Mean Reversion Stocks

Mean reversion is a statistical approach with defined entry criteria (2 SD from mean, RSI extreme, reversal confirmation) and exit rules. Bottom fishing typically refers to buying falling stocks hoping they'll bounce - without rigorous criteria. Mean reversion has mathematical edge based on statistical properties; bottom fishing is often hope-based. Mean reversion also applies to overbought conditions (shorting), while bottom fishing is only about buying dips.

No, mean reversion works best on large-cap stocks with stable fundamentals, high liquidity, and established trading ranges. Avoid: small-caps (can trend for extended periods), stocks with pending events (earnings, M&A), stocks in structural decline, and stocks with low liquidity. Optionable, highly liquid stocks are preferred, since you can use options for leveraged or hedged exposure.

Well-executed mean reversion trades have 50-60% success rate. This might seem modest, but with proper risk-reward (2:1 or better), you're profitable even at 40% win rate. The edge comes from statistical probability - buying at 2 SD below mean has 95%+ historical probability of price eventually trading back to mean. The key is managing the 5% where it doesn't revert through stop losses.

For US large-caps, mean reversion typically takes 3-7 trading days. Set a time stop of 5 days for intraday-originated trades and 10 days for swing trades. If price hasn't begun reverting within 1.5x the expected half-life, the original thesis may be wrong - exit regardless of current P&L. Holding too long ties up capital and often results in eventual stop-out anyway.

Generally no - averaging down in mean reversion is dangerous because if your thesis is wrong (stock is trending, not reverting), you're adding to a losing position. However, a planned scaling approach can work: enter 50% at 2 SD, add 50% at 2.5 SD with original stop still valid. This must be pre-planned with position sizing that accounts for full position at worst entry. Never average down reactively.

Key differences: (1) ADX below 20 suggests range-bound (mean reverting), above 25 suggests trending (continuation likely). (2) Weekly chart: if weekly trend is down, daily oversold may just be pause before further decline. (3) Volume pattern: mean reversion shows declining volume into the low then spike on reversal; trend continuation shows high volume throughout the move. (4) RSI behavior: mean reversion RSI bounces from extreme quickly; trending RSI stays below 30 for extended period (chronic oversold).

Absolutely - this is the ideal approach. Mean reversion and trend following have negative correlation (when one struggles, the other often works). Run both strategies with separate capital allocation. In trending markets (high ADX, directional VIX), trend following dominates. In ranging markets (low ADX, calm VIX), mean reversion dominates. Combined portfolio shows more consistent returns with lower drawdowns than either strategy alone.

Avoid mean reversion 1 week before and 1 week after a stock's earnings announcement. The extreme move may be fundamental (revaluation based on results) rather than temporary. After results, wait for new trading range to establish - the old 20-day mean is obsolete. If you're already in a position and earnings are announced unexpectedly, exit immediately - you're now in an unpredictable fundamental event, not a statistical setup.

ATR-based stops adapt to current volatility. For mean reversion: Initial stop = Entry - 2×ATR for longs (Entry + 2×ATR for shorts). This places the stop outside normal noise. As the trade moves favorably, trail the stop to Entry - 1.5×ATR, then Entry - 1×ATR. Using ATR rather than a fixed percentage prevents getting stopped out by normal volatility while protecting against adverse moves. A typical 14-period ATR works well for US stocks.

Sector rotation can invalidate mean reversion. If money is flowing out of the tech sector into financials, an oversold tech stock may continue declining despite statistical extremes. Monitor: (1) Sector relative strength - is the sector underperforming the S&P 500? (2) Fund flows by sector. (3) Individual stock vs sector divergence. Best mean reversion: stock oversold while sector stable or strong. Worst: stock oversold in an oversold sector (rotation away).

Walk-forward prevents overfitting: (1) Divide historical data into in-sample (IS) and out-of-sample (OOS) periods, e.g., 2 years IS, 6 months OOS. (2) Optimize parameters (entry threshold, lookback period, etc.) on IS period. (3) Test optimized parameters on OOS period without modification. (4) Roll forward: next period, IS = previous IS + OOS, new OOS = next 6 months. (5) Average OOS results across all windows for realistic performance estimate. Parameters that degrade severely in OOS indicate overfitting.

Eigenvalue decomposition on covariance matrix of stock returns identifies principal components. The last few eigenvectors (smallest eigenvalues) represent portfolios with minimum variance - these are most likely to be stationary/mean-reverting. Construct spread portfolio using these eigenvector weights. This mathematically identifies the most mean-reverting linear combination of stocks in your universe. More robust than pairwise analysis for multi-stock mean reversion. Eigenportfolios can be traded using the same Z-score framework.

Standard mean reversion assumes normal distribution, but stock returns have fat tails (more extreme moves than normal predicts). Adjustments: (1) Use Student's t-distribution instead of normal for Z-score calculations - requires higher thresholds for same statistical significance. (2) Implement hard-coded maximum loss limits regardless of model signals. (3) Maintain tail risk hedges (index puts) sized to cover portfolio in 3+ sigma events. (4) Reduce position sizes when kurtosis of recent returns exceeds historical norm - fat tails are clustering.

Microstructure matters significantly: (1) Mean reversion often requires entering against prevailing order flow (buying when others are selling) - face adverse selection and wider effective spreads. (2) Limit orders may not fill as price continues against you; market orders ensure fill but face slippage. (3) Optimal execution: use limit orders at slight improvement to NBBO, with time limit converting to market if unfilled. (4) Avoid executing at open/close when microstructure is most adverse. (5) For systematic portfolios, spread execution across the day to reduce impact.

Real-time monitoring: (1) Rolling win rate - alert if drops below 40% over 20 trades (potential regime change). (2) Rolling Sharpe ratio - alert if drops below 0.5 annualized (strategy degradation). (3) Running ADF test on mean reversion signals - if p-value rises above 0.1, mean reversion property may be weakening. (4) Correlation of returns with market - if rising, strategy losing market neutrality. (5) Maximum drawdown - if exceeds 1.5x historical max DD, pause and investigate. Automated alerts enable quick response to strategy breakdown.