Drivers of Stock Mispricings

Financial markets love to pretend they’re efficient—but reality tells a different story. Stocks often trade above or below their intrinsic value, and these anomalies stick around far longer than they should. The persistence of these inefficiencies isn’t a bug; it’s a feature of how markets behave when psychology, incentives, and structural constraints collide.

That’s why thousands of analysts—on both the buy side and sell side—wake up every day looking for mispricings. It’s not just about finding “cheap” or “expensive” stocks. It’s about understanding why the price is wrong—and what needs to change for it to correct.

The BAIT Framework

I think about mispricings through four lenses: Behavioral, Analytical, Informational, and Technical—the BAIT framework. Each plays a distinct role in how and why securities diverge from fair value.

Behavioral Inefficiencies

Behavioral inefficiencies stem from systematic cognitive biases:

1. Sentiment-driven pricing: When fear or euphoria takes over, prices swing well beyond fundamentals. During panics, even quality companies get punished. During bubbles, junk stocks soar. This creates a setup for mean reversion—assuming you're patient enough to wait for the crowd to sober up. 

2. Performance chasing: Capital flows disproportionately to recent outperformers, creating price pressure unrelated to fundamentals. This results in momentum effects that can persist for months before reversing. This naturally creates herding (or FOMO), as investors who didn’t participate in the run-up are therefore “left behind” and underperform their benchmark. Every day the momentum stock goes up, is a day where the investor is questioning why they don’t own this stock, which inevitably leads to chasing, leading to short-term momentum opportunities, and longer-term reversal opportunities. We saw this with NVDA in 2023. Funds benchmarked to the S&P 500 felt forced to buy in—not necessarily because their view on fundamentals changed, but because the stock was driving index returns. Those who didn’t own it were “underperforming,” and in today’s game, underperformance has consequences

3. Crowding psychology: When investors outsource analytical work to "the crowd," information aggregation breaks down. Analysts who conduct independent research can identify situations where consensus opinion has formed without substantive analysis. This often occurs in popular thematic investments where the narrative dominates valuation considerations, like in the quantum computing stocks in starting in mid-2025

Analytical Inefficiencies

Analytical inefficiencies arise from differences in processing capacity and methodology:

1. Differential weighting: Investors assign varying importance to the same information (e.g., overweighting headline news about a minor business segment). This creates opportunities for analysts who can properly contextualize information within a complete business model. For example, a negative headline about 2% of a company's revenue might trigger a 10% stock decline, creating a buying opportunity for investors who understand the limited impact on overall valuation. Or more topical for today – entire sectors getting sold off on tariff news, despite some companies (in the same sector) impacted less than others

2. Time arbitrage: Different investment horizons create mispricing opportunities. Institutional pressure for short-term results allows long-term investors to acquire assets below intrinsic value. Funds that are more judged by short-term results (i.e. multi-managers or more tradey single-managers) will prioritize every new incremental data point and the implication for the coming weeks / months, while longer-term investors are able to look past these items as long as they don’t impact the long-term

3. Over-extrapolation / narrative changes: Investors project recent trends indefinitely forward, creating predictable mean-reversion opportunities. This results in high premiums for growth stocks during expansions and unwarranted discounts during contractions. During COVID, digital winners like ZM, PTON, and W were priced like their pandemic pull-forward was permanent. By 2022, the market did a full 180—assuming even strong businesses like AMZN and NFLX were permanently impaired. Neither was true.

Informational Inefficiencies

Information distribution and processing creates mispricing:

1. Asymmetric information / alternative data: Hedge funds can legally obtain information advantages through specialized data (i.e. credit card / email receipt data / weather data), or channel checks. These information advantages, while temporary, create windows of opportunity before more comprehensive data becomes widely available. On the flip side, a lot of this data also contains biases that aren’t talked about more often (topic for future post), and can create signals that distort reality – nothing like seeing a consumer stock fall 20% into the print on “faulty” credit card data, before jumping 25% on earnings as “true” data points emerged (like CROX in Q4’24 print)

2. Analyst coverage gaps: Large portion of the market is under-followed, either because the market cap is too small, or because the company is located in a non-sexy geography (i.e. outside the US or Europe). Complex corporate structures, conglomerates with diverse business units, and companies with significant off-balance-sheet assets or liabilities often trade at discounts. This creates opportunities for analysts willing dig through filings and research the opportunity to uncover good opportunities.

Technical Inefficiencies

Structural and mechanical factors create inefficiencies independent of fundamental value:

1. Forced transactions: Index rebalancing, fund liquidations, regulatory requirements, and corporate actions force buying or selling regardless of price. These non-discretionary flows can result in constant under-valuation. For example, stocks being added to major indices typically experience buying pressure before inclusion and selling pressure afterward. Similarly, tax-loss selling in December and pension fund rebalancing at quarter-end create temporary periods of mis-pricings

2. Liquidity constraints: Larger funds cannot play in smaller companies, creating structural undervaluation. Multi-managers / pods also usually have a liquidity requirement on daily trading volume, which even excludes a lot of large caps. As a result, these companies often trade at persistent discounts despite comparable or superior fundamentals.

3. Low float volatility: Stocks with limited free float experience amplified price movements from modest capital flows. This creates both risks and opportunities for investors. Companies with high insider ownership or strategic shareholders often have limited floating shares available for trading, causing exaggerated price moves on normal news

Why Inefficiencies Persist

The Edge Assessment

Before exploiting apparent inefficiencies, investors should ask:

1. "Who is on the other side?" Identify the counterparty and why they're willing to accept apparent mispricing. Why is someone selling if you think it’s cheap? Are they forced? Misguided? Do they see something you don’t?

2. "What's my edge?" Determine if you possess informational, analytical, or structural advantages. Be clear on what you’re differentiated on

3. "Is the inefficiency large enough?" Even if something is cheap, is it cheap enough to justify the risk of being wrong?

Checklist Approach

For each inefficiency category:

• Behavioral: Is sentiment extremely positive/negative? Are valuations disconnected from fundamentals due to recent events?

• Analytical: Does the market misunderstand time horizons or narrative changes? Are consensus expectations misaligned?

• Informational: Do you possess unique data or insights? Is complexity obscuring value?

• Technical: Are non-fundamental forces creating price pressure? Are structural constraints preventing price correction?

Markets are ecosystems, not machines. Buyers and sellers come with different goals, constraints, and timelines—and that’s what creates inefficiencies. The most successful investors don’t just identify mispricings. They understand why the mispricing exists, what needs to change for it to correct, and how long they’re willing to wait. That’s what separates “it’s cheap” from real alpha.