Asset Growth Anomaly Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Asset Growth Anomaly Strategy in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Asset Growth Anomaly Strategy
# Detected pattern: Absolute momentum
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Long assets with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
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
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1335524
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.”