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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>The Chen-Zimmermann (2020, CFR) Signal Library</title>
<style>
* {
box-sizing: border-box;
margin: 0;
padding: 0;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
height: 100vh;
overflow: hidden;
padding-top: 1rem;
}
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width: 90%;
max-width: 1200px;
margin: 0 auto 0;
border-bottom: 1px solid #000;
}
.nav-bar {
text-align: center;
padding: 1rem 0 0.5rem 0;
background: white;
margin-bottom: 0;
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content: '';
display: block;
width: 90%;
max-width: 1200px;
margin: 0.75rem auto 0;
border-bottom: 1px solid #000;
}
.nav-bar a {
color: #000;
text-decoration: none;
padding: 0 1.5rem;
font-family: Arial, sans-serif;
font-size: 0.9rem;
text-transform: uppercase;
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line-height: 1;
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font-weight: bold;
}
.container {
display: flex;
flex-direction: column;
height: calc(100vh - 75px);
}
.header {
background: #2c3e50;
color: white;
padding: 1rem 1.5rem;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
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font-size: 0.9rem;
min-width: 200px;
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flex-direction: column;
background: #f8f9fa;
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.list-header {
padding: 0.75rem 1rem;
background: #ecf0f1;
border-bottom: 1px solid #ddd;
font-weight: 600;
color: #2c3e50;
}
.signal-list {
flex: 1;
overflow-y: scroll;
}
.signal-item {
padding: 0.75rem 1rem;
border-bottom: 1px solid #e0e0e0;
cursor: pointer;
transition: background 0.2s;
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.signal-item:hover {
background: #e8eef2;
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.signal-item.active {
background: #3498db;
color: white;
}
.signal-item-name {
font-weight: 600;
margin-bottom: 0.25rem;
}
.signal-item-acronym {
font-size: 0.85rem;
color: #555;
margin-bottom: 0.25rem;
font-style: italic;
}
.signal-item.active .signal-item-acronym {
color: #e0e0e0;
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.signal-item-meta {
font-size: 0.85rem;
color: #666;
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.signal-item.active .signal-item-meta {
color: #ecf0f1;
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flex: 1;
overflow-y: scroll;
padding: 2rem;
background: white;
}
.detail-empty {
display: flex;
align-items: center;
justify-content: center;
height: 100%;
color: #999;
font-size: 1.1rem;
}
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padding-bottom: 0.5rem;
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.no-results {
padding: 2rem;
text-align: center;
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}
</style>
</head>
<body>
<div class="top-border"></div>
<nav class="nav-bar">
<a href="https://www.openassetpricing.com/data/">DATA</a>
<a href="https://www.openassetpricing.com/code/">CODE</a>
<a href="https://www.openassetpricing.com/faq/">FAQ</a>
<a href="#" class="active">SIGNAL BROWSER</a>
</nav>
<div class="container">
<div class="header">
<div class="search-bar">
<select id="categoryFilter">
<option value="">All Categories</option>
</select>
<input type="text" id="searchInput" placeholder="Search signals...">
</div>
</div>
<div class="main-content">
<div class="list-panel">
<div class="list-header">
<span id="signalCount">0 signals</span>
</div>
<div class="signal-list" id="signalList"></div>
</div>
<div class="detail-panel" id="detailPanel">
<div class="detail-empty">Select a signal to view details</div>
</div>
</div>
</div>
<script>
const signalsData = [
{
"signalname": "AbnormalAccruals",
"Category": "Predictor",
"AuthorYear": "Xie (2001, AR)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "2_fair",
"GScholarCites202509": "139",
"Description": "Abnormal Accruals",
"Journal": "AR",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "accruals",
"SampleStart": "1971",
"SampleEnd": "1992",
"Acronym2": "AccrAbn",
"EvidenceSummary": "t=8 port sort w/ nonstandard data lag",
"KeyTable": "3",
"TestInOP": "port sort size adjusted nonstandard",
