Asset Growth Anomaly Strategy
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The Asset Growth Effect in Stock Returns
Michael J. Cooper; Huseyin Gulen; Michael J. Schill
- University of Utah
- ?University of Utah - David Eccles School of Business
- ?Purdue University - Krannert School of Management
- University of Virginia
- ?University of Virginia - Darden School of Business
Strategy in a nutshell
The investment universe consists of all non-financial U.S. stocks listed on NYSE, AMEX, and NASDAQ. Stocks are then sorted each year at the end of June into ten equal groups based on the percentage change in total assets for the previous year. The investor goes long decile with low asset growth firms and short decile with high asset growth firms. The portfolio is weighted equally and rebalanced every year.
Economic rationale
A variety of papers suggest that the return premium achieved by low asset growth stocks is consistent with compensation for risk (for example, Gomes, Kogan, and Zhang, 2003; and Li, Livdan, Zhang, 2008). Firms maintain a mix of growth options and assets in place, but growth options are inherently more risky than assets in place. As firms exercise growth options, the asset mix of the firm becomes less risky as assets in place displace growth options. The systematic reduction in risk following the exercise of growth options induces a negative correlation between investment and subsequent returns. However, empirical findings are all also consistent with systematic mispricing across asset growth as a firm characteristic. Therefore, the authors are unable to recognize whether the return premium for low growth stocks is due to systematic variation in risk or the return reversal caused by systematic overcapitalization of high growth stocks and undercapitalization of low growth stocks. Building on that, another past research has concluded that the asset growth effect is not fully explained by variations in risk.
However, there is a possibility that the effect is at least partially due to the systematic market mispricing of growing businesses. That source of mispricing could be caused by the extrapolation of past gains to growth for high asset growth companies. A good insight on the reasons for functionality could be found in the work of Kam and Wei: “Asset Growth Reversals and Investment Anomalies“. Quoting the authors: “We simultaneously test the prominent rational and behavioral explanations of the negative relations between corporate asset growth or investments and subsequent stock returns by extensively examining the effects of realized and predicted subsequent growth on the relations. We find: (i) returns on low growth firms with low subsequent growth are not higher than those on high growth firms with subsequent high growth; (ii) high growth firms that have subsequent high growth do not underperform, and the return spreads between low and high growth firms are lower when high growth firms have higher subsequent growth; (iii) the relations between growth and returns are weak or even in opposite direction when subsequent growth tends not to reverse but are significantly negative when subsequent growth tend to reverse and are stronger when the reversals are more extreme. Our findings are consistent with the hypothesis based on extrapolation and growth-based style investing but less consistent with the other explanations.”
Backtest performance
Full Python code
from AlgoLib import *
import numpy as np
#endregion
class AssetGrowthEffect(XXX):
def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100_000)
self.UniverseSettings.Leverage = 5
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.0
# Latest assets data.
self.total_assets: dict[Symbol, float] = {}
self.long_symbols: list[Symbol] = []
self.short_symbols: list[Symbol] = []
self.selection_flag: bool = False
# Filter Parameters
self.exchange_codes: List[str] = ['NYS', 'NAS', 'ASE']
self.quantile: int = 10
self.rebalancing_month: int = 6
# self.fin_sector_code: int = 103
self.fundamental_count:int = 3000
self.fundamental_sorting_key = lambda x: x.MarketCap
self.exchange: Symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.Schedule.On(self.DateRules.MonthEnd(self.exchange),
self.TimeRules.AfterMarketOpen(self.exchange),
self.Selection)
def FundamentalFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
if not self.selection_flag:
return Universe.Unchanged
filtered: List[Fundamental] = [f for f in fundamental if f.HasFundamentalData
and f.SecurityReference.ExchangeId in self.exchange_codes
# and f.AssetClassification.MorningstarSectorCode != self.fin_sector_code
and not np.isnan(f.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths)
and f.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths > 0]
if len(filtered) > self.fundamental_count:
filtered = [x for x in sorted(filtered, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
assets_growth: dict[Symbol, float] = {}
for security in filtered:
symbol: Symbol = security.Symbol
if symbol not in self.total_assets:
self.total_assets[symbol] = None
current_assets: float = security.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths
# There is not previous assets data.
if not self.total_assets[symbol]:
self.total_assets[symbol] = current_assets
continue
# Assets growth calc.
assets_growth[symbol] = (current_assets - self.total_assets[symbol]) / self.total_assets[symbol]
# Update data.
self.total_assets[symbol] = current_assets
# Asset growth sorting.
if len(assets_growth) >= self.quantile:
sorted_by_assets_growth: dict[Symbol, float] = sorted(assets_growth.items(),
key = lambda x: x[1],
reverse = True)
quantile: int = int(len(sorted_by_assets_growth) / self.quantile)
self.long_symbols = [x[0] for x in sorted_by_assets_growth[-quantile:]]
self.short_symbols = [x[0] for x in sorted_by_assets_growth[:quantile]]
return self.long_symbols + self.short_symbols
def OnData(self, slice: Slice) -> None:
if not self.selection_flag:
return
self.selection_flag = False
# Trade execution.
targets: List[PortfolioTarget] = []
for i, portfolio in enumerate([self.long_symbols, self.short_symbols]):
for symbol in portfolio:
if slice.ContainsKey(symbol) and slice[symbol] is not None:
targets.append(PortfolioTarget(
symbol, ((-1) ** i) / len(portfolio)))
self.SetHoldings(targets, True)
self.long_symbols.clear()
self.short_symbols.clear()
def Selection(self) -> None:
if self.Time.month == self.rebalancing_month:
self.selection_flag = True
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee:
fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))