We study whether large language models (LLMs) can generate suitable financial advice and which LLM features are associated with higher-quality advice. To this end, we elicit portfolio recommendations from 32 LLMs for 64 investor profiles, which differ in their risk preferences, home country,...

We show that the recently released text-based artificial intelligence tool GPT-4 can provide suitable financial advice. The tool suggests specific investment portfolios that reflect an investor's individual circumstances such as risk tolerance, risk capacity, and sustainability preference....

We show that the recently released text-based artificial intelligence tool GPT-4 can provide suitable financial advice. The tool suggests specific investment portfolios that reflect an investor’s individual circumstances such as risk tolerance, risk capacity, and sustainability preference....

We show that the recently released text-based artificial intelligence tool GPT-4 can provide suitable financial advice. The tool suggests specific investment portfolios that reflect an investor's individual circumstances such as risk tolerance, risk capacity, and sustainability preference....

We examine the technological and socioenvironmental factors influencing the adoption of humanoid service robots in Austrian, German, and Swiss banks. We integrate the technology acceptance model and the technology–organization–environment framework, and employ structural equation modeling to...

We examine the technological and socioenvironmental factors influencing the adoption of humanoid service robots in Austrian, German, and Swiss banks. We integrate the technology acceptance model and the technology–organization–environment framework, and employ structural equation modeling to...

In this article, we shed more light on the covariances versus characteristics debate by investigating the explanatory power of the instrumented principal component analysis (IPCA), recently proposed by Kelly et al. (2019). They conclude that characteristics are covariances because there is no...

In this article, we shed more light on the covariances versus characteristics debate by investigating the explanatory power of the instrumented principal component analysis (IPCA), recently proposed by Kelly et al. (2019). They conclude that characteristics are covariances because there is no...

In this article, we shed more light on the covariances versus characteristics debate by investigating the explanatory power of the instrumented principal component analysis (IPCA), recently proposed by Kelly et al. (2019). They conclude that characteristics are covariances because there is no...

We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees...