Chaotic stock market scene symbolizing unpredictable financial markets.

Unraveling the Mysteries of the Market: Can We Ever Truly Predict Financial Chaos?

Jordan Keane in Business & Economy December 2025 • 4 min read.

"Explore the limitations of causal reductionism in finance and discover why predicting market behavior may be more complex than we think."

The quest to understand and predict financial markets has led many to embrace causal inference, seeking to identify the root causes of market movements. The idea is appealing: if we can pinpoint the factors that drive the market, we can anticipate future trends and make informed investment decisions. However, financial markets are complex, self-referencing systems. This raises a fundamental question: can we truly apply the principles of causal reductionism to these dynamic environments?

Causality has been a subject of debate since the time of Hume (1739), and existing coverage on causality in the philosophy literature is comprehensive. Rigor and a prescription of causal relevance has also pervaded finance and economics disciplines; including econometrics and investment management. Concurrently, Quantitative Finance (QF), which generically includes the investment and financial economics disciplines, is embarking on a purge of 'incorrect' statistical methodologies. Such flawed statistical approaches have arguably resulted in a proliferation of false claims and charlatanism.

This article explores the limitations of applying scientific deduction and causal inference to financial markets. We'll examine the concept of reflexivity – the market's tendency to react to and incorporate predictions – and its implications for our ability to establish clear causal relationships. We'll also introduce a toy model to illustrate how competing causal chains can create unpredictable outcomes.

Epistemic Norms and Causal Chains in the Market

Chaotic stock market scene symbolizing unpredictable financial markets.

To appreciate the limitations of our methods, we first need to understand the epistemic norms that underpin most economic thinking. These norms assume that markets adhere to certain principles, including the existence of unique, well-defined causal chains. It is through these chains that we extract data to make sense of economics.

However, economic schools of thought differ. The dichotomy between neoclassical, or orthodox, and heterodox economics (arguably, everything else) is frequently made to smooth over and sometimes smother the pragmatic importance of these assumptions as a binary framing that casts idealisms and normative approaches in opposition to positivist perspectives. To get a better grasp of things, we can consider some important assumptions:

Rationality: Orthodox economics assumes that market participants act rationally, making decisions based on logical reasoning and self-interest.
Efficiency: Markets are assumed to be efficient, meaning that prices reflect all available information.
Equilibrium: The market tends toward a state of equilibrium, where supply and demand are balanced.

Heterodox economics challenges these assumptions, highlighting the role of irrational behavior, market inefficiencies, and imbalances. Regardless, modern orthodox and heterodox economic approaches aim to incorporate a range of imperfections within their standard frameworks to render them more realistic. Both approaches still seek to establish narrative explanations that are cohesive and make successful predictions, but the question remains: How reliable are these narratives when markets are inherently self-referential?

The Future of Financial Forecasting: Embracing Uncertainty

The limitations of causal reductionism do not mean that predicting financial markets is impossible. However, it does suggest that we need to approach the task with humility and a healthy dose of skepticism. By acknowledging the role of reflexivity and the inherent uncertainty of market behavior, we can develop more robust and realistic models. We can better prepare ourselves for the inevitable surprises that the market will throw our way. The key is to embrace uncertainty and adapt our strategies accordingly.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2311.1657,

Title: Epistemic Limits Of Empirical Finance: Causal Reductionism And Self-Reference

Subject: q-fin.gn econ.gn nlin.ao q-fin.ec

Authors: Daniel Polakow, Tim Gebbie, Emlyn Flint

Published: 28-11-2023

Everything You Need To Know

Why is it so difficult to predict financial markets using causal inference?

Financial markets are complex, self-referencing systems, making it difficult to apply causal reductionism effectively. The concept of reflexivity, where the market reacts to and incorporates predictions, further complicates the establishment of clear causal relationships. This inherent self-referential nature challenges traditional scientific methods that rely on identifying unique, well-defined causal chains.

What is 'reflexivity' and how does it impact the ability to forecast financial events?

Reflexivity refers to the market's tendency to react to and incorporate predictions. This means that when a prediction is made, the market's behavior changes in response to that prediction, potentially invalidating the original forecast. This feedback loop complicates the task of establishing clear causal relationships and makes predicting economic events more challenging.

What are the fundamental differences between orthodox and heterodox economics and how do these impact financial forecasting?

Orthodox economics, often referred to as neoclassical economics, assumes that market participants act rationally, markets are efficient, and the market tends toward equilibrium. Heterodox economics challenges these assumptions, highlighting the role of irrational behavior, market inefficiencies, and imbalances. These differing assumptions influence how each school of thought approaches financial forecasting, with orthodox economics seeking cohesive, predictive narratives within a rational framework, while heterodox economics considers a wider range of factors that can lead to unpredictable market behavior. However both approaches aim to incorporate a range of imperfections within their standard frameworks to render them more realistic, but both still seek to establish narrative explanations that are cohesive and make successful predictions.

How do epistemic norms, like rationality and efficiency, affect economic thinking and our ability to predict financial markets?

Epistemic norms such as rationality, efficiency, and equilibrium underpin much of economic thinking. The assumption of rationality implies that market participants make logical decisions based on self-interest, while efficiency suggests that prices reflect all available information. However, if these norms don't accurately reflect real-world market behavior, models based on them may fail to predict market movements accurately. The challenge lies in balancing the need for simplifying assumptions with the complexity and irrationality often present in financial markets. When these norms do not hold, the causal chains that underpin our economic models can break down.

Given the challenges in predicting financial markets, what strategies can be adopted to navigate market uncertainty?

Instead of relying solely on causal reductionism, a more pragmatic approach involves acknowledging the inherent uncertainty of market behavior and the role of reflexivity. Developing robust models that account for unpredictable outcomes, adapting strategies as new information emerges, and embracing uncertainty are key. This involves a shift from seeking definitive predictions to preparing for a range of possible scenarios and managing risk accordingly. Focus on understanding the limitations of predictive models and being prepared for surprises.