Is stock return predictability time-varying?

Devpura, Neluka; Narayan, Paresh Kumar; Sharma, Susan Sunila

doi:10.1016/j.intfin.2017.06.001

Cited by 119 publications

(63 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 We do not discuss the derivation of the NW-FGLS estimator in detail because this has been explicitly explained and discussed in number of journal articles (see for instance, Devpura et al, 2018;Sharma, 2016;Phan, Sharma, Tran, 2018). In addition, the model has been extensively explained in the original paper of Narayan (2012, 2015).…”

Section: B Methodologymentioning

confidence: 99%

“…3 The key advantage of the WN-FGLS is that it allows us to control for three statistical aspects of the data and model that are important to the forecasting exercise. These issues relate to endogeneity already recognized as an issue in the predictability literature (see Sharma, 2016); persistency of predictor variables such that instead of diluting the information contained in predictor variables, we can use the variables in their level form; and heteroscedasticity-an issue recognized as a stylized fact in financial time-series data (see, e.g., Devpura et al, 2018;Phan, Sharma, Tran, 2018;Sharma, 2016; among others).…”

Section: B Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Which Variables Predict Indonesia's Inflation?

Sharma¹

2019

BEMP

Self Cite

View full text Add to dashboard Cite

We use an exhaustive list of Indonesia’s macroeconomic variables in a comparative analysis to determine which predictor variables are most important in forecasting Indonesia’s inflation rate. We use monthly time-series data for 30 macroeconomic variables. Using both in-sample and out-of-sample predictability evaluations, we report consistent evidence of inflation rate predictability using 11 out of 30 macroeconomic variables.

show abstract

Section: B Methodologymentioning

confidence: 99%

Section: B Methodologymentioning

confidence: 99%

Which Variables Predict Indonesia's Inflation?

Sharma¹

2019

BEMP

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ignoring such effects when they exist may bias the forecast results (see WN, 2014). This approach has also been used to analyse the predictive model for stock returns nexus both at the aggregate and firm levels (see Narayan and Gupta, 2014;Narayan and Sharma, 2014;Bannigidadmath and Narayan, 2015;Narayan and Bannigidadmath, 2015;Devpura et al, 2017;, inflation (see Salisu and Isah, 2017a; and expenditure (see Makin et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling return and volatility spillovers in global foreign exchange markets

Salisu

Oyewole

Fasanya

2018

Journal of Information and Optimization Sciences

View full text Add to dashboard Cite

The extant literature on exchange rate forecasting on the basis of the Dornbusch-Frankel, Frenkel-Bilson and Hooper-Morton models prominently reveals the dominance of the autoregressive models over the theory-based models. Some studies have however attempted to upturn the results by including the lagged dependent variable in the theory-based models which somewhat implies comparing a modified random walk with a traditional random walk. We follow a different approach both in terms of theory and methodology. We offer an innovative exposition of the Portfolio Balance theory to stock price-exchange rate nexus. Consequently, a predictive model for exchange rate where stock price is a predictor is formulated. The formulated model is expressed in both linear and nonlinear form in order to account for the role of asymmetric changes in stock prices in exchange rate forecasting. Thereafter, we employ the Lewellen (2004) and Westerlund and Narayan (2014) methods which account for any inherent statistical properties of the predictors. Our results validate the Portfolio Balance theory where we show that the sector-level stock prices consistently turn up as good predictors of the exchange rates. The predictive model proposed in this work does not require the inclusion of a lagged dependent variable to beat the autoregressive models which is the practice in the existing literature. We further demonstrate that asymmetry matters to a large extent in the nexus both for the in-sample and out-of-sample predictability.

show abstract

“…Consequently, the approach of [1] [2], which accommodates salient features such as endogeneity, persistence and conditional heteroscedasticity in the predictors of a series, is employed. This approach has been employed by a [12] [13]. The choice of GCC countries in this study is deliberate.…”

Section: Introductionmentioning

confidence: 99%

Improving the Forecast Accuracy of Oil-Stock Nexus in GCC Countries

Nnachi¹

2018

TEL

View full text Add to dashboard Cite

This paper renders new insights into the predictability of GCC stock returns using crude oil prices using the approach of [1] [2] that accounts for salient features of the predictor. The results show superior performance of the oil-based stock model over time-series models (namely, AR, MA, ARMA, and ARFIMA) for both in-sample and out-of-sample forecasts. The results are robust to different oil price series (Brent and WTI prices) and forecast horizons (30 and 60 days).

show abstract

Is stock return predictability time-varying?

Cited by 119 publications

References 42 publications

Which Variables Predict Indonesia's Inflation?

Which Variables Predict Indonesia's Inflation?

Modelling return and volatility spillovers in global foreign exchange markets

Improving the Forecast Accuracy of Oil-Stock Nexus in GCC Countries

Contact Info

Product

Resources

About