Why Rigor Beats Noise: Investing Like a Scientist

Financial markets produce an enormous volume of information every day. Quarterly reports, analyst opinions, news tickers, social media commentary, price charts — the information flow is relentless. Most investors believe their problem is a lack of information. In reality, their problem is the opposite: too much noise and too little rigor.

The distinction between signal and noise is the central problem of investing. And the solution lies not in more data, but in better methods for separating the relevant data from the irrelevant.

Signal Versus Noise

In signal processing, "noise" refers to those parts of a signal that carry no information. The daily price fluctuation of a stock is mostly noise. The long-term trajectory of a company's earnings is signal. The difference is fundamental, yet routinely ignored.

Nassim Nicholas Taleb described this distinction vividly in "Fooled by Randomness." Anyone who checks their portfolio daily sees predominantly noise and confuses it with information. Checking quarterly reveals somewhat more signal. Checking annually reveals mostly signal.

The consequence is counterintuitive: less information can lead to better decisions if it reduces noise. This is not an argument against data — it is an argument for the right data.

The Scientific Method in Investing

Scientific work follows a clear pattern: formulate a hypothesis, collect data, test the hypothesis, draw conclusions. Most investors invert this process: they collect data (news, tips), then search for a hypothesis that fits the data, and act impulsively.

In my work on financial data systems, I have observed this pattern thousands of times. Building Eulerpool was fundamentally the attempt to give investors the tools that scientists take for granted: structured data, historical comparisons, standardized metrics.

A scientific investor first formulates a thesis — for instance: "This company has a durable competitive advantage that manifests in above-average returns on capital." Then they systematically search for data that confirms or refutes this thesis. They actively seek counterevidence. And they revise their view when the data speaks against it.

The Most Common Cognitive Biases

Behavioral economics has cataloged how systematically people deviate from rational decisions. For investors, several biases are particularly relevant:

• Confirmation bias: We preferentially seek information that confirms our existing views and ignore contradictory evidence.
• Availability heuristic: We overestimate the probability of events that come easily to mind — such as dramatic market crashes.
• Anchoring: A price once seen influences our valuation even when it is irrelevant. Someone who bought a stock at 100 euros considers 80 euros "cheap," regardless of intrinsic value.
• Herding: We orient ourselves by what others do, especially in uncertain situations.

Each of these biases adds noise and reduces the precision of decision-making. Scientific methodology is the deliberate attempt to neutralize these biases.

Quantitative Rigor in Practice

Rigor in investing means concretely: working with numbers, not narratives. A narrative like "The cloud is growing" is not an investment thesis. A statement like "This company has grown its free cash flow at a 15% CAGR for 10 years and trades at a 5% FCF yield" is an investment thesis.

The tools we developed at AlleAktien are designed precisely for this purpose: to prepare fundamental data so that investors can distinguish narrative from substance. Every stock analysis begins with the numbers — revenue, earnings, cash flow, debt, return on capital — and only then comes the qualitative assessment.

This does not mean qualitative factors are unimportant. Management quality, corporate culture, and competitive position are decisive. But these factors should be reflected in the numbers — and if they are not, skepticism is warranted.

The Role of the Base Rate

One of the most powerful techniques from statistics is the consideration of the base rate. Before asking "How good is this company?", one should ask "How good are companies of this category on average?"

Most companies are mediocre. Most new products fail. Most acquisitions destroy value. These are the base rates. An investor who ignores them and focuses only on the individual case will be systematically too optimistic.

Daniel Kahneman called this error the "neglect of the base rate" and showed that it is widespread even among experts. The solution: every assessment begins with the base rate and is then adjusted by specific evidence.

How to Systematically Reduce Noise

Practically, noise can be reduced through several straightforward habits:

1. Reduce frequency: Check portfolios weekly or monthly, not daily. Information quality improves disproportionately.
2. Curate sources: A few high-quality sources are better than many mediocre ones. An annual report contains more signal than a hundred news articles.
3. Pre-commit to decision rules: Defining buy and sell criteria before acting reduces emotional reactions.
4. Pre-mortem analysis: Before every investment, ask: "What would need to happen for this investment to fail?" This forces an honest risk assessment.
5. Document outcomes: An investment journal that records decision rationale makes it possible to learn from mistakes — rather than rationalizing them after the fact.

FAQ

Does "reducing noise" mean reading less? Not necessarily less, but more selectively. It is about choosing sources with the highest signal-to-noise ratio. An annual report, a financial statement, or a well-researched analysis is more valuable than a hundred headlines. The question is not "How much do I read?" but "How much of what I read is signal?"

How does one distinguish signal from noise in practice? A useful heuristic: if a piece of information will still be relevant in a year, it is likely signal. If it will be forgotten in a week, it is likely noise. Quarterly earnings, competitive positions, and industry trends are signal. Daily headlines, analyst price targets, and short-term market sentiment are predominantly noise.

Is quantitative investing better than qualitative? The distinction is falsely drawn. Good investing is both — quantitative rigor in data analysis and qualitative judgment in interpretation. Numbers alone are not enough; but without numbers, every judgment is speculation. The best investors seamlessly combine both.