Stocks of Underperforming Funds and Idiosyncratic Volatility
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Stocks of Underperforming Funds and Idiosyncratic Volatility 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: Stocks of Underperforming Funds and Idiosyncratic Volatility
# 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
Mutual Fund Risk Shifting and Risk Anomalies
Xiao Han; Nikolai Roussanov; Hongxun Ruan
- City, University of London
- ?City University London - Bayes Business School
- University of Pennsylvania
- National Bureau of Economic Research
- ?National Bureau of Economic Research (NBER)
- ?University of Pennsylvania - The Wharton School
- Peking University
- ?Guanghua School of Management, Peking University
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3931449
Strategy in a nutshell
The investment universe includes all AMEX, NYSE, and NASDAQ stocks with share codes 10 and 11. Monthly stock returns are obtained from the CRSP database. Stocks priced below $5 are excluded.
The first variable of interest is stock-level fund performance, defined as the weighted average of year-to-date excess returns (relative to a benchmark) of funds holding the stock. Due to a change in Morningstar’s rating methodology, the S&P 500 index is used as the benchmark before June 2002, and the Russell index is used after June 2002.
The second variable is idiosyncratic volatility (IVOL), calculated as the standard deviation of residuals from regressing the most recent month’s daily returns on the Fama-French three factors.
Each month, stocks are first sorted into quintiles based on fund performance. Within the lowest fund-performance quintile, stocks are then sorted into deciles based on IVOL. The strategy goes long the bottom decile (stocks with highest IVOL) and short the top decile (stocks with lowest IVOL). Portfolios are value-weighted and rebalanced monthly.
Quantpedia note: It may be more effective to select the highest fund-performance quintile instead of the lowest.
Economic rationale
The strategy exploits the behavioral patterns of fund managers. Funds with poor ratings often experience declining performance, prompting managers to pursue high-risk stocks in an attempt to recover. Stocks with high idiosyncratic volatility are attractive to these underperforming funds because their prices fluctuate more than benchmark returns on average. The increased demand from lagging funds can lead to overpricing of high-IVOL stocks, generating a more pronounced idiosyncratic volatility anomaly.