The strategy invests in the top-performing industry ETFs (PG6) based on cumulative past returns, adjusting for volatility. The portfolio is rebalanced monthly, focusing on high-return, high-volatility ETFs.
ASSET CLASS: ETFs | REGION: Global | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Risk-Managed, Industry, Momentum
I. STRATEGY IN A NUTSHELL
The strategy invests in 49 industry portfolios, replicable via ETFs. Each month, portfolios are sorted into six groups based on cumulative past returns. The top sixth (PG6) represents winners, and the investor buys ETFs from PG6, weighting them according to expected volatility calculated from the previous month’s realized daily volatility. The portfolio is rebalanced monthly, dynamically adjusting exposure to focus on high-performing ETFs while managing risk.
II. ECONOMIC RATIONALE
Industry ETFs provide diversification and generally lower ex-ante risk than individual stocks. By leveraging volatility clustering, future volatility is estimated from past data, allowing the portfolio to be weighted to reduce risk while maintaining returns. This approach enhances the risk/return profile, offering more stable outcomes than investing in individual stocks without volatility-based adjustments.
III. SOURCE PAPER
Risk-managed industry momentum and momentum crashes [Click to Open PDF]
Klaus Grobys, University of Vaasa; Joni Ruotsalainen, University of Jyväskyla; Janne Äijö, Inderes Oy; [Next Author], University of Vaasa, Department of Accounting and Finance
<Abstract>
This paper investigates Barosso and Santa-Clara’s (2015) risk-managed momentum strategy in an industry momentum setting. We investigate several traditional momentum strategies including that recently proposed by Novy-Marx (2012). We moreover examine the impact of different variance forecast horizons on average payoffs and also Daniel and Moskowitz’s (2016) optionality effects. Our results show in general that neither plain industry momentum strategies nor the risk-managed industry momentum strategies are subject optionality effects, implying that these strategies have no time-varying beta. Moreover, the benefits of risk management are robust across volatility estimators, momentum strategies and subsamples. Finally, the “echo effect” in industries is not robust in subsamples as the strategy works only during the most recent subsample.
IV. BACKTEST PERFORMANCE
| Annualised Return | 25.64% |
| Volatility | 32.15% |
| Beta | -0.137 |
| Sharpe Ratio | 0.72 |
| Sortino Ratio | -0.297 |
| Maximum Drawdown | N/A |
| Win Rate | 49% |
V. FULL PYTHON CODE
from AlgorithmImports import *
class RiskManagedIndustryMomentum(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
self.symbols = ["XLY", # Consumer Discretionary Select Sector SPDR Fund
"PBS", # Invesco Dynamic Media ETF
"PEJ", # Invesco Dynamic Leisure and Entertainment ETF
"PMR", # Invesco Dynamic Retail ETF
"XLP", # Consumer Staples Select Sector SPDR Fund
"PBJ", # Invesco Dynamic Food & Beverage ETF
"XLE", # Energy Select Sector SPDR Fund
"PBW", # Invesco WilderHill Clean Energy ETF
"PXE", # Invesco Dynamic Energy Exploration & Production ETF
"NLR", # VanEck Vectors Uranium+Nuclear Energy ETF
"AMJ", # JPMorgan Alerian MLP Index ETN
"XLF", # Financial Select Sector SPDR Fund
"KBE", # SPDR S&P Bank ETF
"KIE", # SPDR S&P Insurance ETF
"KRE", # SPDR S&P Regional Banking ETF
"PSP", # Invesco Global Listed Private Equity ETF
"XLV", # Health Care Select Sector SPDR Fund
"IBB", # iShares Nasdaq Biotechnology ETF
"IHF", # iShares U.S. Healthcare Providers ETF
"IHE", # iShares U.S. Pharmaceuticals ETF
"XLI", # Industrial Select Sector SPDR Fund
"ITA", # iShares U.S. Aerospace & Defense ETF
"IYT", # iShares Transportation Average ETF
"PHI", # Invesco Water Resources ETF
"XLB", # Materials Select Sector SPDR ETF
"MOO", # VanEck Vectors Agribusiness ETF
"GDX", # VanEck Vectors Gold Miners ETF
"XHB", # SPDR S&P Homebuilders ETF
"IGE", # iShares North American Natural Resources ETF
"XLK", # Technology Select Sector SPDR Fund
"FDN", # First Trust Dow Jones Internet Index
"SOXX", # iShares PHLX Semiconductor ETF
"IGV", # iShares Expanded Tech-Software Sector ET
"IYZ", # iShares U.S. Telecommunications ETF
"XLU", # Utilities Select Sector SPDR Fund
"IGF", # iShares Global Infrastructure ETF
]
self.period = 21
self.SetWarmUp(self.period)
self.data = {}
for symbol in self.symbols:
data = self.AddEquity(symbol, Resolution.Daily)
data.SetLeverage(10)
self.data[symbol] = RollingWindow[float](self.period)
self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance)
def OnData(self, data):
for symbol in self.data:
if symbol in data and data[symbol]:
price = data[symbol].Value
self.data[symbol].Add(price)
def Rebalance(self):
if self.IsWarmingUp: return
# Return sorting
return_volatility = {}
for symbol in self.symbols:
if self.data[symbol].IsReady:
prices = np.array([x for x in self.data[symbol]])
ret = prices[0] / prices[-1] - 1
daily_returns = prices[:-1] / prices[1:] - 1
vol = np.std(daily_returns) * np.sqrt(252)
return_volatility[symbol] = (ret, vol)
sorted_by_return = sorted(return_volatility.items(), key = lambda x: x[1][0], reverse = True)
sixth = int(len(sorted_by_return) / 6)
long = [x for x in sorted_by_return[:sixth]]
short = [x for x in sorted_by_return[-sixth:]]
# Volatility weighting
total_vol_long = sum([1/x[1][1] for x in long])
weight = {}
for symbol, ret_vol in long:
vol = ret_vol[1]
if vol != 0:
weight[symbol] = (1.0 / vol) / total_vol_long
else:
weight[symbol] = 0
total_vol_short = sum([1/x[1][1] for x in short])
for symbol, ret_vol in short:
vol = ret_vol[1]
if vol != 0:
weight[symbol] = -(1.0 / vol) / total_vol_short
else:
weight[symbol] = 0
# Trade execution.
invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in weight:
self.Liquidate(symbol)
for symbol, w in weight.items():
self.SetHoldings(symbol, w)VI. Backtest Performance
