Equity premium prediction: The role of economic and statistical constraints

Li, Jiahan; Tsiakas, Ilias

doi:10.1016/j.finmar.2016.09.001

Cited by 77 publications

(49 citation statements)

References 40 publications

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“…Consistent with similar findings documented in related studies such as Pettenuzzo et al (2014) and Li and Tsiakas (2017), all four weighting schemes perform better during recessions than during expansions in terms of the 2 statistic. For monthly forecasts, all methods achieve about 2.6% in MSFE reduction relative to the prevailing mean during expansions.…”

Section: Expansion and Recession Analysissupporting

confidence: 91%

“…In addition to the metric assessing statistical performance, the quality of returns prediction is often assessed based on the financial gains generated by the underlying models. Therefore, we evaluate and compare the economic gains measured according to the relative annualized certainty equivalent return (CER) and Sharpe ratio following related studies such as Ferreira and Santa-Clara (2011) and Li and Tsiakas (2017). In our empirical results, the two-stage forecast combination always leads to superior economic gains relative to the historical average across all time horizons and subsamples.…”

Section: Introductionmentioning

confidence: 89%

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Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach

Yin¹

2019

IJEF

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This paper introduces a two-stage out-of-sample predictive model averaging approach to forecasting the U.S. market equity premium. In the first stage, we combine the break and stable specifications for each candidate model utilizing schemes such as Mallows weights to account for the presence of structural breaks. Next, we combine all previously averaged models by equal weights to address the issue of model uncertainty. Our empirical results show that the double-averaged model can deliver superior statistical and economic gains relative to not only the historical average but also the simple forecast combination when forecasting the equity premium. Moreover, our approach provides an explicit theory-based linkage between forecast combination and structural breaks which distinguishes this study from other closely related works.In response to the findings reported in Goyal and Welch (2008), recent developments in the literature of forecasting equity returns show that the predictive power of various predictors can be uncovered or restored once an appropriate estimation methodology other than OLS is employed. Based on the general framework considered in Goyal and Welch (2008), Rapach, Strauss, and Zhou (2010) demonstrate that forecast combination could consistently improve upon the historical mean benchmark over time in terms of both statistical and economic gains. Additionally, they argue that, due to the uncertainty regarding model selection and parameter instability, the benefits of forecast combination come from taking advantage of all available information and its linkage to the real economy. However, it is not clear how the weighting methods combining models considered in Rapach et al. (2010) are explicitly linked with structural breaks. Specifically, despite the empirical evidence of instability documented in works such as Rapach and Wohar (2006), Rapach et al. (2010) do not consider any candidate model which allows for breaks when combining models, resulting in difficulty in interpreting the empirical results linking the success of forecast combination with structural breaks.Our main contribution to the literature is to introduce a two-stage forecast combination methodology which improves upon the simple model averaging and can be easily implemented in empirical works. Our approach explicitly accounts for the possible presence of structural breaks beside the uncertainty on model selection,

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Section: Expansion and Recession Analysissupporting

confidence: 91%

Section: Introductionmentioning

confidence: 89%

Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach

Yin¹

2019

IJEF

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“…Campbell and Thompson (2008), Ferreira andSanta-Clara (2011), andPettenuzzo, Timmermann, andValkanov (2014) show that the application of economic constraints generally increases the overall forecast performance of commonly used macroeconomic variables. However, improvements are predominantly located in the distant past (see Li & Tsiakas, 2016;Pettenuzzo et al, 2014). Further forecasting advances incorporate the usage of pooling strategies like principal component analysis (proposed by Stock & Watson, 2002b) or forecast combination methods (proposed by Rapach, Strauss, & Zhou, 2010).…”

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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“…We use the following standard univariate predictive regression model to analyze US market return predictability based on each aggregate idiosyncratic momentum predictor:

R_{t + 1}^{e} = α_{i} + β_{i} \times i {MOM}_{i, t} + ε_{i, t + 1},

where

R_{t + 1}^{e}

is the excess market return for month

t + 1

. We use the monthly log excess return on the S&P 500 index as in the prior literature (e.g., Huang et al, ; Li & Tsiakas, ; Welch & Goyal, );

i {MOM}_{i, t}

includes the five aggregate idiosyncratic momentum variables for month

t

(

i {MOM}^{CAPM}, i {MOM}^{FF 3}, i {MOM}^{normalC 4}, i {MOM}^{FF 5}

, and

i {MOM}^{normalQ 4}

). We use a value‐weighted average of idiosyncratic momentum, as we focus on predicting returns on the value‐weighted market portfolio, and

ε_{i, t + 1}

is a zero‐mean disturbance term.…”

Section: Idiosyncratic Momentum and Aggregate Market Returnsmentioning

confidence: 99%

“…We also construct an aligned index by employing the partial least squares (PLS) technique proposed by Kelly and Pruitt (, ) and find that the predictive power of the aligned index cannot remain out of sample, echoing the finding of Welch and Goyal (), which cautions about the potential fragility of in‐sample market return predictability. We also impose economic constraints on the sign of slope coefficients and return forecasts, as suggested by Campbell and Thompson () and Li and Tsiakas (). Consistently, imposing economic constraints indeed improves the forecasting power of the aligned index.…”

Section: Introductionmentioning

confidence: 99%

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

Lin

2019

Euro Fin Management

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In this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross-sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firmlevel idiosyncratic momentum effect cannot extend to the aggregate stock market.

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Equity premium prediction: The role of economic and statistical constraints

Cited by 77 publications

References 40 publications

Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach

Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach

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

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

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