This paper examines the long‑term dependence between the Polish and German stock markets in terms of industry beta risk estimates according to the Capital Asset Pricing Model (CAPM). The main objective of this research is to compare the Polish and German beta parameters of five Polish and...

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...

In this paper, we show that the way in which fund managers are compensated can, under plausible conditions, lead them to act in a way that does not maximise the wellbeing of their clients. Due to performance bonuses in fund managers' rewards, there is a highly non-linear relationship between the...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

We introduce a hierarchical Bayes approach to model conditional firm-level alphas as a function of firm characteristics. Our empirical framework is motivated by growing concerns in the literature regarding the reliability of inferences from portfolio-based methods. In our initial tests, we...

We propose a new approach to imposing economic constraints on forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two...

To the surprise of, in all likelihood, not only business journalists, the available evidence on the effects of political variables on both stock returns and volatility is scant and mixed. We investigate whether this weak and conflicting evidence may be due to limited sample sizes and too narrow...

A tree-structured linear and quantile regression framework is proposed for the analysis and modeling of equity market returns. The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian...

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls below the variable's past 12-month high....

Long-run risk models, a cornerstone in the macro-finance literature for their ability to capture key asset price phenomena, are known to entail implausibly high levels of timing and risk premia. Our paper resolves this puzzle by considering consumption of durable goods in addition to that of...