The Aligned Economic Index & The State Switching Model
- URL: http://arxiv.org/abs/2512.20460v2
- Date: Mon, 29 Dec 2025 02:59:07 GMT
- Title: The Aligned Economic Index & The State Switching Model
- Authors: Ilias Aarab,
- Abstract summary: I study U.S. stock return predictability across economic regimes and document strong evidence of time-varying expected returns.<n>I introduce a state-switching predictive regression in which the market state is defined in real time using the slope of the yield curve.<n>I propose a new aggregate predictor, the Aligned Economic Index, constructed via partial least squares (PLS)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A growing empirical literature suggests that equity-premium predictability is state dependent, with much of the forecasting power concentrated around recessionary periods (Henkel et al., 2011; Dangl and Halling, 2012; Devpura et al., 2018). I study U.S. stock return predictability across economic regimes and document strong evidence of time-varying expected returns across both expansionary and contractionary states. I contribute in two ways. First, I introduce a state-switching predictive regression in which the market state is defined in real time using the slope of the yield curve. Relative to the standard one-state predictive regression, the state-switching specification increases both in-sample and out-of-sample performance for the set of popular predictors considered by Welch and Goyal (2008), improving the out-of-sample performance of most predictors in economically meaningful ways. Second, I propose a new aggregate predictor, the Aligned Economic Index, constructed via partial least squares (PLS). Under the state-switching model, the Aligned Economic Index exhibits statistically and economically significant predictive power in sample and out of sample, and it outperforms widely used benchmark predictors and alternative predictor-combination methods.
Related papers
- Monitoring State Transitions in Markovian Systems with Sampling Cost [65.4151496405543]
A natural approach is a greedy policy that predicts when the expected prediction loss is below the query cost and queries otherwise.<n>We analyze this policy in a Markovian setting, where the optimal (OPT) strategy is a state-dependent threshold policy.<n>For the case of unknown transition probabilities, we propose a projected gradient descent (PSGD)-based learning variant of the greedy policy.
arXiv Detail & Related papers (2025-10-25T15:07:37Z) - Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression [0.0]
We examine three postprocessing methods for converting point forecasts of day-ahead electricity prices into probabilistic ones.
We find that while the latter demonstrates the most varied behavior, it contributes the most to the ensemble of the predictive distributions.
Remarkably, the performance of the combination is superior to that of state-of-the-art Distributional Deep Neural Networks over two 4.5-year test periods from the German and Spanish power markets.
arXiv Detail & Related papers (2024-04-02T19:50:36Z) - Inside the black box: Neural network-based real-time prediction of US recessions [0.0]
Long short-term memory (LSTM) and gated recurrent unit (GRU) are used to model US recessions from 1967 to 2021.
Shap method delivers key recession indicators, such as the S&P 500 index for short-term forecasting up to 3 months.
arXiv Detail & Related papers (2023-10-26T16:58:16Z) - Performative Time-Series Forecasting [64.03865043422597]
We formalize performative time-series forecasting (PeTS) from a machine-learning perspective.<n>We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts.<n>We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
arXiv Detail & Related papers (2023-10-09T18:34:29Z) - Variational Prediction [95.00085314353436]
We present a technique for learning a variational approximation to the posterior predictive distribution using a variational bound.
This approach can provide good predictive distributions without test time marginalization costs.
arXiv Detail & Related papers (2023-07-14T18:19:31Z) - Predictive Inference with Feature Conformal Prediction [80.77443423828315]
We propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces.
From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions.
Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods.
arXiv Detail & Related papers (2022-10-01T02:57:37Z) - A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator.
We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
arXiv Detail & Related papers (2022-05-20T22:13:25Z) - The Yield Curve as a Recession Leading Indicator. An Application for
Gradient Boosting and Random Forest [0.0]
We find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection.
This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation.
arXiv Detail & Related papers (2022-03-13T12:46:22Z) - Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
This paper aims to capture the movement pattern of stock prices under anomalous circumstances.
We train ARIMA and LSTM models at the single-stock level, industry level, and general market level.
Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
arXiv Detail & Related papers (2021-09-14T18:50:38Z) - CovarianceNet: Conditional Generative Model for Correct Covariance
Prediction in Human Motion Prediction [71.31516599226606]
We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories.
Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables.
arXiv Detail & Related papers (2021-09-07T09:38:24Z) - Economic Recession Prediction Using Deep Neural Network [26.504845007567972]
We identify the deep learning methodology of Bi-LSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S.
We adopt commonly-available macro and market-condition features to compare the ability of different machine learning models to generate good predictions both in-sample and out-of-sample.
arXiv Detail & Related papers (2021-07-21T22:55:14Z) - When in Doubt: Neural Non-Parametric Uncertainty Quantification for
Epidemic Forecasting [70.54920804222031]
Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions.
Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations.
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
arXiv Detail & Related papers (2021-06-07T18:31:47Z) - Models, Markets, and the Forecasting of Elections [3.8138805042090325]
We find systematic differences in accuracy over time, with markets performing better several months before the election, and the model performing better as the election approached.
We propose a market design that incorporates model forecasts via a trading bot to generate synthetic predictions.
arXiv Detail & Related papers (2021-02-06T19:05:07Z) - Ensemble Forecasting for Intraday Electricity Prices: Simulating
Trajectories [0.0]
Recent studies have shown that the hourly German Intraday Continuous Market is weak-form efficient.
A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window.
The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe.
arXiv Detail & Related papers (2020-05-04T10:21:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.