Forecasting stock returns with model uncertainty and parameter instability

Zhang, Hongwei; He, Qiang; Jacobsen, Ben; Jiang, Fuwei

doi:10.1002/jae.2747

Cited by 22 publications

(2 citation statements)

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“…Parameter instability remains a rare focus of studies in the field of forecasting (Catania et al, 2019;Pettenuzzo & Timmermann, 2017;Y. Wang & Hao, 2023;Zhang et al, 2020).…”

Section: Out-of-sample Proceduresmentioning

confidence: 99%

Google Trends Forecasting: Model re-specification of time-series for enhancing forecasts of youth unemployment

Wijnhoven,

De Bruijn,

Effing

2023

Preprint

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In the last decades, the forecasting field has been using the surge in big data and advanced computational capabilities. Despite these developments, forecasters continue using traditional forecasting procedures that assume static relationships between phenomena. To address the reality of dynamic relations among phenomena, this study discusses time-variant re-specification methods as part of time-series based forecasts and compares the outcomes with the traditional procedures. This method-comparison is applied to a real-world exercise, the forecasting of Dutch youth unemployment with big data based on Google Trends. For youth unemployment forecasts, our results show 44% more forecasting accuracy by time-varying forecasting models than the traditional static forecasting models. Additionally, this study makes labour market forecasting an accessible endeavour to all organizations by sharing the algorithm for forecasting youth unemployment rates with publicly available data such as Google Trends. Moreover, our study stresses a reconsideration of forecasting methodologies towards model re-specification instead of model recalibration.

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“…Parameter instability remains a rare focus of studies in the field of forecasting (Catania et al, 2019;Pettenuzzo & Timmermann, 2017;Y. Wang & Hao, 2023;Zhang et al, 2020).…”

Section: Out-of-sample Proceduresmentioning

confidence: 99%

Google Trends Forecasting: Model re-specification of time-series for enhancing forecasts of youth unemployment

Wijnhoven,

De Bruijn,

Effing

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Several methods of finding the (optimal) combination forecast have been proposed in a large body of literature: for example, a weighted average of forecasts, with the weights adding up to unity (Granger & Ramanathan, 1984); trimming (Granger & Jeon, 2004); rankbased approaches (Aiolfi & Timmermann, 2006); a least-squares forecast averaging (Hansen, 2008b); a complete subset regression (Elliott et al, 2013); iterated (Lin et al, 2018) or depthweighted combinations (Lee & Sul, 2021). Recently, ML techniques have been proposed to select and weight appropriate individual forecasts using, for example, Lasso-based procedures (Diebold & Shin, 2019;Mascio et al, 2020;Freyberger et al, 2020); a combining method for sophisticated models with the historical average serving as shrinkage target (Zhang et al, 2020); or the Combination Elastic Net (Rapach & Zhou, 2020). However, in many practical applications, the simple average of candidate forecasts is more robust than more sophisticated combination approaches (Qian et al, 2019), a phenomenon known as the forecast combination puzzle.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Scholz

2022

Ann Oper Res

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Forecast combinations are a popular way of reducing the mean squared forecast error when multiple candidate models for a target variable are available. We apply different approaches to finding (optimal) weights for forecasts of stock returns in excess of different benchmarks. Our focus lies thereby on nonlinear predictive functions estimated by a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. Based on an out-of-sample study, we find that individual nonparametric models outperform their forecast combinations. The latter are prone to in-sample over-fitting and in consequence, perform poorly out-of-sample especially when the set of possible candidates for combinations is large. A reduction to one-dimensional models balances in-sample and out-of-sample performance.

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Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning

Qu,

Yuan

2024

Journal of Forecasting

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We propose a new momentum‐determined indicator‐switching (N‐MDIS) strategy, harnessing the power of machine learning to enhance the accuracy of equity premium prediction. Specifically, we re‐examine the regime‐dependent feature of univariate predictive regression relative to the benchmark. Furthermore, we investigate the prediction mechanism of the momentum‐determined indicator‐switching (MDIS) strategy and validate the significance of market regime information for the MDIS. Our findings demonstrate an overwhelmingly superior ex‐post forecasting performance compared with the MDIS. More notably, our empirical results substantiate that machine learning greatly aids in momentum indicator selection. The results show that the N‐MDIS with machine learning generates more accurate ex‐ante equity premium forecasts than both MDIS strategy and N‐MDIS strategy with logistic regression, yielding statistically and economically significant results. Moreover, our new approach exhibits robust forecasting performance across a series of robustness tests.

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Forecasting stock returns with model uncertainty and parameter instability

Cited by 22 publications

References 50 publications

Google Trends Forecasting: Model re-specification of time-series for enhancing forecasts of youth unemployment

Google Trends Forecasting: Model re-specification of time-series for enhancing forecasts of youth unemployment

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning

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