International evidence on bond risk premia

This paper revisits the study of time-varying excess bond returns in international bond markets. Using newly available yield curve data from 10 different countries with independent monetary policy, I test the robustness of Cochrane and Piazzesi (2005). For most countries in my sample, I find more modest predictive power for forward rates than originally found by Cochrane and Piazzesi (2005) for the US. Their single-factor model captures well the predictability in international data, and this factor also tends to have a tent-shape in most countries of my sample. CP factors are more idiosyncratic across countries than yields or forward rates. Finally, I show that the recent financial crisis has significantly affected the predictability of excess bond returns.     

How good can heuristic-based forecasts be? A comparative performance of econometric and heuristic models for UK and US asset returns

This paper systematically investigates the sources of differential out-of-sample predictive accuracy of heuristic frameworks based on internet search frequencies and a large set of econometric models. The volume of internet searches helps gauge the degree of investors’ time-varying interest in specific assets. We use a wide range of state-of-the-art models, both of linear and nonlinear type (regime-switching predictive regressions, threshold autoregressive, smooth transition autoregressive), extended to capture conditional heteroskedasticity through GARCH models. The predictor variables investigated are those typical of the literature featuring a range of macroeconomic and market leading indicators. Our out-of-sample forecasting exercises are conducted with reference to US, UK, French and German data, both stocks and bonds, and for 1- and 12-months-ahead horizons. We employ several forecast performance metrics and predictive accuracy tests. Internet-search-based models are found to perform better than the average of all of the alternative models. For several country-asset-horizon combinations, particularly for UK bond returns, our heuristic models compare favourably with sophisticated econometric methods. The heuristic models are also shown to perform well in forecasting realized volatility. The baseline results are supported by several extensions and robustness checks, such as using alternative search keywords, controlling for Fama–French and Cochrane–Piazzesi factors, and implementing heuristic-based trading strategies.     

International fund investment and local market returns

International fund investment in bonds and equities is characterized by a positive association between current net inflows and contemporaneous and past market returns: positive-feedback trading, while being possibly profitable for international fund investors, could be destabilizing for the underlying markets. Allowing for interactions between equity investment and bond investment, our panel vector autoregression shows that past equity returns contain useful information in forecasting equity and bond flows and that bond flows impact future equity returns positively.     

The economic value of range-based covariance between stock and bond returns with dynamic copulas

The covariance between stock and bond returns plays important roles in the setting up of asset allocation strategies and portfolio diversification. In the present study, we propose a multivariate range-based volatility model incorporating dynamic copulas into a range-based volatility model to describe the volatility and dependence structures of stock and bond returns. We then go on to assess the economic value of the covariance forecasts based on our proposed model under a mean-variance framework. The out-of-sample forecasting performance reveals that investors would be willing to pay between 39 and 2081 basis points per year to switch from a dynamic trading strategy under the return-based volatility model to a dynamic trading strategy under the range-based volatility model, with more risk-averse investors being willing to pay even higher switching fees. Furthermore, additional economic gains of between 33 and 1471 annualized basis points are achieved when taking the leverage effect into consideration.     

Term structure determinants of time-varying risk of 1-year bond returns

Term structure drivers of 1-year bond premia and conditional bond return risk are distinct. Consequently, the Cochrane–Piazzesi factor captures aggregate price of risk and not the amount of risk in 1-year bond returns. One linear combination of forward rates captures most of the variation in bond return risk across maturities. Interest rate level captures substantial amount of variation in the conditional return risk, a finding consistent with rising inflation uncertainty with level of inflation and interest rates. The 4-5 yield spread, an important positive predictor of bond return premia, has an opposing but limited impact on the conditional volatility.     

News and the cross-section of expected corporate bond returns

We study the cross-section of expected corporate bond returns using an inter-temporal CAPM (ICAPM) with three-factors: innovations in future excess bond returns, future real interest rates and future expected inflation. Our test assets are a broad range of corporate bond market index portfolios. We find that two factors – innovations about future inflation and innovations about future real interest rates – explain the cross-section of expected corporate bond returns in our sample. Our model provides an alternative to the ad hoc risk factor models used, for example, in evaluating the performance of bond mutual funds.     

