Distinguishing Types of Drawdowns Is More Important Than Blindly Buying the Dip

marsbitPubblicato 2026-02-15Pubblicato ultima volta 2026-02-15

Introduzione

Distinguishing between types of drawdowns is more important than blindly buying the dip. Academic finance categorizes risk into systemic (market-wide, unavoidable) and idiosyncratic (company-specific). Similarly, drawdowns can be market-driven (systemic, like the 2008 crisis) or company-specific (idiosyncratic, like the recent AI-driven selloff in software stocks). Using FactSet (FDS) as an example, the article illustrates that a systemic drawdown, such as in 2008/09, was a test of the market's durability, not FactSet's economic moat. In these cases, investors can leverage a behavioral advantage—patience and a long-term view—as history shows markets tend to recover. Conversely, the current 2025/26 drawdown in software stocks is largely idiosyncratic, driven by specific fears that AI could disrupt industry pricing power and erode moats. To capitalize on such a drawdown, an investor needs an analytical advantage: a more accurate vision of the company's future a decade from now than the market's pessimistic price implies. This requires understanding why informed sellers are wrong, a fine line between conviction and arrogance. The key takeaway is not to apply a blunt behavioral solution (like simply buying the dip) to a problem that requires nuanced, fundamental analysis.

Original Author: Todd Wenning

Original Compilation: Deep Tide TechFlow

Guide: Academic finance theory categorizes risk into systematic risk and idiosyncratic risk. Similarly, stock drawdowns can also be divided into two types: market-driven systematic drawdowns (such as the 2008 financial crisis) and company-specific idiosyncratic drawdowns (such as the current software stock crash driven by AI concerns).

Using FactSet as an example, Todd Wenning points out: during a systematic drawdown, you can leverage a behavioral advantage (patiently waiting for the market to recover); but during an idiosyncratic drawdown, you need an analytical advantage—having a more accurate vision of what the company will look like in ten years than the market does.

Amid the current AI impact on software stocks, investors must distinguish: is this a temporary market panic, or is the moat truly crumbling?

class="ql-align-justify">Do not use a blunt behavioral solution to solve a problem that requires nuanced analysis.

Full Text Below:

Academic finance theory posits two types of risk: systematic and idiosyncratic.

  • Systematic risk is unavoidable market risk. It cannot be eliminated through diversification, and it is the only type of risk for which you are compensated.
  • On the other hand, idiosyncratic risk is company-specific risk. Because you can cheaply purchase a diversified portfolio of uncorrelated businesses, you are not compensated for taking on this risk.

We can debate Modern Portfolio Theory another day, but the systematic-idiosyncratic framework is helpful for understanding different types of drawdowns (the percentage decline from peak to trough of an investment) and how we, as investors, should evaluate opportunities.

From the moment we picked up our first value investing book, we were taught to take advantage of a despondent Mr. Market when stocks are sold off. If we remain calm while he loses his senses, we will prove ourselves to be stoic value investors.

But not all drawdowns are the same. Some are market-driven (systematic), while others are company-specific (idiosyncratic). Before you make a move, you need to know which type you're looking at.

Generated by Gemini

The recent sell-off in software stocks due to AI concerns illustrates this point. Let's look at the 20-year history of drawdowns between FactSet (FDS, blue) and the S&P 500 (measured via the SPY ETF, orange).

Source: Koyfin, as of February 12, 2026

FactSet's drawdown during the financial crisis was primarily systematic. In 2008/09, the entire market was worried about the durability of the financial system, and FactSet could not be immune to these concerns, especially since it sells products to financial professionals.

At that time, the stock's drawdown had less to do with FactSet's economic moat and more about whether FactSet's moat would matter if the financial system collapsed.

The 2025/26 FactSet drawdown is the opposite case. Here, the concerns are almost entirely focused on FactSet's moat and growth runway, alongside broader fears about accelerating AI capabilities disrupting pricing power in the software industry.

In a systematic drawdown, you can more reasonably make a time arbitrage bet. History shows that markets tend to rebound, and companies with intact moats may emerge even stronger than before, so if you are willing and able to remain patient while others panic, you can leverage a strong stomach to exploit a behavioral advantage.

Photo by Walker Fenton on Unsplash

However, in an idiosyncratic drawdown, the market is telling you that something is wrong with the business itself. Specifically, it suggests increasing uncertainty about the business's terminal value.

Therefore, if you hope to take advantage of an idiosyncratic drawdown, you need to possess an analytical advantage in addition to a behavioral one.

To succeed, you need to have a more accurate vision of what the company will look like in ten years than the current market price implies.

Even if you know a company well, this is not easy to do. Stocks don't typically fall 50% relative to the market for no reason. A lot of once-steadfast holders—even investors you might respect for their deep research—had to capitulate for this to happen.

