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Diverging roads: theory-based vs. machine learning-implied stock risk premia

Grammig, Joachim; Hanenberg, Constantin; Schlag, Christian - 2020

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the … preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical … models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine …

Persistent link: https://www.econbiz.de/10012163064

Censored posterior and predictive likelihood in Bayesian left-tail prediction for accurate value at risk estimation

Gatarek, Lukasz; Hoogerheide, Lennart; Hooning, Koen; … - 2013

Persistent link: https://www.econbiz.de/10009756308

Forecasting the Equity Premium with Frequency-Decomposed Technical Indicators

Stein, Tobias - 2020

Technical trading rules are widely used by practitioners to forecast the U.S. equity premium. I decompose technical indicators into components with frequency-specific information, showing that the predictive power comes from medium-frequency variation in buy and sell signals, without much...

Persistent link: https://www.econbiz.de/10012839601

Equity Premium Prediction by Sparse Pooling of Parsimonious State-Dependent Models

de Almeida, Daniel - 2018

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. This paper proposes a forecasting approach for the equity risk premium with two novel features. First, individual month-ahead forecasts are obtained from parsimonious threshold...

Persistent link: https://www.econbiz.de/10012913585

The Variance Risk Premium and Fundamental Uncertainty

Conrad, Christian - 2015

We propose a new measure of the expected variance risk premium that is based on a forecast of the conditional variance from a GARCH-MIDAS model. We find that the new measure has strong predictive ability for future U.S. aggregate stock market returns and rationalize this result by showing that...

Persistent link: https://www.econbiz.de/10013027179

Time-frequency Forecast of the Equity Premium

Faria, Gonçalo - 2020

Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We...

Persistent link: https://www.econbiz.de/10012835434

Time-frequency forecast of the equity premium

Faria, Gonçalo; Verona, Fabio - 2020

Persistent link: https://www.econbiz.de/10012208225

Equity Premium Predictions with Adaptive Macro Indexes

Bai, Jennie - 2014

Fundamental economic conditions are crucial determinants of equity premia. However, commonly used predictors do not adequately capture the changing nature of economic conditions and hence have limited power in forecasting equity returns. To address the inadequacy, this paper constructs macro...

Persistent link: https://www.econbiz.de/10013069149

Censored Posterior and Predictive Likelihood in Bayesian Left-Tail Prediction for Accurate Value at Risk Estimation

Gatarek, Lukasz T. - 2014

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

Persistent link: https://www.econbiz.de/10013064150

Macroeconomic Extrapolation, Machine Learning, and Equity Risk Premium Forecast

Lu, Yueliang (Jacques); Han, Yufeng - 2022

We present a macroeconomic trend extrapolation approach that utilizes all economic fundamentals of different time periods simultaneously in the aggregate market. We demonstrate that the trend-pooling method statistically and economically outperforms the historical average that assumes a constant...

Persistent link: https://www.econbiz.de/10013406571

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