Does a Market Trend Filter Beat Buy-and-Hold? An Out-of-Sample Test on 989 Stocks

What Did We Test?

The strategy does not use a separate stock-level indicator. It assigns 100% of the allotted capital to the stock when the market condition is satisfied, with no leverage, and moves to cash otherwise. The condition is evaluated at each day’s close, with any resulting action taken at the next day’s open.

What Happened?

The main caveat is that the tickers were current S&P 500, S&P 400, and S&P 600 constituents, so companies removed from those indexes during the window are absent, creating survivorship bias. Also, the 989 tickers were not independent: their mean pairwise return correlation was 0.29, and the portfolio excess-return interval ran from -12.42 to 2.67 percentage points, which includes zero.

How Many Tickers Did It Beat Buy-and-Hold On?

The strategy beat buy-and-hold on an annualized growth rate (CAGR) basis for 306 of 989 tickers, or 30.9%. The excess CAGR versus buy-and-hold had a mean of -2.40 percentage points, a median of -2.68 percentage points, a 10th percentile of -9.15 percentage points, and a 90th percentile of 4.37 percentage points. This means the strategy lagged on most tickers, although the gap was positive for some.

The strategy had a smaller maximum drawdown, the largest decline from a previous peak, on 81.5% of tickers. Median maximum drawdown was 51.83% for the strategy versus 62.86% for buy-and-hold. Its Sharpe ratio, a return measure adjusted for volatility, was better on 32.2% of tickers, with median values of 0.39 versus 0.45, and median time in the market was 79.1%.

Results were consistent in the broad share of winners across the two sub-periods: the strategy beat buy-and-hold on 33.7% of tickers in the first half and 33.5% in the second half. Median excess CAGR remained negative in both, at -22.83 percentage points and -18.64 percentage points. By sector, Communication Services had the highest beat share at 48.5%, followed by Real Estate at 45.3% and Consumer Discretionary at 41.2%; Utilities stood out on the low end at 2.7%, while every sector listed had a negative median excess CAGR.

Is the Result Statistically Significant?

The naive tests point in one direction. The strategy beat buy-and-hold on 306 of 989 tickers, or 30.9%, which is below a 50% coin flip; the exact binomial test gave a p-value of less than 0.0001. A p-value is the probability of seeing a result at least this extreme if there were no real edge. The mean excess CAGR was -2.40 percentage points, with a bootstrap 95% interval of -2.86 to -1.93 percentage points, and the t-test gave t = -10.14 with p = less than 0.0001. On their own, these tests suggest the strategy underperformed buy-and-hold.

Those tests treat the tickers as more independent than they really are. Their mean pairwise correlation was 0.29, making the 989 tickers behave like about 3.5 independent observations; after adjusting for this dependence, the t-statistic was -0.60 and the p-value was 0.5999. That adjusted result is NOT strong evidence of an edge. The equal-weight portfolio comparison also showed lower performance for the strategy, with a CAGR of 11.80% versus 16.33% for buy-and-hold, while the block-bootstrap 95% interval for annualised excess return was -12.42 to 2.67 percentage points, which includes zero and is therefore NOT strong evidence of an edge.

Ticker-level tests found 77 significant tickers at the 5% level, including 76 with a positive mean, while chance alone would produce about 48.6. After the Benjamini-Hochberg false-discovery correction, 0 remained significant, including 0 positive. The pooled-trade test found 50,759 closed trades, a mean return per trade of 2.61%, and p = less than 0.0001, with a 95% interval of 2.42% to 2.80%, but trades overlap in time across tickers, so that p-value is indicative only. The data can show that naive tests and pooled trades look positive in places, but after accounting for ticker dependence and multiple testing, it cannot establish a statistically significant edge over buy-and-hold.

The Equal-Weight Portfolio View

Limits of This Test

Conclusions

The biggest caveat is survivorship bias: the test used current index constituents, so companies removed, acquired, or delisted during the window were absent. This is an educational backtest walkthrough, not investment advice, and past performance in a backtest does not predict future results.

Setup, Audit & Replication

# =========================================================
# trend_regime_family.R  -- PRE-REGISTERED strategy family (written before any
# results were examined).
#
# One rule family, nine variants: be long a stock while a simple moving-average
# trend condition holds, otherwise sit in cash.
#   filter = "own"    : the stock's close is above its own N-day simple moving average
#   filter = "market" : the market's (SPY) close is above SPY's own N-day moving average
#   filter = "both"   : both conditions hold
#   lookback N in {100, 150, 200} trading days
# Entry when the condition becomes true, exit when it becomes false. Signals are
# evaluated at each bar's close and filled at the next open (engine rule).
# The only free choices are `filter` and `lookback`; they are picked on a
# training set of tickers and an earlier period, then frozen and tested on
# different tickers and a later period (see research_trend_regime.R).
# =========================================================

trend_regime_signals <- function(ohlc, params, market_close) {
  cl <- as.numeric(quantmod::Cl(ohlc))
  n  <- params$lookback
  own_ok    <- cl > TTR::SMA(cl, n)
  market_ok <- market_close > TTR::SMA(market_close, n)
  cond <- switch(params$filter, own = own_ok, market = market_ok, both = own_ok & market_ok)
  list(entry = cond %in% TRUE, exit = (!cond) %in% TRUE)   # NA (insufficient history) -> no action
}

trend_regime_description <- function(filter, lookback) {
  what <- switch(filter,
    own    = sprintf("the stock's closing price is above its own %d-day simple moving average", lookback),
    market = sprintf("the S&P 500 index fund (SPY) closes above its own %d-day simple moving average", lookback),
    both   = sprintf("both the stock's closing price is above its own %d-day simple moving average and SPY closes above its own %d-day simple moving average", lookback, lookback))
  sprintf("Hold the stock (100%% of the capital allotted to it, no leverage) while %s; otherwise hold cash. The condition is checked at each day's close and acted on at the next day's open.", what)
}

make_variant_spec <- function(filter, lookback) {
  list(name = sprintf("Trend filter (%s, %d-day)", filter, lookback),
       description = trend_regime_description(filter, lookback),
       params = list(filter = filter, lookback = lookback),
       needs_market = TRUE,
       signals = trend_regime_signals)
}

trend_regime_variants <- function() {
  g <- expand.grid(filter = c("own", "market", "both"), lookback = c(100, 150, 200), stringsAsFactors = FALSE)
  lapply(seq_len(nrow(g)), function(i) list(id = sprintf("%s_%d", g$filter[i], g$lookback[i]), filter = g$filter[i], lookback = g$lookback[i],
                                            label = sprintf("%s filter, %d-day", c(own = "Own-trend", market = "Market-trend", both = "Own + market trend")[[g$filter[i]]], g$lookback[i])))
}

# FROZEN after selection on training data (see selection.json): filter = "market", lookback = 100
strategy_spec <- make_variant_spec("market", 100)