Academic Anti-Science

December 2025 edition of the Portfolio Construction newsletter, explaining how anti-science manifests itself in academic finance and economics.

Dec 02, 2025

ChatGPT cartoon illustration of “Academic Anti-Science.”

This is a longer edition of the Portfolio Construction newsletter, presenting the many ways that I have witnessed anti-scientific tendencies in finance and economics academia. If you are fully aware of these, and just want to skip to the popular posts recap, this is available at the bottom of the post.

The TL;DR version is that the current academic system seems to foster a nepotistic environment in which the most anti-scientific thrive, while the meritocratic and scientific researchers find a job outside of academia. So, although the number of articles is increasing, the scientific progress is severely lacking.

This post contains the many subtle ways in which old dogma is reinforced by people who benefit from this system and some advice for researchers who still wish to pursue an honest career in academia. So, I do not claim that all academics are nepotistic and anti-scientific, just that the current system benefits those who are.

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Academic anti-science

That academic research currently suffers from many anti-scientific tendencies seems to be a well-known fact for anyone who is involved with the industry.

I find that some fields are less affected than others. For example, mathematicians seems to be generally better at remaining scientific than many other fields.

This might be caused by the nature of mathematics, which is essentially strict logic. Hence, it becomes harder to present poorly supported conclusions when most of the readers are proactively looking for logical inconsistencies.

However, we can unfortunately not assume that all mathematicians are scientific, as there are several examples of anti-scientific behavior when they transition to finance and economics.

In fact, sometimes the mathematical formality is used to obfuscate the logical inconsistencies, because readers with finance and economics backgrounds are not able to assess the mathematical validity of all the assumptions being made in a careless way.

The societal consequence

In most parts of the world, academia receives significant public funding. Hence, people working in academia have an obligation to work for the public good. This is at least the implicit assumption.

In practice, many academic fields seem to operate in an extractive way, evident first and foremost by the publishers who enjoy significant margins on the articles produced, submitted, and reviewed freely by academics.

However, individual academics can also behave in this extractive way by misusing their power as heads of institutions or article reviewers to increase their personal income in various ways.

If you have honest dialogues with PhD students and postdocs, you will quickly realize that such cases seem to appear almost everywhere. However, funding such behavior with public money should clearly not be accepted.

Sabine Hossenfelder, who I strongly encourage you to follow, gives some insights into this kind of behavior in the video below. In general, she shares many insights in relation to how anti-science manifests itself in physics, which by many is still perceived as a very scientific field but seems to suffer from the same problems.

Finance and economics

Since I have a traditional quantitative finance and economics background, I have a deep insight into how anti-science manifests itself in the field, because I had to carefully study this content for exams, and because I personally witnessed the shortcomings of the mainstream methods in my work as an institutional investment manager.

I carefully present the issues with the methods and suggest improvements in the Portfolio Construction and Risk Management book, so I will mostly refrain from reiterating that methods like CAPM, Black-Litterman, and mean-variance probably do more harm than good in practice.

The old methods are fundamentally flawed and developed for decades-old technology. Modern technology allows us to eliminate their fundamental problems and spend more time on the truly value-adding parts of investment management, see the Portfolio Construction and Risk Management book.

You can also find a collection of LinkedIn posts, where I point out issues with various methods and theories here. I also encourage you to check out Jostein Hauge’s latest LinkedIn posts about scientific issues related to finance and economics in particular.

Below, I will present the many ways that I have witnessed old dogma about the investment methods that I criticize being reinforced.

The publishing gatekeeping

As many academics have published articles and books about topics such as mean-variance and the related variance/covariance estimation, they obviously are not too keen on completely discarding this work and the reputational benefits they enjoy from it.

However, from a strictly scientific perspective, these theories and methods are easy to reject. As a reminder of the scientific method, brilliantly explained by Richard Feynman, I encourage you to watch this video.

So, the first obvious way in which academics prevent progress is by simply rejecting articles that are fundamentally critical of the old methods and accepting articles that excuse the shortcomings of the old methods.

There are many such examples about how mean-variance is supposedly not that bad, and even books written about how to duct tape its shortcomings. Common for all this work is that it contains quite obvious logical inconsistencies and is easily rejected when tested against real-world data, see for example The Normal Distribution Myth article.

In a more sinister way, reviewers from these academic journals often require citation of their own or related work. It is not always as explicit as in the real example below, but many people looking to publish in these journals implicitly understand that this increases the probability of getting their articles accepted.

The above example also illustrate how rigorous the peer reviewing process actually is. Hence, the peer review process currently seems to be used to reinforce old dogmas rather than assessing the scientific quality of the work.

For the above reasons, I have no desire to publish my own articles in any academic journals. Although I have been contacted by some “practitioner-oriented” journals, I have still found them to be affected by the same issues as the academic ones.

Given the above insights, I encourage you to proactively share my work with people whom you think it is relevant for, because you can be certain that it is proactively gatekept by the academics. We have some important new results that we are considering sharing, but we have to assess the trade-off between keeping it proprietary and the additional exposure we get from sharing it publicly.

The industry reinforcement

There are many examples of asset managers communicating to their clients how they use “Nobel Prize winning CAPM and Markowitz optimization”.

This might sound cool to unknowing clients, because they are not told that these methods have been developed for the technology of the 1950-1960’s.

Hence, the academic theories are mostly used as asset management marketing. Many of the finance and economics academics employed at investment firms indeed serve a marketing function, publishing “thought leader” white papers with slight adjustments to old methods.

A more subtle example is a fund manager who has received an allocation from a larger firm that has a finance and economics academic employed, who might influence the decision. For example, I have talked with people who have privately revealed that they of course do not believe in mean-variance but avoid explicitly voicing this, because they don’t want to risk this current allocation.

Whether the academic person would have influence on the allocation decision or not remains unknown, but business is clearly prioritized over science in this case, similar to academic publishing.

Another more subtle example of people staying silent or moderating their critique, despite knowing better, is when they are somehow dependent on academic institutions. For example, if they host conferences/bootcamps at university campuses.

I find it interesting and revealing that I almost never discuss the insufficiency of CAPM and mean-variance with people who actually have a good risk-adjusted return track record. It’s mainly academics and people who use it for marketing that object.

For some more perspectives on how the old methods are promoted and reinforced in business, see the Note below:

Advice for young academic researchers

As a finance and economics PhD student or postdoc, I am sure that you have experienced the above issues even more intensely than I have.

I frequently get told about the suppression of honest researchers who try to do good scientific work. I understand that you suffer more than I do, and I hope that conditions for you can improve by me raising awareness.

My advice for you is to either rebel or leave for more meaningful work in industry instead of wasting your energy and talent reinforcing the hopeless academic system that has no long-term future.

Conclusion

The academic research system is currently utterly dysfunctional. I don’t know if it has always been like this, or if it is simply becoming more evident with an increasing access to information.

It does not have to be this way. The industry should be structured in a way that rewards real-world impact rather than number of articles and citations, which naturally discourages real scientific progress and promotes reiteration of old dogmas.

Academia has a halo effect that unfortunately is being exploited by the most morally scrupulous, think about recent revelations related to the Epstein files and a prominent finance and economics academic.

There are of course honest academic researchers, and they suffer a lot in the current system. Let’s focus on helping these voices being heard. They can be a driving force for improving the system, which is necessary because the current deadweight loss of academic research is very high.