scholarly journals Reconciling the Return Predictability Evidence In-Sample Forecasts, Out-of-Sample Forecasts, and Parameter Instability*

2005 ◽  
Author(s):  
Stijn Van Nieuwerburgh ◽  
Martin Lettau
Keyword(s):  
Return Predictability ◽  
Parameter Instability ◽  
Out Of Sample

scholarly journals Out-of-sample stock return predictability in Australia

2012 ◽  
Vol 37 (3) ◽  
pp. 461-479 ◽  
Author(s):  
Yiwen (Paul) Dou ◽  
David R. Gallagher ◽  
David Schneider ◽  
Terry S. Walter
Keyword(s):  
Stock Return ◽  
Return Predictability ◽  
Stock Return Predictability ◽  
Out Of Sample

Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis

2018 ◽  
Vol 44 (3) ◽  
pp. 10-24
Author(s):  
Giulia Dal Pra ◽  
Massimo Guidolin ◽  
Manuela Pedio ◽  
Fabiola Vasile
Keyword(s):  
Stock Return ◽  
Return Predictability ◽  
Portfolio Analysis ◽  
Regime Shifts ◽  
Stock Return Predictability ◽  
Out Of Sample

scholarly journals Revisiting the Dynamic Linkages of Treasury Bond Yields for the BRICS: A Forecasting Analysis

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 102-129
Author(s):  
Stelios Bekiros ◽  
Christos Avdoulas
Keyword(s):  
Interest Rates ◽  
Error Correction ◽  
Term Structure ◽  
Parameter Instability ◽  
Vector Error Correction ◽  
Brics Countries ◽  
Vector Error ◽  
Long Run ◽  
Out Of Sample ◽  
Dynamic Linkages

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among interest rates was established by employing the Vector Error Correction modeling (VECM), which revealed the validation of the Expectation Hypothesis Theory (EH) of the term structure of interest rates, taking into account long-run deviations from equilibrium and inherent nonlinearities. We unveiled short-run dynamic adjustments for the term structure of the BRICS, subject to regime switches. We then used Markov Switching Vector Error Correction models (MS-VECM) to forecast them dynamically during an out-of-sample period of May 2016 through August 2019. The MSIH-VECM forecasts were found to be superior to the VECM approaches. The novelty of our paper is mainly due to the exploration of the possibility of parameter instability as a crucial factor, which might explain the rejection of the restricted version of the cointegration space, and on the dynamic out-of-sample forecasts of the term structure over a more recent time span in order to assess further the usefulness of our nonlinear MS-VECM characterization of the term structure, capturing the effects of the global and domestic financial crisis.

scholarly journals Break Risk

2020 ◽  
Author(s):  
Simon C Smith ◽  
Allan Timmermann
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Prediction Models ◽  
Return Predictability ◽  
Present Value ◽  
New Approach ◽  
Present Value Model ◽  
Out Of Sample ◽  
Rate Processes ◽  
Value Model

Abstract We develop a new approach to modeling and predicting stock returns in the presence of breaks that simultaneously affect a large cross-section of stocks. Exploiting information in the cross-section enables us to detect breaks in return prediction models with little delay and to generate out-of-sample return forecasts that are significantly more accurate than those from existing approaches. To identify the economic sources of breaks, we explore the asset pricing restrictions implied by a present value model which links breaks in return predictability to breaks in the cash flow growth and discount rate processes.

Out-of-sample stock return predictability in emerging markets

2016 ◽  
Vol 58 (3) ◽  
pp. 727-750
Author(s):  
Afsaneh Bahrami ◽  
Abul Shamsuddin ◽  
Katherine Uylangco
Keyword(s):  
Emerging Markets ◽  
Stock Return ◽  
Return Predictability ◽  
Stock Return Predictability ◽  
Out Of Sample

Forecasting Islamic securities index using artificial neural networks: performance evaluation of technical indicators

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faheem Aslam ◽  
Khurrum S. Mughal ◽  
Ashiq Ali ◽  
Yasir Tariq Mohmand
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sine Wave ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Content Type ◽  
Parameter Instability ◽  
Out Of Sample ◽  
Selection Step ◽  
Artificial Neural

PurposeThe purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).Design/methodology/approachThe data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into training and test data sets by using the 80:20 ratio, which corresponds to 2016 and 504 observations, respectively. In total, 25 features are used; however, in model selection step, based on maximum accuracy, top ten indicators are selected from several iterations of predictive models.FindingsThe results of feature selection show that top five influencing indicators on Islamic index include Bollinger Bands, Williams Accumulation Distribution, Aroon Oscillator, Directional Movement and Forecast Oscillator while Mesa Sine Wave is the least important. The findings show that the model captures much of the trend and some of the undulations of the original series.Practical implicationsThe findings of this study may have important implications for investment and risk management by using index-based products.Originality/valueNumerous studies proved that traditional econometric techniques face significant challenges in out-of-sample predictability due to model uncertainty and parameter instability. Recent studies show an upsurge of interest in machine learning algorithms to improve the prediction accuracy.

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