Predicting returns in the stock and bond markets

Keim, Donald B.; Stambaugh, Robert F.

doi:10.1016/0304-405x(86)90070-x

Cited by 1,556 publications

(794 citation statements)

References 38 publications

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“…This line of research has quite some history for equity premium prediction and has developed established indicators, such as the short-term interest rate, the credit and the term spread (see, e.g., Ang & Bekaert, 2007;Fama & French, 1989; Keim & Stambaugh, 1986), the inflation rate (e.g., Campbell & Vuolteenaho, 2004;Fama & Schwert, 1977;Nelson, 1976), stock market volatility (investigated by Guo, 2006), or the consumption-wealth ratio provided by Lettau and Ludvigson (2001), to name just a few. Most of the empirical studies (e.g., Campbell & Shiller, 1988;Cochrane, 2008;Lewellen, 2004) use valuation ratios such as the dividend yield, the price-earnings ratio, or the book-to-market ratio, which should serve as proxies for expected business conditions, as mentioned by Campbell and Diebold (2009).…”

Section: Introductionmentioning

confidence: 99%

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

Baetje

2017

Journal of Forecasting

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A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on lowdimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in-sample predictive ability. However, the out-of-sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias-efficiency trade-off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.

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Section: Introductionmentioning

confidence: 99%

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

Baetje

2017

Journal of Forecasting

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“…Thus, the decline in stock prices or the probability of a decline in stock prices through an increase in volatility causes an increase in leverage and the firm's default risk, which in turn leads to an increase in the spreads and a decline in the debt's price. The negative relationship between stock returns and credit spreads has been validated by many empirical studies: Ramaswami (1991), Shan (1994) and Kwan (1996) at the level of the individual firm; Keim and Stambaugh (1986), Campbell and Ammer (1993) at the aggregate level; and Blume et al (1991) and Cornell and Gren (1991) at the portfolio level. Campbell and Taksler (2003) found a significant positive relationship between asset volatility and credit spreads.…”

Section: Theoretical and Empirical Backgroundmentioning

confidence: 86%

Modelling credit spreads with time volatility, skewness, and kurtosis

Clark

Baccar

2015

Ann Oper Res

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This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing.

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“…The seminal studies of Keim and Stambaugh (1986), Fama and French (1988) and Campbell and Shiller (1988) empirically demonstrated that variables such as dividend yield, book-to-market ratio, or interest rate spreads have significant predictive ability over future stock returns. Fama (1991) interpreted these findings as evidence of time-varying risk premia rather than as evidence against market efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction

Cheng¹,

Gao²,

Linton³

2018

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In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive regression model, in which the predictive variables are stochastically nonstationary. We also establish the estimation theory and asymptotic properties for these models in the short horizon and long horizon case. To evaluate the effectiveness of these models, we investigate their capability of stock return prediction. The empirical resultsshow that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we find that these models can always beat the historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting.

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Predicting returns in the stock and bond markets

Cited by 1,556 publications

References 38 publications

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

Modelling credit spreads with time volatility, skewness, and kurtosis

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction

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