This strategy goes over JP Morgan’s SCTO strategy, a basic XL-sector/RWR rotation strategy with the typical associated risks and returns with a momentum equity strategy. It’s nothing spectacular, but if a large bank markets it, it’s worth looking at.

Recently, one of my readers, a managing director at a quantitative investment firm, sent me a request to write a rotation strategy based around the 9 sector spiders and RWR. The way it works (or at least, the way I interpreted it) is this:

Every month, compute the return (not sure how “the return” is defined) and rank. Take the top 5 ranks, and weight them in a normalized fashion to the inverse of their 22-day volatility. Zero out any that have negative returns. Lastly, check the predicted annualized vol of the portfolio, and if it’s greater than 20%, bring it back down to 20%. The cash asset–SHY–receives any remaining allocation due to setting securities to zero.

For the reference I used, here’s the investment case document from JP Morgan itself.

Here’s my implementation:

Step 1) get the data, compute returns.

require(quantmod)
require(PerformanceAnalytics)
symbols <- c("XLB", "XLE", "XLF", "XLI", "XLK", "XLP", "XLU", "XLV", "XLY", "RWR", "SHY")
getSymbols(symbols, from="1990-01-01")
prices <- list()
for(i in 1:length(symbols)) {
  prices[[i]] <- Ad(get(symbols[i]))  
}
prices <- do.call(cbind, prices)
colnames(prices) <- gsub("\.[A-z]*", "", colnames(prices))
returns <- na.omit(Return.calculate(prices))

Step 2) The function itself.

sctoStrat <- function(returns, cashAsset = "SHY", lookback = 4, annVolLimit = .2,
                      topN = 5, scale = 252) {
  ep <- endpoints(returns, on = "months")
  weights <- list()
  cashCol <- grep(cashAsset, colnames(returns))
  
  #remove cash from asset returns
  cashRets <- returns[, cashCol]
  assetRets <- returns[, -cashCol]
  for(i in 2:(length(ep) - lookback)) {
    retSubset <- assetRets[ep[i]:ep[i+lookback]]
    
    #forecast is the cumulative return of the lookback period
    forecast <- Return.cumulative(retSubset)
    
    #annualized (realized) volatility uses a 22-day lookback period
    annVol <- StdDev.annualized(tail(retSubset, 22))
    
    #rank the forecasts (the cumulative returns of the lookback)
    rankForecast <- rank(forecast) - ncol(assetRets) + topN
    
    #weight is inversely proportional to annualized vol
    weight <- 1/annVol
    
    #zero out anything not in the top N assets
    weight[rankForecast <= 0] <- 0
    
    #normalize and zero out anything with a negative return
    weight <- weight/sum(weight)
    weight[forecast < 0] <- 0
    
    #compute forecasted vol of portfolio
    forecastVol <- sqrt(as.numeric(t(weight)) %*% 
                          cov(retSubset) %*% 
                          as.numeric(weight)) * sqrt(scale)
    
    #if forecasted vol greater than vol limit, cut it down
    if(as.numeric(forecastVol) > annVolLimit) {
      weight <- weight * annVolLimit/as.numeric(forecastVol)
    }
    weights[[i]] <- xts(weight, order.by=index(tail(retSubset, 1)))
  }
  
  #replace cash back into returns
  returns <- cbind(assetRets, cashRets)
  weights <- do.call(rbind, weights)
  
  #cash weights are anything not in securities
  weights$CASH <- 1-rowSums(weights)
  
  #compute and return strategy returns
  stratRets <- Return.portfolio(R = returns, weights = weights)
  return(stratRets)      
}

In this case, I took a little bit of liberty with some specifics that the reference was short on. I used the full covariance matrix for forecasting the portfolio variance (not sure if JPM would ignore the covariances and do a weighted sum of individual volatilities instead), and for returns, I used the four-month cumulative. I’ve seen all sorts of permutations on how to compute returns, ranging from some average of 1, 3, 6, and 12 month cumulative returns to some lookback period to some two period average, so I’m all ears if others have differing ideas, which is why I left it as a lookback parameter.

Step 3) Running the strategy.

scto4_20 <- sctoStrat(returns)
getSymbols("SPY", from = "1990-01-01")
spyRets <- Return.calculate(Ad(SPY))
comparison <- na.omit(cbind(scto4_20, spyRets))
colnames(comparison) <- c("strategy", "SPY")
charts.PerformanceSummary(comparison)
apply.yearly(comparison, Return.cumulative)
stats <- rbind(table.AnnualizedReturns(comparison),
               maxDrawdown(comparison),
               CalmarRatio(comparison),
               SortinoRatio(comparison)*sqrt(252))
round(stats, 3)

Here are the statistics:

                          strategy   SPY
Annualized Return            0.118 0.089
Annualized Std Dev           0.125 0.193
Annualized Sharpe (Rf=0%)    0.942 0.460
Worst Drawdown               0.165 0.552
Calmar Ratio                 0.714 0.161
Sortino Ratio (MAR = 0%)     1.347 0.763

               strategy         SPY
2002-12-31 -0.035499564 -0.05656974
2003-12-31  0.253224759  0.28181559
2004-12-31  0.129739794  0.10697941
2005-12-30  0.066215224  0.04828267
2006-12-29  0.167686936  0.15845242
2007-12-31  0.153890329  0.05146218
2008-12-31 -0.096736711 -0.36794994
2009-12-31  0.181759432  0.26351755
2010-12-31  0.099187188  0.15056146
2011-12-30  0.073734427  0.01894986
2012-12-31  0.067679129  0.15990336
2013-12-31  0.321039353  0.32307769
2014-12-31  0.126633020  0.13463790
2015-04-16  0.004972434  0.02806776

And the equity curve:

To me, it looks like a standard rotation strategy. Aims for the highest momentum securities, diversifies to try and control risk, hits a drawdown in the crisis, recovers, and slightly lags the bull run on SPY. Nothing out of the ordinary.

So, for those interested, here you go. I’m surprised that JP Morgan itself markets this sort of thing, considering that they probably employ top-notch quants that can easily come up with products and/or strategies that are far better.

Thanks for reading.

NOTE: I am a freelance consultant in quantitative analysis on topics related to this blog. If you have contract or full time roles available for proprietary research that could benefit from my skills, please contact me through my LinkedIn here.