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...

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...

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...

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...

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...

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...

The US Treasury effectively ”owns” about 24% of the stocks held by high income US taxable investors. Through the capital gains tax, Uncle Sam has an effective exposure of more than $1 trillion of equities. And this huge-but-silent investor might be about to get a lot bigger if capital gains...

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...

This paper examines the effects of multivariate outliers on factor models of the stochastic discountfactor (SDF) through expected returns estimates. To demonstrate these effects clearly within a machine learning context we use the estimation method of Kozak et al. (2020) and an outlier robust...

The proliferation of anomalies and the resulting `factor zoo' has challenged finance researchers to identify firm characteristics that are genuinely related to the cross-sectional variation in expected stock returns. We address this challenge using a Bayesian ensemble of trees approach, namely,...