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This study examines evidence of instability in models of ex post predictable components in stock returns related to structural breaks in the coefficients of state variables such as the lagged dividend yield, short interest rate, term spread and default premium. We estimate linear models of excess returns for a set of international equity indices and test for stability of the estimated regression parameters. There is evidence of instability for the vast majority of countries. Breaks do not generally appear to be uniform in time: different countries experience breaks at different times. For the majority of international indices, the predictable component in stock returns appears to have diminished following the most recent break. We assess the adequacy of the break tests and model selection procedures in a set of Monte Carlo experiments.

Predictability of stock returns has been well documented in the empirical finance literature and is now routinely used in studies of mutual fund performance (Christopherson et al., 1998 and Ferson and Schadt, 1996), tests of the conditional CAPM (Ferson and Harvey, 1991 and Ghysels, 1998) and optimal asset allocation (Ait-Sahalia and Brandt, 2001, Barberis, 2000, Brandt, 1999, Campbell and Viceira, 1998 and Kandel and Stambaugh, 1996). Variables commonly used to predict stock returns include the dividend yield, the short term interest rate, and term and default premia. Most studies assume a stable prediction model in which the coefficients on the state variables do not change over time.1 Recent empirical studies have, however, cast doubt upon the assumed stability of return forecasting models. In a forecasting model based on the dividend and earnings yield, Lettau and Ludvigsson (2001) find some evidence of instability in the second half of the 1990s. Likewise, Goyal and Welch (2003) uncover instability in return models based on the dividend yield when data from the 1990s is added to the sample. Ang and Bekaert (2004) also find evidence of deterioration in predictability patterns in US returns in the second half of the 1990s. Signs of instability in financial prediction models have also emerged from studies that specifically address the question of whether stock market investors could have exploited predictability to earn abnormal returns in real time. These studies have generally found that although stock returns were predictable ex post (or in-sample), the evidence of genuine ex ante (or out-of-sample) predictability appears to be much weaker. Bossaerts and Hillion (1999) find that stock returns on a range of US and international portfolios are largely unpredictable during an out-of-sample period (1990–95), while Cooper, Gutierrez and Marcum (2005) conclude that the relative returns on portfolios of stocks sorted on firm size, book-to-market value and past returns were not ex ante predictable during the period 1974–97.2Marquering and Verbeek (2004) study the economic significance of predictability in both the conditional mean and conditional variance of stock returns and conclude that the profitability of trading strategies they examine is concentrated in the first half of the sample period. Sullivan, Timmermann and White (1999) find that technical trading rules cease to identify profitable trading strategies for the period 1986–96, although there was some evidence that they managed to do so prior to this period. While these studies find evidence of instability in return forecasting models, they do not determine the time where the return models may have changed, nor do they consider the possibility of earlier structural breaks or the time of their occurrence. These are important issues to address since a plausible explanation for the discrepancy between the apparent strong in-sample predictability and the weak out-of-sample predictability is that the predictive relations are structurally unstable and change over time. Furthermore, if financial prediction models are unstable, the economic significance of return predictability can only be assessed provided it is determined how widespread such instability is both internationally and over time and the extent to which it affects the predictability of stock returns. This study investigates these questions. Using data on a sample of excess returns on international equity indices we analyze both how widespread the evidence of structural breaks is and to what extent breaks affected the predictability of stock returns. We focus on ex post or full-sample predictability, while many earlier studies have studied ex ante predictability. There are several advantages of this approach over an ex ante approach that splits stock return data into estimation and forecasting sub-samples (as is traditionally done in the literature). First, our approach allows us to date the possible time of changes in the return prediction models. In real time it is very difficult to identify such breaks and their timing can only be determined with the benefits of hindsight, i.e., by using the full sample of stock returns. Second, our approach is likely to have more power to detect changes in predictable relations. In a recent paper, Inoue and Kilian (2004) show that tests based on in-sample predictability typically have much better power than out-of-sample tests which generally use much smaller sample sizes. Indeed, it is possible that the absence of strong out-of-sample predictability in stock returns is entirely due to the use of relatively short evaluation samples. By using the full sample for our analysis, we gain sufficient power to address whether this explanation is valid or whether predictability genuinely has declined over time. More specifically, we provide a systematic analysis of the stability of forecasting models using a dataset of monthly stock returns for ten OECD countries, including all members of the G7. With the exception of the default premium, local country forecasting variables are employed. We test for the presence of structural breaks in stock returns and characterize the timing and nature of the breaks. We find evidence of breaks for the vast majority of countries in multivariate regression models for excess returns. Further, our results indicate that the relationship between particular state variables and stock returns may change substantially following a break. Empirical evidence of predictability is not uniform over time and is concentrated in certain periods. For a number of the countries examined in our study ex post predictability appears to be substantially weaker after the most recent break, although a few exceptions exist. Using a longer historical dataset for the UK and US we find evidence of a common break around 1974–1975, which we relate to the oil price shock. Additionally, there is some evidence of a common break affecting a number of European markets during the period 1978–1982. We suggest that this break may be related to the introduction of the European Monetary System in 1979 and the associated constraints imposed on monetary and fiscal policy in member nations. Our focus on international indices affords several advantages. First, the literature on stock return predictability is weighted toward US data with relatively few studies examining the question of predictability in global returns. Ang and Bekaert (2004) examine predictability for the US, UK, Germany and France while Campbell (2003) examines predictability in 11 countries using monthly data beginning in 1970. Hjalmarsson (2004) provides a comprehensive empirical investigation of global stock return predictability, using panel data that include over 20,000 monthly observations from 40 international markets, including 