Bond Return Predictability Research Articles

Order Book Commonalities and Bond Return Predictability

We examine the commonalities in the limit order book and the return predictive power of the order book information. Our analysis focuses on the transaction data that contains most actively traded Euro-area sovereign bonds on the MTS electronic platform and covers the period from Apr 1 to Sep 30, 2003. We evaluate the trading values of limit orders and use the option-based order book asymmetries to characterize the state of the limit order book. Our empirical finding suggest that a significant portion of the individual book asymmetry can be explained by common factors in the limit order book. More importantly, the common factors in the order book and the individual book asymmetry could significantly predict the future variations of bond returns. The long-term predictive power of the order book asymmetries is significant for up to 55 percent of the bonds in the sample. Evaluating the out-of-sample performance, we show that our forecasting model outperforms the benchmark in the accuracy of the return forecasts.

Order Book Commonalities and Bond Return Predictability

Order Book Commonalities and Bond Return Predictability

Heterogeneous Expectations and Bond Markets

This paper presents a dynamic equilibrium model of bond markets in which two groups of agents hold heterogeneous expectations about future economic conditions. The heterogeneous expectations cause agents to take on speculative positions against each other and therefore generate endogenous relative wealth fluctuation. The relative wealth fluctuation amplifies asset price and contributes to the time variation in bond premia. Our model shows that a modest amount of heterogeneous expectations can help explain several puzzling phenomena, including the excessive volatility of bond yields, the failure of the expectations hypothesis, and the ability of a tent-shaped linear combination of forward rates to predict bond returns. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Does debt market timing increase firm value?

Survey evidence indicates that firm managers try to time debt markets when choosing the maturity of new debt issues, but we do not know whether these strategies increase firm value. This research examines differences in value across nontimers and timers, where timers are defined as firms that follow either a naïve strategy of choosing long-term debt when the term premium is low or a strategy from Baker et al. (2003) based on the predictability of future excess bond returns. After controlling for various determinants of firm value, the research finds no differences in value across timers and nontimers. It also documents that the timing strategies do not increase firm value and do not affect announcement effects of long-term debt offerings. The results suggest that corporate debt markets are efficient and well integrated with equity markets.

Bond Risk Premia and Realized Jump Volatility

We find that adding a measure of market jump volatility risk to a regression of excess bond returns on the term structure of forward rates nearly doubles the R² of the regression. Our market jump volatility measure is based on the realized jumps identified from high-frequency stock market returns using the bi-power variation technique. The significant enhancement of bond return predictability is robust to different forecasting horizons, to using non-overlapping returns and to the choice of different window sizes in computing the jump volatility. This market jump volatility factor also crowds out the price-dividend ratio in explaining much of the countercyclical movement in bond risk premia. We argue that this finding provides support for the unspanned stochastic volatility hypothesis according to which the conditional distribution of excess bond returns is affected by state variables that are not in the span of the term structure of yields and forward rates.

Stock and Bond Returns with Moody Investors

We present a tractable, linear model for the simultaneous pricing of stock and bond returns that incorporates stochastic risk aversion. In this model, analytic solutions for endogenous stock and bond prices and returns are readily calculated. After estimating the parameters of the model by the general method of moments, we investigate a series of classic puzzles of the empirical asset pricing literature. In particular, our model is shown to jointly accommodate the mean and volatility of equity and long term bond risk premia as well as salient features of the nominal short rate, the dividend yield, and the term spread. Also, the model matches the evidence for predictability of excess stock and bond returns. However, the stock-bond return correlation implied by the model is somewhat higher than in the data.

Does Debt Market Timing Increase Firm Value?

Survey evidence indicates that firm managers try to time debt markets when choosing the maturity of new debt issues, but we do not know whether these strategies increase firm value. I examine differences in value across non-timers and timers, where timers are defined as firms that follow either a naive strategy of choosing long-term debt when the term premium is low or a strategy from Baker, Greenwood, and Wurgler (2003) based on the predictability of future excess bond returns. After controlling for various determinants of firm value, I find no differences in value across timers and non-timers. I also find that the timing strategies do not increase firm value and do not affect announcement effects of long-term debt offerings. The results suggest that corporate debt markets are efficient and well integrated with equity markets.

Stock and bond return predictability: the discrimination power of model selection criteria

The discrimination power of well-known model selection criteria is analyzed when the R-squared is low as in typical asset return predictability studies. It turns out that the discrimination power is low in this situation and this may explain, already in a simple i.i.d. setup, why often in-sample predictability, but no out-of-sample predictability is found. In particular it is possible to give another interpretation to the results of the well-cited Bossaerts and Hillion (Rev. Financial Stud. 12 (1999) 405–428) study. As a consequence, model selection criteria are put in a testing framework and a bootstrap-based procedure is proposed as a diagnostic tool to construct the class of models which are statistically indistinguishable from the best model chosen by a model selection criterion. In an empirical illustration the Pesaran and Timmerman (J. Finance 50 (1995) 1201–1228) results are reanalyzed and it turns out that in this case this class of models can be large. Finally it is shown that similar problems arise in a more hidden way in the context of recent model uncertainty studies using Bayesian model selection criteria.

