Hi Maria, thanks for your feedback. AI is disrupting the current environment at a global level and the impact will be even more relevant as soon as the availability and the access to AI outputs will be spread among all agents. As every innovation, the beginning is all about “test and see” to then start developing solid and empirically tested models. This current pioneering environment has uncertainty as one of the main variables.

Thanks a lot for your feedback. Fully defining how the stock market run is quite challenging but the key focus currently is in identifying theories still valid and theories becoming obsolete due to new structural regimes. And, within the surviving ones, the new successful frameworks are the ones able to split and critic part of the no-longer-empirically-verified theories by reshaping the old frameworks and include the innovation impact. The macro and economical environments experienced a structural shift and this shift made many theories partially no-longer-empirically demonstrated. A common empirical example is the negative rates on the ECB side and the ZIRP concept – the current market balance is an environment never taken into account in past frameworks and successful models are the ones starting from removing the constraint of not accepting negative rates.

After reading your paper, my main question is: to what degree the theoretical consideration of AI in finance is compatible with the real principle of ontological uncertainty in economic models?

Thanks in advance,

Maria