GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice

We assess the ability of GPT -- a large language model -- to serve as a financial robo-advisor for the masses, by using a financial literacy test. Davinci and ChatGPT based on GPT-3.5 score 66% and 65% on the financial literacy test, respectively, compared to a baseline of 33%. However, ChatGPT based on GPT-4 achieves a near-perfect 99% score, pointing to financial literacy becoming an emergent ability of state-of-the-art models. We use the Judge-Advisor System and a savings dilemma to illustrate how researchers might assess advice-utilization from large language models. We also present a number of directions for future research.Comment: 43 pages, 2 figures and 2 tables in main tex

Heterogeniczność preferencji czasowych a skłonność do oszczędzania, zadłużania się oraz inwestowania: analiza zależności na poziomie krajowym

The purpose of the study is to test whether the existence of differences in mean time preferences between countries is a significant determinant of the level of savings, indebtedness, and the size of capital markets across countries. Results show only limited support for the link between time preference and the propensity to save. The paper contains a discussion on the possible reasons for the general lack of support for the hypothesized effects of cross-country time preference heterogeneity.Celem artykułu jest przedstawienie badania mającego wskazać, czy istnienie różnicy w przeciętnym poziomie preferencji czasowej między krajami stanowi istotną determinantę poziomu oszczędności, zadłużenia i stopnia rozwoju rynków kapitałowych w poszczególnych krajach. Wyniki analizy wskazały wyłącznie na ograniczony związek między preferencją czasową a skłonnością do oszczędzania. W artykule zawarto dyskusję możliwych powodów braku wsparcia dla postulowanych efektów międzykrajowej heterogeniczności w preferencji czasowej

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Treatment choice in the presence of conflicting information: The role of physician likeability in the choice of non-proven therapies against conventional treatment

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Dafina Petrova was supported by a Sara Borrell fellowship from the Health Institute Carlos III (Expde: CD19/00203) and a Juan de la Cierva Fellowship from the Ministry of Science (JC2019-039691-I). We thank Jesus Henares Montiel for the review and feedback on the medical scenarios, and Daniel Kaszas for feedback on the manuscript. Funding for the open access charge was provided by Universidad de Granada / CBUA.Research on why patients sometimes choose non-proven therapies (NPT) instead of conventional treatments is limited. We investigated how physician likeability influences the choice ofNPT instead of conventional treatment. In an experiment with three medical scenarios, participants (N = 384) consulted two physicians who gave conflicting recommendations: The first physician recommended a conventional treatment and the second one recommended a NPT. We manipulated the likeability of the first physician, who was either likeable or unlikeable. Using mediation analyses, we explored how the effect of likeability was channelled and whether time pressure influenced treatment choice. Participants chose the NPT more often (OR = 1.43, 95% CI [1.03–2.00]), had more positive affective responses, and perceived more benefit from NPT when the conventional treatment was recommended by an unlikeable (vs. likeable) physician. Time pressure had no effect on treatment choice. Physicians’ likeability might play an important role in treatment choice in the presence of conflicting information. Providers should be cognizant that poor communication might push patients to prefer the advice of more likeable physicians, even when they prescribe NPT instead of conventional treatment.Instituto de Salud Carlos III Expde: CD19/00203Juan de la Cierva Fellowship from the Ministry of Science JC2019-039691-IUniversidad de Granada / CBU

Social Desirability Bias and Earnings Management around the World

In this paper we test whether inter-country variation in individuals’ tendency to conform, as measured by the Lie (social desirability) scale used in the Eysenck Personality Questionnaire, can explain differences in the propensity to employ corporate earnings management around the world. Such a link is feasible, given that survey data suggest executives tend to be under severe pressure to meet earnings benchmarks, to which they often succumb by engaging in earnings management (to the detriment of the company’s long-term prospects). We hypothesize that in countries where the propensity to act in a socially desirable (outsider-satisfying) way is stronger, earnings management should be more prevalent. Research results support our hypothesis, and demonstrate the existence of a positive relationship between the prevalence of earnings management in a country and the mean score of individuals from that country on the Eysenck Lie scale, which further evidences that capital market pressure is a significant determinant of earnings management

Judgements of research co-created by Generative AI: experimental evidence

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using Generative AI (large language models; LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether - after deciding to delegate the research process - they would trust the scientist (that decided to delegate) to oversee future projects. Thirdly, they rated the expected accuracy and quality of the output from the delegated research process. Our results show that people judged delegating to an LLM as less morally acceptable than delegating to a human (d = -0.78). Delegation to an LLM also decreased trust to oversee future research projects (d = -0.80), and people thought the results would be less accurate and of lower quality (d = -0.85). We discuss how this devaluation might transfer into the underreporting of Generative AI use. </p

Robo-investment aversion

In five experiments (N = 3,828), we investigate whether people prefer investment decisions to be made by human investment managers rather than by algorithms (“robos”). In all of the studies we investigate morally controversial companies, as it is plausible that a preference for humans as investment managers becomes exacerbated in areas where machines are less competent, such as morality. In Study 1, participants rated the permissibility of an algorithm to autonomously exclude morally controversial stocks from investment portfolios as lower than if a human fund manager did the same; this finding was not different if participants were informed that such exclusions might be financially disadvantageous for them. In Study 2, we show that this robo-investment aversion manifests itself both when considering investment in controversial and non-controversial industries. In Study 3, our findings show that robo-investment aversion is also present when algorithms are given the autonomy to increase investment in controversial stocks. In Studies 4 and 5, we investigate choices between actual humans and an algorithm. In Study 4 –which was incentivized–participants show no robo-investment aversion, but are significantly less likely to choose machines as investment managers for controversial stocks. In contrast, in Study 5 robo-investment aversion is present, but it is not different across controversial and non-controversial stocks. Overall, our findings show a considerable mean effect size for robo-investment aversion (d = –0.39 [–0.45, –0.32]). This suggests that algorithm aversion extends to the financial realm, supporting the existence of a barrier for the adoption of innovative financial technologies (FinTech).ISSN:1932-620

Impact of the Application of Fuel and Water Emulsion on CO and NOx Emission and Fuel Consumption in a Miniature Gas Turbine

Miniature gas turbines (MGT) are an important part of the production of electric energy in distributed systems. Due to the growing requirements for lower emissions and the increasing prices of hydrocarbon fuels, it is becoming more and more important to enhance the efficiency and improve the quality of the combustion process in gas turbines. One way to reduce NOx emissions is to add water to the fuel in the form of a water-based emulsion (FWE). This article presents the research results and the analysis of the impact of the use of FWE on CO and NOx emissions as well as on fuel consumption in MGT GTM-120. Experimental tests and numerical calculations were carried out using standard fuel (DF) and FWE with water content from 3% to 12%. It was found that the use of FWE leads to a reduction in NOx and CO emissions and reduction in the consumption of basic fuel. The maximum reduction in emissions by 12.32% and 35.16% for CO and NOx, respectively, and a reduction in fuel consumption by 5.46% at the computational operating point of the gas turbine were recorded