecasts?

We examine whether analysts’ earnings forecasts are more accurate when they also issue cash flow forecasts. We find that (i) analysts’ earnings forecasts issued together with cash flow forecasts are more accurate than those not accompanied by cash flow forecasts, and (ii) analysts’ earnings forecasts reflect a better understanding of the implications of current earnings for future earnings when they are accompanied by cash flow forecasts. These results are consistent with analysts adopting a more structured and disciplined approach to forecasting earnings when they also issue cash flow forecasts. Finally, we find that more accurate cash flow forecasts decrease the likelihood of analysts being fired, suggesting that cash flow forecast accuracy is relevant to analysts’ career outcomes.     

Understanding the term structure of credit default swap spreads

The shape of the term structure of credit default swap (CDS) spreads displays large variations over time and across firms. Consistent with the predictions of structural models of credit risk, we find that the slope of CDS spread term structure increases with firm leverage and volatility, but decreases with the level and the slope of the Treasury yield curve. However, these variables together have rather limited explanatory power for CDS slope and there is a significant common component in the regression residuals. In addition, we find that CDS slope predicts future changes in the CDS spreads, even after controlling for the contemporaneous variables that determine changes in the CDS spreads according to the structural models. Our results suggest that while structural models are qualitatively useful for understanding the shape of credit term structure, there are missing factors that importantly affect the term structure of CDS spreads.     

Asset volatility

We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.     

The debt market relevance of management earnings forecasts: evidence from before and during the credit crisis

We investigate the credit market’s response via changes in credit default swap (CDS) spreads to management earnings forecasts and evaluate the importance of these forecasts relative to earnings news during the periods before and during the recent credit crisis. We document that credit markets react significantly to management forecast news and that the reactions to forecast news are stronger than to actual earnings news. Consistent with the asymmetric payoffs to debt holders, the forecast news is mainly relevant for firms with poor credit rating or announcing bad news. We also show that the relevance of management forecasts to credit markets is particularly strong during periods of high uncertainty, as experienced during the recent credit crisis.     

Trade Generation, Reputation, and Sell-Side Analysts

This paper examines the trade‐generation and reputation‐building incentives facing sell‐side analysts. Using a unique data set I demonstrate that optimistic analysts generate more trade for their brokerage firms, as do high reputation analysts. I also find that accurate analysts generate higher reputations. The analyst therefore faces a conflict between telling the truth to build her reputation versus misleading investors via optimistic forecasts to generate short‐term increases in trading commissions. In equilibrium I show forecast optimism can exist, even when investment‐banking affiliations are removed. The conclusions may have important policy implications given recent changes in the institutional structure of the brokerage industry.     

US macroeconomic surprises and the emerging-market sovereign CDS market

Our primary aim is to examine whether US macroeconomic surprises affect the slope of the term structure of ‘sovereign credit default swap’ (SCDS) spreads in emerging markets. Our empirical results show that positive (negative) US macroeconomic surprises are likely to reduce (increase) the term structure slope of SCDS spreads in emerging countries. We find that the slope values in emerging markets are positively related to future market returns over 1- and 2-day horizons. Our results provide general support for the future informational role played by SCDS spreads for the national stock market within emerging markets.     

Are more able managers good future tellers? Learning from Japan

Baik et al. (2011) find that high-ability managers in the U.S. are more likely to issue accurate management earnings forecasts. Focusing on Japan, where management earnings forecasts are effectively mandated, we extend the literature by exploring (1) whether the relationship between managerial ability and forecast accuracy is unique to the U.S. disclosure system, where management forecasts are voluntary, and (2) how high-ability managers increase their forecast accuracy. We find that managerial ability is negatively associated with forecast errors based on initial forecasts, suggesting that high-ability managers are more likely to issue accurate forecasts at the beginning of the fiscal year. We then show that high-ability managers are less likely to revise their initial earnings forecasts and less likely to use earnings management to improve the accuracy of their earnings forecasts. Our findings show that, while high-ability managers are more likely to issue accurate initial management forecasts, low-ability managers are more likely to revise their forecasts and conduct earnings management to reduce their forecast errors.