Can Managers Successfully Time the Maturity Structure of their Debt Issues?

This paper shows that, contrary to existing evidence, corporate managers cannot successfully time the maturity of their debt issues to reduce their cost of capital. Our results indicate that the negative correlation between future excess long-term bond returns and the ratio of long-term debt issues to total debt issues is driven by aggregate pseudo market timing. We show that a structural shift in U.S. monetary and fiscal policy during the early 1980s induces a pseudo market timing effect in the in-sample tests of bond return predictability. After accounting for this structural shift, we find no evidence that corporate managers are able to predict future variations in excess long-term bond returns or to strategically choose the maturity of their debt.

Bond Return Predictability: An Investigation for the European Market

The predictability of security returns has received considerable attention in the finance literature. Notwithstanding, the predictability of bond returns, in particular outside the US, has been far less explored. In this paper we analyse the ability of several predetermined information variables in predicting bond returns in the European market. We test if variables, commonly used for that matter in the context of other markets (such as inverse relative wealth, term spread, real bond yield and a January dummy) are also useful predictors of European bond returns. Due to some particularities of the sample period of analysis, characterised by the EMU convergence, we also make another contribution by including the yield spread in relation to German bonds. Furthermore, we analyse the return predictability across different bond maturities: 1-3, 3-5 and 5 or more years to maturity. The results indicate that variables like the term spread, IRW and a January dummy represent useful information in order to predict bond returns for different maturities. The other two variables add little in terms of explanatory power. Surprisingly, the DM yield spread does not seem to have any predictive ability for the countries expected to participate in the EMU. However, a puzzling result was obtained: this variable appears to be significant for the UK market! Additionally, we find that investors, using simple trading strategies that exploit this information, may obtain higher returns. This outperformance is observed for different maturities, being more evident for long-term Government bonds. These findings may have important implications on other related issues such as market efficiency, asset pricing and portfolio performance evaluation.

Conditioning Information and European Bond Fund Performance

In this paper we evaluate the performance of European bond funds using unconditional and conditional models. As conditioning information we use variables that we find to be useful in predicting bond returns in the European market. The results show that, in general, bond funds are not able to outperform passive strategies. These findings are robust to whatever model (unconditional versus conditional and single versus multi‐index) we use. The multi‐index model seems to add some explanatory power in relation to the single‐index model. Furthermore, when we incorporate the predetermined information variables, we can observe a slight tendency towards better performance. This evidence is consistent with previous studies on stock funds and comes in support of the argument that conditional models might allow for a better assessment of performance. However, our results suggest that the impact of additional risk factors seems to be greater than the impact of incorporating predetermined information variables.

Return reversals in the bond market: Evidence and causes

The finance literature has shown that equity returns are predictable using past returns. This study extends that literature by examining bond return predictability. Using returns constructed from dealer bid prices, we find short- to intermediate-term reversals in investment grade corporate bond returns. These reversals are larger in the first half of the sample period and consistent with the predictions of dealer inventory cost models. This supports Jegadeesh and Titman’s [J. Financ. Intermed. 4 (1995) 116] assertion that daily, weekly, and monthly reversals in equity returns come from dealer inventory considerations, not behavioral biases. Finally, unlike equity returns, we find no evidence of momentum in bond returns.

Forecasting U.S. Bond Returns

The author examines the ability of variables such as steepness of the yield curve, real bond yield, recent stock market performance, and bond market momentum to predict bond returns. He shows that these indicators are more effective at predicting returns collectively than individually. He uses historical data to back test static and active investment strategies tied to these predictors. Not all investors are willing to replicate these allocation methods, because the forecasts are incorrect 40 percent of the time and expected losses in the short term may be viewed as a high cost to bear for potential long-term gains.

Time‐Varying Expected Returns in International Bond Markets

ABSTRACTThis article examines the predictable variation in long‐maturity government bond returns in six countries. A small set of global instruments can forecast 4 to 12 percent of monthly variation in excess bond returns. The predictable variation is statistically and economically significant. Moreover, expected excess bond returns are highly correlated across countries. A model with one global risk factor and constant conditional betas can explain international bond return predictability if the risk factor is proxied by the world excess bond return, but not if it is proxied by the world excess stock return.

Time-Varying Expected Returns in International Bond Markets

This paper examines the predictable variation in long- maturity government bond returns in six countries. A small set of global instruments can forecast 4-12% of monthly variation in excess bond returns. The predictable variation is statistically and economically significant. Moreover, expected excess returns are highly correlated across countries. A model with one global risk factor and constant conditional betas can explain international bond return predictability if the risk factor is proxied by the world excess bond return, but not if it is proxied by the world excess stock return.