This article addresses decision-making parameters in machine-learning algorithms, and more specifically, critiques the mathematical explanation for the Hipster Effect (a group of evolutionary processes), and discusses implications for portfolio management and corporate bankruptcy prediction (two...

This paper proposes a simple technical approach for the analytical derivation of Point-in-Time PD (probability of default) forecasts, with minimal data requirements. The inputs required are the current and future Through-the-Cycle PDs of the obligors, their last known default rates, and a...

The existing literature finds that information not captured by traditional term structure factors helps predict excess bond returns. When estimating no-arbitrage affine term structure models, aligning in-sample and out-of-sample objective functions results in term structure factors that capture...

This paper presents a mathematical model to make optimal trading decisions using forecasts made by multivariate regression. Given that these results are uncertain, the model maximizes the expected profit from opening and closing trade positions. To this end, a dynamic programming approach is...

Short-term forecasting is usually made in recent literature by modeling the spot price of commodities such as coffee and cattle with ARIMA models and in some articles including volatility models. Unlike such articles, herein the models of the spot price of coffee and cattle are estimated...

We quantify disagreement about the economy with ex-ante measures of divergence of opinion among economic forecasters and investigate if economic disagreement has a significant impact on the cross-sectional pricing of individual stocks. We find a significant disagreement premium of 7.2% per...

In this article we review the state of play in the use of stochastic models for the measurement and management of longevity risk. A focus of the discussion concerns how robust these models are relative to a variety of inputs: something that is particularly important in formulating a risk...

We adjust the dividend-price ratio for share repurchases and investigate whether predictive power can be improved when constructing forecasts of UK and French equity premia. Regulations in the two largest European stock markets allow us to employ actual repurchase data in our predictive...

We analyze the optimal stock-bond portfolio under both learning and ambiguity aversion. Stock returns are predictable by an observable and an unobservable predictor, and the investor has to learn about the latter. Furthermore, the investor is ambiguity-averse and has a preference for investment...

We propose an ex ante measure of analysts' production of private information (PPI) based on the correlations between analysts' forecast revisions and prior stock price changes. We validate this measure by examining whether analysts with lower correlations (higher PPI) provide more information...