Equity · United States · stocks · Yearly Free

Balance Sheet Accruals Strategy

Academic paper

The Persistence of the Accruals Anomaly

AuthorsBaruch Lev; Doron Nissim

Institute

New York University
New York University - Stern School of Business
Columbia University
Columbia University - Columbia Business School

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=546108

Screenshot from the original paper

Strategy in a nutshell

The investment universe includes stocks from NYSE, AMEX, and NASDAQ. Accruals, a non-cash earnings component, are calculated using the formula: BS_ACC = (∆CA - ∆Cash) - (∆CL - ∆STD - ∆ITP) - Dep, with ∆CA representing the annual change in current assets, ∆Cash the change in cash equivalents, ∆CL the change in current liabilities, ∆STD the change in short-term debt, ∆ITP the change in income taxes payable, and Dep the depreciation expense. Stocks are ranked into deciles based on accruals, with investments in the lowest and shorts in the highest. Portfolios are rebalanced annually in May, post-earnings publication.

Economic rationale

The accrual anomaly, attributed to the earnings fixation hypothesis, suggests investors overly focus on earnings, neglecting to separately evaluate cash-flow and accrual elements. This oversight leads to misplaced optimism for companies with high accruals and undue pessimism for those with low accruals, causing valuation errors: high accrual companies become overvalued and underperform, while low accrual ones are undervalued but yield high returns. Recent studies, including Detzel, Schabel, and Strauss's "There are Two Very Different Accruals Anomalies," highlight the distinction between investment-related and non-investment accruals. They found that investment accruals better predict returns, are influenced by market sentiment, and have a risk-associated premium, unlike non-investment accruals. This differentiation clarifies previous mixed findings, indicating two separate phenomena within the accrual anomaly: a risk-based investment accruals premium and mispricing of non-investment accruals.

Entry & exit

Entry

On each month, compute 252-day total return on SPY, BIL. Hold only names with positive absolute momentum, equal-weight.

Exit

Sell a name when its trailing 252-day return turns non-positive. If none remain positive, sit in cash.

Market data required

Needs at least 252 adjusted daily closes on SPY, BIL before the first signal is ready. ETF columns print on exchange sessions only. The lab loads Yahoo Finance via backtest.data.load_daily_prices.

Symbols

SPY, BIL

Frequency

Yearly · Daily adjusted close; signals rebalance on the month change (last available session)

History needed

≥ 252 trading days per symbol (about 12 months), plus a few extra bars so the first signal can fire.

Fields

Adjusted close (Yahoo auto_adjust)
Session date index

Credentials

None for paper and the onsite lab
Broker or exchange API keys only for live orders

APIs & providers

Quant Buffet Data API

The lab calls load_daily_prices for the ASSETS panel of adjusted closes, then caches CSV so backtests replay the same series.

Provider: backtest.data.load_daily_prices
Auth: None for paper / sandbox backtests
Endpoints: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /en/docs/data-api

Yahoo Finance via yfinance

US equities and ETFs, adjusted OHLCV, free for research (rate limits apply).

Provider: yfinance → Yahoo Finance
Auth: None for public history
Endpoints: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Balance Sheet Accruals 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 →

IDE · 50 lines

"""
Quant Buffet interactive lab — accrual-anomaly
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
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_metrics

ASSETS = ['SPY', 'BIL']
LOOKBACK = 252
CASH = 'BIL'

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    if CASH not in cols and CASH in prices.columns:
        cols = cols + [CASH]
    risky = [c for c in cols if c != CASH]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        scores = {
            s: float(rets.at[dt, s])
            for s in risky
            if s in rets.columns and pd.notna(rets.at[dt, s])
        }
        if not scores:
            engine.set_target_weights(dt, {CASH: 1.0} if CASH in cols else {})
            return
        best = max(scores, key=scores.get)
        if scores[best] > 0:
            engine.set_target_weights(dt, {best: 1.0})
        elif CASH in cols:
            engine.set_target_weights(dt, {CASH: 1.0})
        else:
            engine.set_target_weights(dt, {})

    ready = rets.dropna(how="all").index.min() if rets.notna().any().any() else None
    return on_day, ready

"""
Quant Buffet interactive lab — accrual-anomaly
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
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_metrics

ASSETS = ['SPY', 'BIL']
LOOKBACK = 252
CASH = 'BIL'

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    if CASH not in cols and CASH in prices.columns:
        cols = cols + [CASH]
    risky = [c for c in cols if c != CASH]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        scores = {
            s: float(rets.at[dt, s])
            for s in risky
            if s in rets.columns and pd.notna(rets.at[dt, s])
        }
        if not scores:
            engine.set_target_weights(dt, {CASH: 1.0} if CASH in cols else {})
            return
        best = max(scores, key=scores.get)
        if scores[best] > 0:
            engine.set_target_weights(dt, {best: 1.0})
        elif CASH in cols:
            engine.set_target_weights(dt, {CASH: 1.0})
        else:
            engine.set_target_weights(dt, {})

ready = rets.dropna(how="all").index.min() if rets.notna().any().any() else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

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_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

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, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

7.89%

Sharpe

0.63

Max DD

-33.72%

Vol

13.60%

Sortino

0.93

Beta

0.51

Up days

49%

Run the backtest to populate charts.

Order records

Run the backtest to load every order fill alongside 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.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Absolute momentumAssets: SPY, BIL

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Balance Sheet Accruals 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", "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.