"Sign": "-1.0",
"Return": "0.916666667",
"TStat": "8.43",
"StockWeight": "EW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Define Accruals as net income (ib) minus operating cash flow (oancf), divided by average total assets (at) for years t-1 and t. If oancf is missing, replace operating cash flow with funds from operations (fopt) minus the annual change in total current assets (act) plus the annual change in cash and short-term investments (che) plus the annual change in current liabilities (lct) minus the annual change in debt in current liabilities (dlc). For each year t and 2-digit sic code, regress Accruals on: the inverse of average total assets for year t-1, the change in revenue (sale) from year t-1 to t divided by total assets for t-1, propery plant and equipment (ppegt) divided by total assets for t-1. AbnormalAccrual is the residual from this cross-sectional regression. See code for more details.",
"Notes": "OP is aggressive and lags accounting data by only 3 months (p361) instead of the usual 6. This likely accounts for our relative underperformance.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/AbnormalAccruals.py"
},
{
"signalname": "Accruals",
"Category": "Predictor",
"AuthorYear": "Sloan (1996, AR)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "56",
"Description": "Accruals",
"Journal": "AR",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "accruals",
"SampleStart": "1962",
"SampleEnd": "1991",
"Acronym2": "Accruals",
"EvidenceSummary": "t > 4 in port sort CAPM alpha 12 month holding",
"KeyTable": "6 year t+1",
"TestInOP": "port sort size adjusted",
"Sign": "-1.0",
"Return": "0.866666667",
"TStat": "4.71",
"StockWeight": "EW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "abs(prc)>5",
"Definition": "Annual change in current total assets (act) minus annual change in cash and short-term investements (che) minus annual change in current liabilities (lct) minus annual change in debt in current liabilities (dlc) minus change in income taxes (txp). All divided by average total assets (at) over this year and last year. Exclude if abs(prc) < 5.",
"Notes": "Table 6 year t+1 hedge. Only size adjusted and CAPM adjusted.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/Accruals.py"
},
{
"signalname": "AccrualsBM",
"Category": "Predictor",
"AuthorYear": "Bartov and Kim (2004, RFQA)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "22",
"Description": "Book-to-market and accruals",
"Journal": "RFQA",
"FormCategory": "discrete",
"DataCategory": "Accounting",
"EconomicCategory": "valuation",
"SampleStart": "1980",
"SampleEnd": "1998",
"Acronym2": "AccrualsBM",
"EvidenceSummary": "t=5.5 in long-short",
"KeyTable": "3 mean diff 1-2",
"TestInOP": "LS port",
"Sign": "1.0",
"Return": "0.206",
"TStat": "5.5",
"StockWeight": "EW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Binary variable equal to 1 if stock is in the highest Accrual quintile and the lowest BM quintile, and equal to 0 if stock is in the lowest Accrual quintile and the highest BM quintile. Exclude if book equity (ceq) is negative.",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/AccrualsBM.py"
},
{
"signalname": "Activism1",
"Category": "Predictor",
"AuthorYear": "Cremers and Nair (2005, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "2,036",
"Description": "Takeover vulnerability",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "13F",
"EconomicCategory": "other",
"SampleStart": "1990",
"SampleEnd": "2001",
"Acronym2": "Activism1",
"EvidenceSummary": "t=3.1 in port sort",
"KeyTable": "3A VW BLOCK=4",
"TestInOP": "port sort CAPM alpha",
"Sign": "1.0",
"Return": "0.9025",
"TStat": "3.13",
"StockWeight": "VW",
"LSQuantile": "0.25",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "24 minus Governance Index (G). Set to missing if G is missing, or if not in the highest quartile of institutional ownership (maxinstown\\_perc), or if dual share class.",
"Notes": "works a bit better EW in Tab 3",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/Activism1.py"
},
{
"signalname": "AM",
"Category": "Predictor",
"AuthorYear": "Fama and French (1992, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "29,625",
