-switching models, and forecast combination to predict the dynamics in the S&P 500. First, we aggregate the weekly information of 115 …

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 …

predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out … model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing …

Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More … growth level, from which the actual stock price may deviate as an effect of random external disturbances, and iii) a driving …

utilised to predict returns on the price of gold.We first demonstrate that analysts are at least in part basing their earnings … coverage impounds not only market-wide and industry information, but also gold price information for these firms—as measured … via its impact on stock return synchronicity.We then examine if the difference between forecast and observed earnings for …

respective forecast accuracy and test whether the forecasted information are relevant to equity or debt markets. The results, in … terms of forecast accuracy in firm-level tests. This study finds that the opposing performance outcome appears to be driven …

forecast after observing the one produced by the algorithm (a condition found to mitigate algorithm aversion), the average …