Common mistakes about the stock market

The common belief: A strong economy means a strong stock market, and weak GDP means falling stocks.

What the data shows: The contemporaneous link is weak and often misleading. The index is dominated by a handful of large, global, profit-driven firms whose earnings need not track domestic output, and prices move on expectations rather than realised activity. Across countries, research by Dimson, Marsh and Staunton finds no dependable relationship between long-run GDP growth and equity returns. Stocks can fall in expansions and rally in contractions.

→ Fuller explanation: Why don’t stocks and the economy move together?

The common belief: Prices respond to current data, so good news lifts stocks and bad news sinks them.

What the data shows: Markets discount anticipated conditions, so prices often move before the data confirm them and fade once the news is widely expected. In the 2007–2009 cycle the S&P 500 reached its closing low of 676.53 on 9 March 2009, roughly three months before the NBER trough of June 2009, and gained about 70% over the following twelve months while unemployment was still rising. The signal is in the surprise, not the level.

→ Extended explanation: Why do markets price the future, not the present?

The common belief: Long-run equity returns are driven mainly by rising share prices and expanding valuation multiples.

What the data shows: Over long horizons, earnings growth and reinvested dividends dominate; multiple expansion contributes little. According to S&P Dow Jones Indices, dividend income made up roughly 31% of the S&P 500’s total return from 1926 to 2025, exceeding half in some decades such as the 1940s and 1970s. Price-only charts understate compounding, and changes in the valuation multiple tend to wash out across full cycles.

→ The complete explanation: What drives stock returns over the long run?

The common belief: A spiking volatility index is a warning to sell before further losses.

What the data shows: The VIX measures the option market’s expected volatility, which is highest after prices have already fallen. Our equity-volatility dataset tracks this measure across decades. Its record closes — above 80 in November 2008 and again in March 2020 — clustered around major market lows rather than marking the start of fresh declines. Historically, extreme readings have coincided with elevated subsequent returns, which is why the VIX is often read as a contrarian rather than a directional gauge.

→ Full account: Is a high VIX a contrarian buy signal?

The common belief: The market rises because most of its constituents go up, so the average stock resembles the index.

What the data shows: From 1926 to 2016, just over 4% of U.S.-listed stocks accounted for the entire net wealth created above one-month Treasury bills, while the remaining ~96% collectively only matched cash and about 57% had lifetime returns below T-bills (Bessembinder, 2018). The index advances through positive skewness — a few extreme winners offsetting a majority of laggards — not broad participation. This is why a cap-weighted index and the “average stock” are different objects, and why concentration in mega-cap leaders (2023–2024) is a recurring feature, not an anomaly.

→ Complete breakdown: Why do a few stocks dominate index returns?

The common belief: It is safer to re-enter equities once a recession has officially ended.

What the data shows: Recessions are dated with a long lag, and equities have historically bottomed before activity does. The NBER confirmed the June 2009 trough only in September 2010, by which point the S&P 500 had already risen sharply from its March 2009 low. Waiting for the “all-clear” has, in past cycles, meant missing the steepest part of the recovery, because the market turns on improving expectations, not confirmed data.

→ Complete explanation: Why do stocks rally before recessions end?

The common belief: Smaller companies reliably out-earn large ones thanks to a durable “size premium.”

What the data shows: The size effect documented by Banz (1981) has been weak and inconsistent in U.S. data since the 1980s, and much of it disappears once junk, illiquid and low-quality firms are removed. Small caps tend to be more cyclical and more sensitive to credit conditions, so they have historically led in early recovery and lagged in late-cycle and stress phases. “Small beats large” is conditional, not a constant.

→ Detailed explanation: Why do small caps behave differently from large caps?

The common belief: Moving between sectors at each cycle stage is a dependable way to time the market.

What the data shows: Sector leadership does shift with the cycle — defensives such as staples, utilities and health care have tended to lead late-cycle and in contractions, cyclicals such as industrials, financials and discretionary in early recovery. But the turning points are only clear in hindsight, and crowded rotations can reverse quickly. The pattern is a description of regime behaviour, not a timing rule with a stable edge.

→ In-depth explanation: How does sector rotation work across cycles?

The common belief: Share buybacks are a cosmetic way to inflate earnings per share and nothing more.

What the data shows: Buybacks are a distribution channel, like dividends, and the relevant measure is total shareholder yield — dividends plus net buybacks. Since the mid-2000s, aggregate buybacks have at times exceeded aggregate dividends for S&P 500 firms, partly because they are more flexible and more tax-efficient for shareholders. They lift EPS only when shares are retired below intrinsic value; executed at high valuations or funded by debt, they can destroy value rather than create it.

→ The full explanation: How do buybacks affect shareholder returns?

The common belief: The equity risk premium is a known constant, around 5%, that can be plugged into any model.

What the data shows: The premium is an estimate, not an observable, and realised history is a poor guide to the forward number. Long-run U.S. estimates cluster in a 4–6% range, but implied measures (such as Damodaran’s) move materially with prices and rates — compressed near the late-1990s peak, elevated after 2008. The premium you assume depends on the method, the horizon and the starting valuation, so treating it as a single fixed figure is the source of many flawed forecasts.

→ Full breakdown: How is the equity risk premium measured?

The common belief: “Quality” is a marketing label for richly valued growth names.

What the data shows: In the factor literature, quality is defined by fundamentals — high and stable profitability, low leverage, modest earnings volatility — not by price or growth. Research by Asness, Frazzini and Pedersen on “quality minus junk” finds that profitable, safe, well-managed firms have historically earned a premium, and quality often overlaps with value rather than growth. It tends to be defensive: quality has historically held up better in drawdowns and late-cycle stress.

→ Fuller explanation: What is the quality factor in equity investing?

The common belief: Buying recent winners is naive performance-chasing with no real edge.

What the data shows: Momentum — the tendency of past 3-to-12-month winners to keep outperforming — is one of the most persistent anomalies documented across markets and asset classes since Jegadeesh and Titman (1993). It is not free: momentum is prone to sharp crashes at sentiment reversals, such as the spring 2009 rebound when prior losers violently outperformed. The factor is real and well-evidenced, but its return comes with significant tail risk.

→ Extended explanation: What is the momentum factor in markets?

The common belief: When the S&P 500 earnings yield is above the 10-year Treasury yield, stocks are cheap — the “Fed model.”

What the data shows: The Fed model compares a real magnitude (the earnings yield) with a nominal one (the bond yield), which conflates inflation regimes. Critics including Asness (2003) note that the apparent relationship largely reflects the 1965–2000 era when inflation and rates moved together, and that the model has weak power to predict long-run returns. Cycle-adjusted valuation measures such as CAPE have shown a firmer link to subsequent returns.

→ The complete explanation: What is the Fed model and why is it flawed?