Fundamentals of Quantitative Investments
In this series, the Omphalos AI Research Team want to discuss the key and fundamental aspects of quantitative investing in detail and depth. In particular, our series will not be a beautiful story of how to build the holy grail of investing, but rather a long list of pitfalls that can be encountered when building such systems. It will not be a complete and exhaustive list of pitfalls, but we will try to describe those we discuss in great depth so that their significance is clear to everyone. And importantly, this will not be a purely theoretical discussion. We will provide a practical view on all of these aspects — shaped by real-world lessons and, in many cases, by our own painful and sometimes even traumatic experiences in building and testing systematic investment strategies. These hard-earned lessons are precisely why Omphalos Fund has been designed as a resilient, modular, and diversified platform — built to avoid the traps that have undone so many before.
At Omphalos Fund, we have always been clear about one thing: artificial intelligence is not magic. It is a powerful tool, but its value depends entirely on the system it operates in and the rules that guide it. When applied to asset management, this means that even the most advanced AI can only be effective if it is built on a deep understanding of how markets work — with all their complexities, inefficiencies, and risks.
That is why our latest Behind the Cloud white paper takes a step back from the technology itself. Instead, it examines the foundations of quantitative investing — the real-world mechanics, pitfalls, and paradoxes that shape investment strategies. The aim is not to present a flawless “holy grail” of investing, but to show the challenges and traps that every systematic investor must navigate.
We believe this is essential for anyone working with AI in finance. Without appreciating the underlying business of investing, AI models risk becoming black boxes that look impressive in theory but fail in practice. By shedding light on the subtle but critical issues in quantitative investment design — from overfitting to diversification, from the illusion of normal distributions to the reality of risk of ruin — we provide context for why our platform is built the way it is: modular, transparent, and resilient.
The goal of this white paper is simple:
To help readers understand that using AI in asset management is not only about smarter algorithms — it’s about building systems that are grounded in strong investment fundamentals and designed to survive the real world of markets.
The Black-Scholes option pricing model is one of the most celebrated achievements in financial history. Introduced in 1973 and awarded a Nobel Prize in 1997, it revolutionized the pricing of options and shaped entire financial markets. It brought clarity, elegance, and mathematical rigor to a previously chaotic corner of trading.
But beneath its success lies a fatal flaw: the assumption that financial returns follow a normal distribution. This idea, while convenient for modeling, dramatically underestimates the probability of extreme events, so-called “fat tails.” In theory, such events are rare. In reality, they occur far more frequently than Gaussian models suggest.
Nowhere was this disconnect more devastating than in the collapse of Long-Term Capital Management (LTCM), a hedge fund built on the very foundations of Black-Scholes. LTCM wasn’t just another fund. It was a financial supernova, drawing on the reputations of Nobel laureates like Robert Merton and Myron Scholes, co-architect of the Black-Scholes model. To this academic firepower was added street credibility: John Meriwether, the legendary bond trader from Salomon Brothers and a central figure in Liar’s Poker, served as founder and CEO. The firm combined theoretical elegance with trading muscle - and for a while, it worked brilliantly.
Then came 1998. When Russia defaulted on its debt, global markets convulsed. Spreads exploded, correlations broke down, and volatility spiked. LTCM’s positions, assumed to be low-risk, were hit from all sides. Within weeks, the fund had lost $4.6 billion. The models that once promised precision turned dangerously blind. A Federal Reserve-led bailout prevented systemic collapse, but the damage was done.
A Case in Point: The Fall of LTCM
LTCM is often remembered as a failure of risk management, but it was more than that - it was a failure of model overconfidence. Despite boasting some of the brightest minds in finance and reporting incredibly low Value-at-Risk (VaR), the fund ignored one critical truth: real markets do not follow normal distributions. When volatility hit and correlations converged, the models proved useless. The trades weren’t just wrong, but they were wrong by orders of magnitude. The very assumptions that powered LTCM’s rise became its undoing.
The lesson is simple: if your strategy depends on the market behaving “normally,” it’s only a matter of time before reality breaks your model.
Research Spotlight
Lessons for Today’s Quants
The brilliance of Black-Scholes was not in its assumptions, but in the innovation it sparked. Yet its limitations must be clearly understood. The assumption of normally distributed returns may work under calm conditions but markets are anything but calm. Volatility clusters, correlations spike, and outliers dominate outcomes. The math doesn’t break; it simply becomes irrelevant when the premises collapse.
This is precisely the warning articulated by Nassim Nicholas Taleb in The Black Swan and his broader work on fat‑tailed systems. Taleb’s core insight is uncomfortable but essential: extreme events drive long‑term outcomes, yet traditional models systematically dismiss them as statistical impossibilities. In finance, what we fail to model is often what matters most.
For modern quants, the lesson is clear. It is not enough to improve calibration or add layers of sophistication to fragile assumptions. Systems must be built with an explicit acceptance that markets are asymmetric, discontinuous, and prone to shocks that cannot be inferred from historical averages alone.
Robustness, not elegance, is the defining challenge of quantitative investing.
Omphalos Perspective
At Omphalos, we believe that robustness beats elegance when it comes to survival. Our trading agents are not optimized around Gaussian assumptions. They are trained and tested on real-world distributions, including heavy tails, skew, and regime shifts.
We explicitly reject the notion that markets behave “normally.” Instead, we work with advanced probabilistic frameworks, such as the t-distribution, to reflect the complexity and asymmetry of actual market behavior. Mateusz’s ongoing work in this area provides the mathematical backbone for a more honest approach to volatility, risk, and tail exposure.
Our overarching goal is to minimize volatility. In a world where returns are unpredictable and distributions unstable, keeping volatility low is one of the most effective ways to protect capital and compound consistently. It reduces exposure to ruin, stabilizes long-term outcomes, and provides our investors with a smoother ride - without relying on fragile assumptions about market behavior.
By designing around reality and not theoretical convenience we give our strategies the one advantage LTCM never had: humility in the face of uncertainty.
👉 In Chapter 15, the last one of this series, we will scrutinize the “Human Trader: The Weakest Point – Why Automate Your Trading Systems”
Stay tuned for Behind The Cloud, where we’ll continue to explore the frontiers of AI in finance and investing.
If you missed our former editions of "Behind The Cloud", please check out our BLOG.
Omphalos Fund won the "Funds Europe Awards 2025" in the category "European Thought Leader of the Year".
Omphalos Fund nominated for "EuroHedge Awards 2025"
© The Omphalos AI Research Team - January 2026
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