election outcomes. Prediction market prices have been shown to be consistently accurate forecasts of these outcomes, but we don … incentives to acquire information about the outcome, thus tend to have better forecasts. Moreover, their trades have larger …

yield curve forecasts while explaining the stylized facts of the term structure of interest rates. It is flexible enough to …-, and 12-month out-of-sample forecasts with statistical significance at 1, 3, and 6 months. Interestingly, it prefers the …, some relevant hyperparameters need to vary with the forecast horizon rather than uniformly in order to improve forecasting …

ability to forecast stock prices. We show that these persistent differences in accuracy are driven instead by stock return … volatility. Building upon option pricing theory, we construct a measure of forecast quality that controls for stock return … volatility and forecast horizon. Contrary to previous studies, which failed to properly account for differences in stock return …

each method. Last, we propose a combination forecast approach for estimating VaR. Our findings suggest that both the …

(integrated) production of information. Nonetheless, this paper documents significant stock return and forecast revision …

-called "curse of dimensionality" and produce biased forecasts. In this paper, we relied on dimensionality reduction methods to … Factorization (NMF) and Least Absolute Shrinkage and Selection Operator (LASSO) with hybrid artificial neutral networks to forecast …