US Equity Value Factor Strategy: Monthly Long High and Short Low Book-to-Price Stocks
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Fact, Fiction, and the Size Effect
Ron Alquist; Ronen Israel; Tobias J. Moskowitz
- United States Department of the Treasury
- ?Financial Stability Oversight Council, U.S. Treasury
- Capital University
- ?AQR Capital Management, LLC
- National Bureau of Economic Research
- ATAgency for Quality Assurance and Accreditation Austria
- Yale University
- ?AQR Capital
- ?National Bureau of Economic Research (NBER)
- ?Yale University, Yale SOM
Strategy in a nutshell
The investment universe encompasses NYSE, AMEX, and NASDAQ stocks, targeting "value" investing through the HML portfolio, which longs stocks with high book-to-price ratios and shorts those with low ratios. This approach includes analyzing the average returns of two subsets: HML small, focusing on small-cap stocks, and HML large, targeting large-cap stocks, both adopting a value investing stance. The portfolio, equally weighted across all holdings, undergoes monthly rebalancing to maintain its strategy alignment and capture the essence of value investing through a diversified approach across market capitalizations.
Economic rationale
One theory suggests investors excessively favor growth stocks due to their growth potential, leading to value stocks being undervalued. Some scholars argue that the market value to book value ratio acts as a risk indicator, positing that the higher returns from low MV/BV stocks serve as a reward for bearing additional risk. Typically, stocks with low MV/BV ratios are in financial distress, further reinforcing the concept that their higher returns compensate investors for the increased risk associated with these stocks.
Backtest performance
Full Python code
from AlgorithmImports import *
import numpy as np
from typing import List
class MarketBookValueFactor(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.UniverseSettings.Leverage = 5
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.0
self.longList: List[Symbol] = []
self.shortList: List[Symbol] = []
self.exchangeList: List[str] = ['NYSE', 'NASDAQ', 'AMEX']
self.quintile: int = 5
self.rebalancePeriod: int = 12
self.shouldRebalance: bool = True
self.spy: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.Schedule.On(self.DateRules.MonthEnd(self.spy),
self.TimeRules.AfterMarketOpen(self.spy),
self.RebalancePortfolio)
def CoarseSelectionFunction(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
if not self.shouldRebalance:
return Universe.Unchanged
filtered = [c for c in coarse if c.Symbol.SecurityType == SecurityType.Equity
and c.Exchange in self.exchangeList
and c.PriceBookRatio > 0]
sortedSecurities = sorted(filtered, key=lambda x: x.PriceBookRatio)
if len(sortedSecurities) > self.quintile:
quintileSize = len(sortedSecurities) // self.quintile
self.longList = [s.Symbol for s in sortedSecurities[:quintileSize]]
self.shortList = [s.Symbol for s in sortedSecurities[-quintileSize:]]
return self.longList + self.shortList
def OnData(self, data: Slice):
if not self.shouldRebalance:
return
self.shouldRebalance = False
targets = [PortfolioTarget(symbol, 1 / len(self.longList)) for symbol in self.longList] + \
[PortfolioTarget(symbol, -1 / len(self.shortList)) for symbol in self.shortList]
self.SetHoldings(targets)
def RebalancePortfolio(self):
self.shouldRebalance = True
def OnSecuritiesChanged(self, changes: SecurityChanges):
for security in changes.AddedSecurities:
security.SetFeeModel(StandardFeeModel())
# Standard fee model to simplify the fee structure
class StandardFeeModel(FeeModel):
def GetOrderFee(self, parameters: OrderFeeParameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))