Usually, no—not as a universal forecast. A move above or below the 200-day moving average can describe a change in the market’s intermediate- to long-term trend, and some historical trading rules using that signal produced attractive results. But a crossing does not reliably predict a gain, identify every market top or bottom, or establish one forecast horizon for every stock. The outcome depends on the exact rule, asset universe, sample period, trading frequency, position taken, benchmark, and costs.
What the 200-day moving average measures
The 200-day simple moving average (SMA) is the arithmetic mean of a security’s last 200 trading-day closing prices. On day t, it can be written as SMA200(t) = (Close(t) + Close(t-1) + ... + Close(t-199)) / 200. Each new close enters the calculation while the oldest close drops out.
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Because it averages roughly ten months of trading data, the line changes slowly. It filters out much of the day-to-day noise but also responds late to a genuine reversal. The 200-day SMA is therefore a description of where recent price sits relative to a long recent history, not a fundamental valuation measure and not a guarantee about the next return.
What a crossing means
A crossing above occurs when price moves from below the moving-average line to above it, usually based on closing prices. A crossing below is the reverse. A chart can show an intraday touch, a closing cross, or a confirmed move that remains on the new side for several sessions; those are different rules and can produce different results.
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The line itself can be rising, falling, or nearly flat. Price above a falling average is not the same trend condition as price above a strongly rising average. Likewise, a brief cross caused by a one-day jump is not equivalent to a sustained change in direction.
Why a crossing is not the same as a prediction
A prediction makes a claim about a future return over a defined horizon. A moving-average strategy instead specifies an action: remain invested, move to cash, buy, sell, or short after a signal. Its measured return includes the rule’s exposure, timing, risk, and costs. A strategy can outperform a benchmark without the crossing being a reliable positive-return forecast for every stock or period.
What the historical evidence actually tested
The strongest directly relevant historical results available come from tests of particular rules and samples. None establishes a timeless law for a new crossing today.
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| Study | Universe and method | Reported result | What it does not establish |
|---|---|---|---|
| Clare, Seaton, Smith and Thomas (2013), Journal of Asset Management | Technical rules, including a popular 200-day moving-average rule, applied to the S&P 500 and compared with passive long-only investment | The abstract says the tested simple rules dominated passive long-only investment in that historical sample. It also reports that common stop-loss rules added no value and that end-of-month monthly decisions beat more frequent trading. | It is not a current estimate for every stock, every horizon, or every implementation. The result depends on the sample, benchmark and rule definitions. |
| Glabadanidis (2015), International Review of Finance | Monthly returns for value-weighted U.S. portfolios sorted by size, book-to-market and momentum; seven international markets; and about 18,000 individual U.S. stocks | The paper reports 3–7% annual risk-adjusted returns after transaction costs for its moving-average strategy and samples. | The figure is not an expected return for a current investor or a clean estimate for one 200-day crossing. The author attributes much of the performance to stock-return volatility; the payoff resembled an at-the-money protective put and could be partly explained by conditional factor models, especially the default premium. |
| Reschenhofer (2020) review | Comparisons involving trend-momentum, MACD and moving-average strategies on the Dow Jones Industrial Average | In the strategies and comparisons reviewed, technical approaches had been dramatically outperformed by buy-and-hold from about 1980 onward, even before transaction costs. The review stresses cumulative returns rather than relying only on long-run averages. | This is a result for the review’s investigated strategies and DJIA comparison, not proof that every moving-average rule fails in every market. |
| Hurst, Ooi and Pedersen (2017) | Diversified trend-following or time-series momentum across global markets since 1880 | Average returns were positive in every decade studied, with positive performance in eight of the ten largest crisis periods defined by drawdowns of a 60/40 stock-bond portfolio. | This does not directly test one 200-day signal on one stock or predict the next equity return. |
| Han, Zhou and Zhu (2016) | A multi-horizon trend factor built from moving-average price signals ranging from three to 1,000 days | The combined factor outperformed short-term reversal, momentum and long-term reversal factors in their analysis. | A cross-sectional, multi-horizon factor cannot be treated as direct evidence that an isolated 200-day crossing predicts an individual stock’s return. |
The direct S&P 500 result
Clare, Seaton, Smith and Thomas wrote in their 2013 abstract: “We find that a range of fairly simple rules, including the popular 200-day moving average (MA) trading rule, dominate the long-only, passive investment in the index.” That sentence describes their historical S&P 500 test and its comparison, not a promise that buying after every upward cross will beat an index from now on.
The same study found that adding common stop-loss rules did not improve the result and that making investment decisions at the end of each month beat more frequent decisions in its tested setup. That finding matters because a daily signal and a month-end signal can generate different entries, exits and turnover even when both use a 200-day average.
What the broader moving-average study adds
Glabadanidis examined a much wider set of portfolios, markets and individual stocks. Its reported 3–7% annual risk-adjusted return after transaction costs is useful evidence that some moving-average implementations historically earned compensation beyond the chosen risk adjustment. It is not a forecast for a particular ticker. The paper also says that volatility drove much of the result and that the payoff looked similar to buying an at-the-money protective put on a buy-and-hold position. Those observations suggest that a rule may derive value from reducing exposure during turbulent declines rather than simply identifying every upward move.