Habit formation,surplus consumption and return predictability: International evidence

On an international post World War II dataset, we use an iterated GMM procedure to estimate and test the Campbell and Cochrane (1999, By force of habit: a consumption-based explanation of aggregate stock market behavior. Journal of Political Economy 107, 205–251.) habit formation model with a time-varying risk-free rate. In addition, we analyze the predictive power of the surplus consumption ratio for future stock and bond returns. We find that, although there are important cross-country differences and economically significant pricing errors, for the majority of countries in our sample the model gets empirical support in a variety of different dimensions, including reasonable estimates of risk-free rates. Further, for the majority of countries the surplus consumption ratio captures time-variation in expected returns. Together with the price-dividend ratio, the surplus consumption ratio contains significant information about future stock returns, also during the 1990s. In addition, in most countries the surplus consumption ratio is also a powerful predictor of future bond returns. Thus, the surplus consumption ratio captures time-varying expected returns in both stock and bond markets.     

Treasury return predictability and investor sentiment

We document that the Treasury market investor sentiment (TSENT) of institutional investors is a powerful predictor of bond risk premia. Specifically, TSENT positively predicts Treasury bond excess returns in and out of sample. The forecasting gains of TSENT are incremental to those in conventional bond return predictors: Fama–Bliss forward spreads, Cochrane–Piazzesi forward rate factor, and Ludvigson–Ng macro factor, as well as equity market sentiment proxies such as the investor sentiment index and the partial least squares sentiment index. Asset allocation analysis indicates the forecasting power of TSENT is economically valuable to investors. Finally, we show that the time-series bond risk premia predictability associated with TSENT relates to its predictive power for macroeconomic performance, such as payroll employment, unemployment rate, and industrial production.     

Who Values Economist Forecasts? Evidence From Trading in Treasury Markets

While economic forecasting is ubiquitous within the industry, its role in the trading process has received little attention in the literature. We examine how economist forecasts are related to trading activity in the OTC treasury bond market at the participant level. Consistent with models of heterogeneous opinions, we show that the forecasting economists employing institution places a disproportionately large reliance on the forecast. There is pervasive evidence that this reliance is asymmetric. Only forecasts which imply a fall in future treasury bond prices are associated with an abnormal trading reaction consistent with the forecast. Reference dependence and loss aversion offer one possible explanation for this asymmetric trading response.     

Evaluating the information content of earnings forecasts

This study develops a framework to compare the ability of alternative earnings forecast approaches to capture the market expectation of future earnings. Given prior evidence of analysts’ systematic optimistic bias, we decompose earnings surprises into analysts’ earnings surprises and adjustments based on alternative forecasting models. An equal market response to these two components indicates that the associated earnings forecast is a sufficient estimate of the market expectation of future earnings. To apply our framework, we examine four recent regression-based earnings forecasting models, alongside a simple earnings-based random walk model and analysts’ forecasts. Using the earnings forecasts of the model that satisfies our sufficiency condition, we identify a set of stocks for which the market is unduly pessimistic about future earnings. The investment strategy of buying and holding these stocks generates statistically signi?cant abnormal returns. We offer an explanation as to why this and similar strategies might be successful.     

Long-run returns following open market share repurchases

Past studies find abnormal returns to buying after repurchase program announcements. We analyze the profitability of trading after both program announcements and individual repurchase trade publication using different trading strategies – market and limit orders. The analysis of trades is possible because of a unique Canadian data set. The highest abnormal returns are earned by companies on their own repurchase trades which benefits the non-tendering shareholders. For the public investor, we find no strategies that, in practice, would earn abnormal returns to buying after program announcements. However, there is qualified evidence of abnormal returns to a limit order strategy following publication of individual repurchase trades.     

Covariance Stationarity of International Equity Markets Returns: Recent Evidence

Intertemporal stationarity tests of the variance-covariance matrix of monthly returns on seven international equity indices are conducted over the most recent period. Pairwise covariances are then decomposed into their component statistics for further examination of the source(s) of stationarity or nonstationarity. Historical analysis reveals that pairwise covariances were invariably highly nonstationary over forecast intervals that varied in length between one month and five years. Reliance on historical covariances to estimate future covariances over a hold-out sample produced suboptimal results in comparison to an alternative naive forecasting model. These findings were robust in that they were invariant to whether nominal or real returns were used. Evidence on the intertemporal stationarity of the vector of mean returns is also provided.     

Does information drive trading in option strategies?