22 of 24 OECD nations. Rapach, Wohar and Rangvid (2002) examine both in-sample and out-of-sample performance of return prediction models for 12 countries. Broadly, the evidence reported in these studies suggests that the return predictability phenomenon extends to the global setting. Ang and Bekaert (2004), Rapach, Wohar and Rangvid (2002) and Hjalmarsson (2004) conclude that the short interest rate is a robust predictor of stock returns internationally, particularly at short horizons. The studies arrive at different conclusions, however, regarding the dividend yield as a forecasting variable. Ang and Bekaert (2004) find that the dividend yield predicts returns at short horizons when used in conjunction with the short rate, whereas Hjalmarsson (2004) concludes that there is no consistent evidence that the dividend yield (or earnings ratio) predicts returns for OECD countries. While these recent studies address the question of international stock return predictability, to our knowledge this paper is the first to systematically address the question of whether globally documented predictive relationships are stable over time. Hjalmarsson (2004) touches briefly upon this issue by presenting results from rolling regressions using a 60-month window, however, formal tests of stability are not presented. Further, Hjalmarsson (2004) considers each regressor separately in turn, while we consider multiple regression models. Finally, following recent developments in breakpoint testing, we focus on occasional, large shifts in coefficients rather than a gradual evolution and we attempt to characterize the timing and nature of breaks, as well as investigate whether the timing of breaks appears to be uniform across countries. In contemporaneous research, Rapach and Wohar (2005) find complementary evidence of instability in return regressions using US data and a broad set of forecasting variables. They apply SupF-type tests to detect the presence of breaks and apply a method suggested by Bai and Perron to select models (as we do). We demonstrate via simulation experiments that the finite sample performance of SupF-type tests can be rather poor in the presence of persistent lagged endogenous regressors. This finding is clearly relevant in the context of stock return regressions since ‘ratio’ variables such as the dividend yield and price-earnings ratio satisfy this description. Fortunately, our simulation analysis illustrates that a recent test for instability suggested by Elliott and Müller (2003) possesses excellent finite sample size properties even in the presence of persistent lagged endogenous regressors. This test provides important corroboration regarding our evidence of breaks. As further corroboration, we present results for breaks in long-horizon return regressions using cumulated returns. The breaks identified at the single-month horizon carry over to multiple-horizon regression models in most cases. The remainder of the paper is organized as follows. Section 2 introduces the breakpoint methodology applied in this study. Section 3 reports the outcome of Monte Carlo experiments for the small sample performance of break tests and model selection procedures. Section 4 describes the international returns data. Section 5 presents empirical results of tests for breakpoints and structural stability in international equity indices. Section 6 characterizes the nature of breaks, including the timing of the breaks, changes in the regressions coefficients and the predictable component of returns, and offers possible economic motivations for common breaks. Section 7 considers issues of robustness as well as several extensions of the basic results. Section 8 summarizes and further discusses our findings.

This study presents systematic empirical evidence of structural breaks in models of predictable components in international stock returns based on the lagged dividend yield, short interest rate, term spread and default premium. We find evidence of breaks for the vast majority of countries in multivariate regression models for excess returns. Further, our results indicate that the relationship between particular state variables and stock returns may change substantially following a break. Empirical evidence of predictability is not uniform over time and is concentrated in certain periods. For a number of the countries examined in our study ex post predictability appears to be substantially weaker after the most recent break, although a few exceptions exist. Using a longer historical dataset for the UK and US we find evidence of a common break around 1974–1975, which we relate to the oil price shock. Additionally, there is some evidence of a common break experienced by a number of European stockmarkets during the period 1978–1982.We suggest that this breakmay be related to the introduction of the European Monetary System in 1979 and the associated constraints imposed on monetary and fiscal policy in member nations. The presence of structural breaks in predictive return regressions raises several economically interesting issues. First, how should conditional expected returns be estimated in the presence of breaks? One possibility is to use data after the most recently identified break date. Pesaran and Timmermann (2002) propose a procedure that reverses the ordering of the data in the prediction model and estimates themodel parameters using only post-break data. This method determines the break date using a Cusum-squared test and directly addresses the question of how much historical data to use. Such an approach is, however, unlikely to work well if the data sample after the most recent break is very short. For this case, Pesaran and Timmermann (in press) prove that it can be optimal to use prebreak data provided that the break is not very large (so the bias in the predictive return regression does not get too large). They propose viewing the length of the data window used in estimation of the conditional mean as a separate parameter that is optimally chosen to trade-off (squared) bias against reduction in parameter estimation error resulting from using pre-break data. Avery active strand in the finance literature considers asset allocation problems in the presence of predictable asset returns (see, e.g., Ait-Sahalia and Brandt, 2001; Barberis, 2000; Brandt, 1999; Campbell and Viceira, 1998; Kandel and Stambaugh, 1996). The predominant approach in the literature is to presume a time-invariant relationship between forecasting variables and expected returns. Clearly, the possibility of breaks and time-variation in the conditional mean function for asset returns complicates the portfolio allocation problem. Thus, a second question is how to adjust portfolio weights in the presence of breaks to the return forecasting equation. Answering this question requires a full-blown model for the underlying breakpoint process since the predictive density of future stock returns now becomes a mixture of the return distribution conditional on no future breaks (i.e., remaining in the current regime) and the return distribution given that a break occurs. Hence the probability of a future breakmust be computed and the parameters of the return predictionmodel after a break must be drawn from some ‘meta distribution’ characterizing the parameters across the various break segments. This is best accomplished using a Bayesian approach, but relies on distributional assumptions about the regressor variables, error terms and the underlying breakpoint process that go beyond the present paper. These issues are addressed by Pettenuzzo and Timmermann (2005) who find that structural breaks can have significant effects on the optimal asset allocation.