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Why the critical review changes the picture
Reschenhofer’s review is a warning against selecting only successful intervals. In its Dow Jones comparisons, the tested technical strategies lagged buy-and-hold from roughly 1980 onward, even without transaction costs. The review argues that average annual returns over a long span can conceal when gains and losses occurred; cumulative wealth, drawdowns and subperiod results reveal the path an investor actually had to endure.
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The long-history study by Hurst, Ooi and Pedersen supports the idea that diversified trend following has persisted across many markets and decades. Its positive return in every decade since 1880 and in eight of ten major 60/40 drawdown crises are important context for trend-following evidence. They do not isolate the 200-day window, a single stock, or a single equity index, so they cannot answer the literal question on their own.
Why results vary from one test to another
The signal definition
A valid test must decide whether a signal uses the closing price, an intraday observation, or a confirmation period; whether execution occurs at that close, the next open, or month-end; and whether a move below the line sends the position to cash or into a short position. Small choices can change both returns and turnover. A rule that waits for month-end confirmation may avoid many whipsaws but enter later than a daily rule.
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The market and time period
Large-cap U.S. indexes, small stocks, international markets and individual companies have different liquidity, volatility and trend behavior. A rule that looks strong in one bull-and-bear sequence can weaken when markets move sideways or rebound sharply after a sell-off. Historical findings therefore need the exact universe and dates attached to them.
Risk exposure and benchmark
Moving to cash after a downside cross reduces market exposure; shorting creates a different return and risk profile. Comparing either approach with an always-invested index without matching exposure can make the result look better or worse for reasons unrelated to forecasting skill. A fair comparison uses the same asset universe, dates and clearly stated benchmark.
Trading costs
Costs include commissions, bid-ask spreads and market impact. Spreads are often wider in small stocks and less efficient markets. A signal that changes position frequently can lose its apparent edge after realistic execution costs, even when a frictionless backtest looks attractive. The 2013 S&P 500 study’s preference for end-of-month decisions illustrates how trading frequency can affect the result.
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How to test whether the signal helped in a specific setting
- Define the question first. Choose the asset universe, date range and forecast horizon. “Return over the next month” is a different test from “return until the next cross.”
- Specify the average. State that it is a 200-day simple average of closing prices, and decide whether adjusted prices are used for splits and distributions.
- Specify the crossing rule. For example, require a closing price above the SMA after being below it, or require two consecutive closes. Do not change the rule after seeing results.
- Set execution timing. A signal known only at the close cannot be traded at that same close without an explicit, realistic assumption. Use the next available price or a stated month-end procedure.
- Choose the position response. Compare long-only, long-or-cash and long-short versions separately. Record position size and whether dividends, interest on cash and borrow costs are included.
- Apply costs before judging the result. Include commissions, the spread paid on entry and exit, and an allowance for market impact. Use higher assumptions for less liquid securities.
- Report more than an average. Show cumulative return, annualized volatility, maximum drawdown, turnover and results in separate subperiods. A long-run average alone can hide long stretches of underperformance.
- Check robustness. Test reasonable nearby windows and different start dates, but label those as additional tests rather than selecting the best result. Keep an untouched period for out-of-sample evaluation when possible.
How to interpret a fresh crossing on a chart
Price crosses above
An upward cross says that recent price has improved enough to move above a slow reference line. It may support a trend-following decision for an investor who has already defined a rule, but it does not say how far price will rise or for how long. Check whether the average itself is rising, whether the move survives at the chosen confirmation interval, and whether the position size fits the portfolio.
Price crosses below
A downward cross signals deterioration relative to the prior 200 sessions. It can be used as an exit or de-risking trigger only if that response was chosen in advance. Selling after a decline can reduce further losses, but it can also miss a rapid rebound; the historical studies do not establish a universal best response.
Repeated crosses and sideways markets
When price oscillates around a flat average, alternating signals can produce whipsaws: a purchase, sale and repurchase with little net trend but several costs. Confirmation rules, month-end evaluation or a separate volatility limit may reduce activity, but each modification changes the strategy and must be tested on its own.
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Quick Recap
A practical decision framework
| If your objective is… | A 200-day crossing can reasonably be used as… | Do not infer… |
|---|---|---|
| Trend participation | One input in a pre-defined rule that changes exposure after a confirmed move | That the next return will be positive or that the signal marks the exact low |
| Downside control | A possible de-risking trigger whose benefit must be weighed against whipsaws and missed rebounds | That moving to cash always improves risk-adjusted performance |
| Stock selection | A screening variable combined with liquidity, fundamentals and diversification controls | That the line alone predicts an individual company’s earnings or fair value |
| Strategy evaluation | A clearly documented hypothesis with costs, turnover and subperiod analysis | That a historical average transfers unchanged to a live portfolio |
Common mistakes to avoid
- Calling every cross a forecast. A rule-generated return and a conditional prediction are not identical claims.
- Mixing daily and monthly evidence. The timing convention is part of the strategy.
- Ignoring the cash or short leg. “Sell” can mean cash, a hedge or a short position, each with different outcomes.
- Using frictionless prices. Commissions, spreads and market impact can erase a small apparent edge.
- Reporting only a long-run mean. Examine cumulative wealth, drawdowns and distinct market regimes.
- Generalizing from diversified trend following. Results across many markets do not prove that one stock will respond to one 200-day crossing.
- Assuming historical success is current evidence. No cited study provides a live estimate for a new crossing or a universal forecast horizon.
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