Changes in Ownership Breadth Predict Performance of Equity Factors
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Changes in Ownership Breadth Predict Performance of Equity Factors 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: Changes in Ownership Breadth Predict Performance of Equity Factors
# 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
Changes in Ownership Breadth and Anomaly Returns
Yangru Wu; Weike Xu
- Rutgers, The State University of New Jersey
- NLRutgers Sexual and Reproductive Health and Rights
- ?Rutgers University, Newark - School of Business - Department of Finance & Economics
- Clemson University
- ?Clemson University - Department of Finance
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2988428
Strategy in a nutshell
The strategy targets all NYSE, AMEX, and NASDAQ common stocks, excluding firms with share prices below $1 and financial sector firms. Using data from CRSP, Compustat, and Thomson Reuters 13f filings, stocks are sorted into terciles based on quarterly changes in institutional ownership breadth. Within each tercile, stocks are further sorted into quintiles according to 11 anomaly variables: asset growth, failure probability, gross profitability, investment-asset ratio, long-term equity issuance, momentum, net operating assets, net payout, net stock issuance, operating accruals, o-score, and return on assets. The long leg consists of stocks with the highest anomaly scores in the top ownership breadth change tercile, while the short leg consists of stocks with the lowest anomaly scores in the bottom ownership breadth change tercile. Portfolios are value-weighted and rebalanced quarterly, with a two-month lag applied to ensure tradability.
Economic rationale
The strategy exploits signals from informed institutional investors. Changes in ownership breadth indicate entries and exits of well-informed investors, predicting future stock returns. Evidence supports the informed trading hypothesis, as controlling for future earnings surprises removes the predictive power of breadth changes. Short-selling constraints explain only part of the negative alpha for short positions and are otherwise insignificant.