If you are going to step in as a buyer during an idiosyncratic drawdown, you need to have an answer for why these otherwise informed and thoughtful investors were wrong to sell and why your vision is correct.

There's a fine line between conviction and arrogance.

Whether you are holding a stock in a drawdown or looking to initiate a new position in one, it's important that you understand what type of bet you are making.

Idiosyncratic drawdowns can tempt value investors to start looking for opportunities. Before you take the plunge, make sure you aren't using a blunt behavioral solution to solve a problem that requires nuanced analysis.

Stay patient, stay focused.

Todd

Domande pertinenti

QAccording to the article, what are the two main types of risk in academic financial theory?

ASystematic risk and idiosyncratic risk.

QWhat is the key difference in the type of advantage an investor needs to exploit a systematic drawdown versus an idiosyncratic one?

AAn investor can exploit a systematic drawdown with a behavioral advantage (patience), but to exploit an idiosyncratic drawdown, they need an analytical advantage (a more accurate vision of the company's future).

QUsing the example from the article, what was the primary cause of FactSet's drawdown during the 2008/09 financial crisis?

AFactSet's drawdown during the 2008/09 crisis was primarily systematic, driven by market-wide fears about the durability of the financial system.

QWhat does the article suggest is the market implying about a business during an idiosyncratic drawdown?

ADuring an idiosyncratic drawdown, the market is implying that there is a problem with the business itself and that its terminal value is becoming increasingly uncertain.

QWhat is the 'fine line' the article warns investors about when considering a purchase during an idiosyncratic drawdown?

AThe fine line is between conviction and arrogance. An investor must be able to explain why the sellers were wrong and their own vision is correct, without being overconfident.

Letture associate

Why Hasn't the U.S. Seen the Rise of 'Huabei' or 'Jiebei'?

The article explores why the U.S. lacks large-scale consumer credit products like China's "Huabei" and "Jiebei," despite having a developed financial sector. Key reasons include: 1. **Structural Barriers**: A fragmented federal and state regulatory system, reinforced by post-2008 reforms like the Dodd-Frank Act, raises compliance costs and protects traditional banks, stifling fintech innovation. 2. **Credit Card Dominance**: Credit cards, used by 70-80% of adults, form a $1.28 trillion debt market with high APRs (avg. 22.3%). This system cross-subsidizes users who pay in full with those carrying balances, creating a predatory yet entrenched ecosystem. 3. **Data Privacy Laws**: Strict regulations (e.g., FCRA, CCPA) prevent tech giants from leveraging behavioral data for credit scoring, unlike in China where such data fuels fintech models. 4. **Capital Market Disincentives**: Wall Street penalizes tech firms entering finance due to lower valuations associated with heavy regulation and risk, as seen in Apple’s failure with Apple Card. 5. **Banking Oligopoly**: Major banks control consumer lending, leveraging lobbying power and consumer habits to maintain high-cost credit, while alternatives like payday loans (400% APR) or "unbanked" services remain niche or exploitative. Ultimately, regulatory, structural, and corporate interests collectively block the emergence of accessible, low-cost digital lending in the U.S.

Odaily星球日报35 min fa

Why Hasn't the U.S. Seen the Rise of 'Huabei' or 'Jiebei'?

Odaily星球日报35 min fa

More and More 'Model Supermarkets' Are Opening: ByteDance, Alibaba, and Tencent Compete to Integrate

Chinese tech giants like ByteDance, Alibaba, and Tencent are accelerating the rollout of integrated AI model subscription services—dubbed “model supermarkets”—to provide developers with bundled access to multiple leading domestic large language models (LLMs). ByteDance’s Volcengine recently upgraded its "Coding Plan" by adding newer models like GLM-5.1, Minimax M2.7, and Kimi k2.6, allowing subscribers to use various top models under a single monthly fee starting at ¥40. However, user feedback reveals significant issues, including rapid consumption of usage limits (e.g., hitting caps within hours), frequent server errors (like HTTP 429), and slow response times during peak hours. Complaints about misleading deduction rates—where calls to advanced models consume more quota—are also common. The trend is industry-wide: Alibaba, Tencent, and Baidu have all launched similar multi-model coding plans. While these platforms reduce trial costs for developers, they also expose challenges in balancing affordability with service quality and computational stability. Amid this shift, independent AI companies like Zhipu, MiniMax, and Moonlight Face (Kimi) are developing strategies to avoid becoming mere “pipes” in this ecosystem—focusing on vertical applications, autonomous agents, and long-context models to retain competitiveness. Analysts suggest that, while platform aggregation may pressure model firms in the short term, specialized and vertical AI capabilities will remain differentiated in the long run.

marsbit39 min fa

More and More 'Model Supermarkets' Are Opening: ByteDance, Alibaba, and Tencent Compete to Integrate

marsbit39 min fa

Trading

Spot
Futures
活动图片