Many of us regret that we pursued a PhD in finance/economics. As a young lad, I had great career prospects with multiple bank internships etc., but I wanted to do the PhD for intellectual fulfillment, it was big mistake. PhDs in finance and econ are not well regarded in industry today, and you will have to learn whatever skills employers are asking for. It was a lot easier for me to find a good job as undergrad than it is now as a PhD in finance. Employers view ~6 years doing a PhD in finance/econ as a waste of time, and they prefer to hire younger folks with better coding and data analytics skills, since the econ/finance PhD training is regarded as insufficient. With the regression monkey stuff dominating nowadays, who can blame them?
In other words, all your academic research is considered worthless by prospective employers and you will have to sit on Datacamp every night to learn Python to become a "data scientist" so you can compete with undergrads/master students - what a waste of time to do a PhD. The finance and econ programs seriously need to reinvent themselves, and finance/econ academia as a whole must embrace technological advancements and techniques. Also, MBAs are increasingly struggling to get jobs, as employers are starting to recognize that quant skills are needed everywhere in business. As an example, think of the task of analyzing the economics of an advertisement campaign nowadays vs. 10 years ago. This would be right within the econ/finance/business school domain ten years ago. However,
today, folks with proper knowledge of statistics and great coding skills are preferred over MBAs (and econ/finance PhDs) that have basic knowledge of analyzing large datasets and little to no skills coding, and this trend will continue. Think of this from an employer's perspective, who would be must effective in assessing the effectiveness of an advertisement campaign: An MBA with no coding skills who have learned "marketing diagram models", or a statistics/CS bachelors with a few years experience that has documented experience with coding and analyzing data? Similar ideas can be applied to essentially any problem that, e.g., a financial company may interested in. Who would be most effective in assessing, for example, default risk for a portfolio: an MBA that has learned some basic "tractable" financial models based on academic research or someone who can actually analyze the data effectively using statistical and machine learning modeling techniques?
So, you are not alone, we are all struggling today's environment and for the deficiencies in finance and econ programs.