Do Return Prediction Models Add Economic Value?

Cenesizoglu, Tolga; Timmermann, Allan

doi:10.2139/ssrn.1944052

Cited by 51 publications

(44 citation statements)

References 28 publications

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“…Therefore, our results corroborate the evidence presented by (Cenesizoglu and Timmermann, 2012), which reveals that there is a positive relation between the statistical and economic performance measures.…”

Section: Resultssupporting

confidence: 91%

“…The Our results reveal that, although there is not an overwhelming statistical evidence of predictive ability, the economic performance of the models is, generally, very good. In order to test if the economic and statistical measures of performance are related, we follow (Cenesizoglu and Timmermann, 2012) and compute the linear correlation and the rank correlation between these measures. Table 4 presents the results.…”

Section: Resultsmentioning

confidence: 99%

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Provas Académicas na FEUC

Económicas¹

2015

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Provas Académicas na FEUC

Económicas¹

2015

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“…Our findings are substantiated by employing both statistical and economic measures of forecast accuracy, as they yield remarkably similar results in our application. This is an interesting result in itself because, for example, Cenesizoglu and Timmermann (2012) have suggested that the results from statistical tests and investment strategies are not always in line with each other.…”

Section: Conclusion and Possible Extensionsmentioning

confidence: 93%

“…Similarly, we need to convert the results from predictive regressions into sign forecasts. This is done in the same way as for the realized excess market returns in (2). In other words, ifRE t in model (1) is positive, we get a signal forecastŷ t = 1.…”

Section: Goodness-of-fit Measures and Statistical Testsmentioning

confidence: 99%

Predicting the direction of US stock markets using industry returns

Pönkä

2016

Empir Econ

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In this paper, we examine the directional predictability of excess stock market returns by lagged excess returns from industry portfolios and a number of other commonly used variables by means of dynamic probit models. We focus on the directional component of the market returns because, for investment purposes, forecasting the direction of return correctly is presumably more relevant than the accuracy of point forecasts. Our findings suggest that only a small number of industries have predictive power for market returns. We also find that the binary response models outperform conventional predictive regressions in forecasting the direction of the market return. Finally, we test trading strategies and find that a number of industry portfolios contain information that can be used to improve investment returns.JEL Classification: C25, C53, C58, G17

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“…However, many report a strong comovement between the actual spread and the spread predicted by the EH, thereby making it di¢ cult to reject a hypothesis that is economically signi…cant. 6 Here we use a Multivariate VAR in Transformed Interest Rates (MVART) model, which embeds the cointegrating relations between the yields as implied by the EH, to capture predictability.…”

Section: Introductionmentioning

confidence: 99%

Decision‐Based Forecast Evaluation of UK Interest Rate Predictability

Sirichand

Hall

2015

Journal of Forecasting

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This paper illustrates the importance of density forecasting and forecast evaluation in portfolio decision making. The decision‐making environment is fully described for an investor seeking to optimally allocate her portfolio between long and short Treasury bills, over investment horizons of up to 2 years. We examine the impact of parameter uncertainty and predictability in bond returns on the investor's allocation and we describe how the forecasts are computed and used in this context. Both statistical and decision‐based criteria are used to assess the predictability of returns. Our results show sensitivity to the evaluation criterion used and, in the context of investment decision making under an economic value criterion, we find some potential gain for the investor from assuming predictability. Copyright © 2015 John Wiley & Sons, Ltd.

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Do Return Prediction Models Add Economic Value?

Cited by 51 publications

References 28 publications

Provas Académicas na FEUC

Provas Académicas na FEUC

Predicting the direction of US stock markets using industry returns

Decision‐Based Forecast Evaluation of UK Interest Rate Predictability

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