Thank you for sharing!

👉 The working paper and its online appendix are available now on SSRN: papers.ssrn.com/sol3/papers....

We believe our findings are important for academics, editors, publishers, and research funding bodies, but also for the wider society in terms of accessibility to the ivory tower of science.

Lastly, we present evidence suggesting that a higher reliance on ChatGPT makes authors 𝗺𝗼𝗿𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗲 (more publications per year), but 𝗱𝗼𝗲𝘀 not 𝗵𝗲𝗹𝗽 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 publication 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗻𝗼𝗿 scholarly 𝗶𝗺𝗽𝗮𝗰𝘁.

Conversely, author teams from countries that place less emphasis on creativity, display lower skepticism toward technology, and have higher levels of moral ambiguity show a more pronounced effect.

𝗪𝗵𝗶𝗰𝗵 𝗳𝗮𝗰𝘁𝗼𝗿𝘀 𝗮𝗿𝗲 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗱𝗲𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗿𝗲𝗮𝗱𝗮𝗯𝗶𝗹𝗶𝘁𝘆?

We find that author teams from primarily English-speaking countries do not show this drastic effect.

We then move on and document a decrease in the readability of finance articles since the advent of ChatGPT. Papers require 𝗼𝗻𝗲 𝗳𝘂𝗹𝗹 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘆𝗲𝗮𝗿 of education to read comfortably. The decrease of readability coincides with an increase of AI-associated words.

First, we run a set of simulations. We let LLMs generate introductions based on actual abstracts and results sections and then compare the generated introduction with the original one. On average, none of the LLMs is able to create a more readable version of the introduction.

Among other topics, future research should address 𝙬𝙝𝙮 𝙁𝙍𝘼𝙨 𝙙𝙤 𝙣𝙤𝙩 𝙖𝙥𝙥𝙚𝙖𝙡 𝙩𝙤 𝙡𝙚𝙨𝙨 𝙛𝙞𝙣𝙖𝙣𝙘𝙞𝙖𝙡𝙡𝙮 𝙡𝙞𝙩𝙚𝙧𝙖𝙩𝙚 𝙥𝙚𝙤𝙥𝙡𝙚, who likely would benefit more than others from using FRAs and 𝙩𝙝𝙚 𝙞𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣 𝙤𝙛 𝙇𝙖𝙧𝙜𝙚 𝙇𝙖𝙣𝙜𝙪𝙖𝙜𝙚 𝙈𝙤𝙙𝙚𝙡𝙨 𝙖𝙣𝙙 𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙫𝙚 𝘼𝙄 to enhance user interaction.

We review on the nascent research on FRAs. We identify two streams of literature:
(1) 𝗮𝘀𝘀𝗲𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, which focuses on designing FRAs and improving the functioning of these machine advisors, and
(2) 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿𝗮𝗹 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, which investigates technology adoption and issues related to biased advice.

Financial Robo-Advisors (FRAs) enable households to participate in financial markets w/ a limited amount of money and w/o time or place constraints. FRAs can help investors overcome behavioural biases, but have disadvantages, like relying on a limited number of inputs and lacking individualization.

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