We study trading in option strategies in the FTSE-100 index market. Trades in option strategies represent around 37% of the total number of trades and over 75% of the total trading volume in our sample. We find some evidence that order flow in volatility–sensitive option strategies contains information about future realized volatility. We do not find evidence that order flow in directionally–sensitive option strategies contains information about future returns. Overall, our evidence suggests that option strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies.     

STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions) and multi-step-ahead forecasting methods. The forecasts are evaluated with statistical and economic criteria. In terms of statistical criteria, we compared the out-of-sample forecasts using goodness of forecast measures and various testing approaches. The results indicate that ANNs consistently surpass the random walk model and, although the evidence for this is weaker, provide better forecasts than the linear AR model and the STAR models for some forecast horizons and forecasting methods. In terms of the economic criteria, we assess the relative forecast performance in a simple trading strategy including the impact of transaction costs on trading strategy profits. The results indicate a better fit for ANN models, in terms of the mean net return and Sharpe risk-adjusted ratio, by using one-step-ahead forecasts. These results show there is a good chance of obtaining a more accurate fit and forecast of the daily stock index returns by using one-step-ahead predictors and nonlinear models, but that these are inherently complex and present a difficult economic interpretation.     

Stock and bond market interactions with level and asymmetry dynamics: An out-of-sample application

We model the dynamic interaction between stock and bond returns using a multivariate model with level effects and asymmetries in conditional volatility. We examine the out-of-sample performance using daily returns on the S&P 500 index and 10 year Treasury bond. We find evidence for significant (cross-) asymmetries in the conditional volatility and level effects in bond returns. The out-of-sample covariance matrix forecasts of the model imply that an investor is willing to pay between 129 and 820 basis points per year for using a dynamic trading strategy instead of a passive strategy.     

Evaluating stock returns with time-varying risk aversion driven by trend deviations from the consumption-to-wealth ratio: An analysis conditional on income levels

Based on the cointegrating relationship between consumption and wealth, we estimate the long run consumption-to-wealth ratio for each of five consumer income quintiles as well as national data for benchmarking purposes. Short run deviations from the consumption-to-wealth ratio for each quintile are examined for their ability to forecast changes in future consumption, income, housing values, and especially stock returns. We demonstrate that these trend deviations when combined with consumption growth in a multifactor model, significantly improve the ability of the dividend-to-price ratio to forecast future market returns over short and intermediate horizons for consumers in the highest-income quintile. This paper contributes to the financial economic literature by showing that the highest-income consumers are forecasting future stock returns with the help of the persistence in the dividend-to-price ratio and are modifying their consumption accordingly.     

Does volume help in predicting stock returns? An analysis of the Australian market

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.     

THE ECONOMIC VALUE OF USING REALIZED VOLATILITY IN FORECASTING FUTURE IMPLIED VOLATILITY

We examine the economic benefits of using realized volatility to forecast future implied volatility for pricing, trading, and hedging in the S&P 500 index options market. We propose an encompassing regression approach to forecast future implied volatility, and hence future option prices, by combining historical realized volatility and current implied volatility. Although the use of realized volatility results in superior performance in the encompassing regressions and out-of-sample option pricing tests, we do not find any significant economic gains in option trading and hedging strategies in the presence of transaction costs.     

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.     

Forecasting annual excess stock returns via an adaptive network‐based fuzzy inference system

In this study, an adaptive network‐based fuzzy inference system (ANFIS) and a neural network were tested for the ability of these techniques to forecast the annual excess returns of three large publicly traded companies from a time series of said returns. The predictive ability of these techniques was compared with that of an autoregressive moving average (ARMA) model. The Fair–Shiller test was used in the comparisons in order to obtain results that were not subjective and so that conclusions could be made regarding the information used by the techniques in the generation of their forecasts. Since predictive ability does not translate to profitability, a simple trading strategy was used to determine the ability to generate profits from trading upon the forecasts of the respective techniques. As hypothesized, the ANFIS and neural network techniques are able to generate forecasts with significant predictive ability. However, neither technique dominates the other or the ARMA model. In tests of the ability of the techniques to generate profits from their forecasts, a simple trading strategy was used (trading on the predicted sign of the return). The ANFIS and the neural network generated profits in all of the trading scenarios. However, neither technique dominated the other, nor did they consistently outperform the traditional and naive models (strategies). The mixed results in the predictive ability tests and the profitability tests indicate that the conclusions from the study differ based upon the context in which the forecasts are used. Copyright © 2005 John Wiley & Sons, Ltd.