"Description": "Total assets to market",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "valuation",
"SampleStart": "1963",
"SampleEnd": "1990",
"Acronym2": "AM",
"EvidenceSummary": "t=5.7 in univar reg",
"KeyTable": "3 Ln(A/ME)",
"TestInOP": "univariate reg",
"Sign": "1.0",
"Return": "",
"TStat": "5.69",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Total assets (at) divided by market value of equity.",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/AM.py"
},
{
"signalname": "AnalystRevision",
"Category": "Predictor",
"AuthorYear": "Hawkins, Chamberlin, Daniel (1984, FAJ)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "2_fair",
"GScholarCites202509": "117",
"Description": "EPS forecast revision",
"Journal": "FAJ",
"FormCategory": "continuous",
"DataCategory": "Analyst",
"EconomicCategory": "earnings forecast",
"SampleStart": "1975",
"SampleEnd": "1980",
"Acronym2": "AnalystRevision",
"EvidenceSummary": "t=3.2 in long only CAPM alpha",
"KeyTable": "10 mean",
"TestInOP": "long only port CAPM alpha",
"Sign": "1.0",
"Return": "0.460583333",
"TStat": "3.169151376",
"StockWeight": "EW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "12.0",
"Filter": "",
"Definition": "keep fpi == \"1\", last obs each month. Signal is meanest / last month's meanest.",
"Notes": "OP only longs top 20 stocks according to signal. We were more flexible. Sample is very short but the results seem robust.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/AnalystRevision.py"
},
{
"signalname": "AnnouncementReturn",
"Category": "Predictor",
"AuthorYear": "Chan, Jegadeesh and Lakonishok (1996, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "3,314",
"Description": "Earnings announcement return",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Price",
"EconomicCategory": "earnings event",
"SampleStart": "1977",
"SampleEnd": "1992",
"Acronym2": "AnnounRet",
"EvidenceSummary": "t=9.3 in regression",
"KeyTable": "7 ABR",
"TestInOP": "mv reg",
"Sign": "1.0",
"Return": "",
"TStat": "9.25",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "12.0",
"Filter": "",
"Definition": "Get announcement date for quarterly earnings from IBES (fpi = 6). AnnouncementReturn is the sum of (ret - mktrf + rf) from one day before an earnings announcement to 2 days after the announcement.",
"Notes": "Table 4 has port sort but no t-stats. Tab 7 has huge t-stats in regressions",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/AnnouncementReturn.py"
},
{
"signalname": "AssetGrowth",
"Category": "Predictor",
"AuthorYear": "Cooper, Gulen and Schill (2008, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "2,226",
"Description": "Asset growth",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "investment",
"SampleStart": "1968",
"SampleEnd": "2003",
"Acronym2": "InvestAG",
"EvidenceSummary": "t=8.5 in port sort",
"KeyTable": "2B.1 year 1",
"TestInOP": "port sort",
"Sign": "-1.0",
"Return": "1.73",
"TStat": "8.45",
"StockWeight": "EW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Annual growth rate of total assets (at)",
"Notes": "Also works VW (t=5, Tab2.B.2)",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/AssetGrowth.py"
},
{
"signalname": "BetaLiquidityPS",
"Category": "Predictor",
"AuthorYear": "Pastor and Stambaugh (2003, JPE)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "7,459",
"Description": "Pastor-Stambaugh liquidity beta",
"Journal": "JPE",
"FormCategory": "continuous",
"DataCategory": "Price",
"EconomicCategory": "liquidity",
"SampleStart": "1968",
"SampleEnd": "1999",
"Acronym2": "BetaLiquidityPS",
"EvidenceSummary": "t=2.54 in VW port sort CAPM alpha",
"KeyTable": "4A CAPM 10-1",
"TestInOP": "port sort CAPM alpha",
"Sign": "1.0",
"Return": "0.533333333",
"TStat": "2.54",
"StockWeight": "VW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "12.0",
"Filter": "abs(prc)>5",
"Definition": "Monthly excess return (ret -rf) regressed on innovations in liquidity from Pastor's website (\\url{https://faculty.chicagobooth.edu/lubos.pastor/research/liq_data_1962_2018.txt}). Use 60 month rolling window regression, and require at least 36 non-missing observations.",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/BetaLiquidityPS.py"
},
{
"signalname": "BetaTailRisk",
"Category": "Predictor",
"AuthorYear": "Kelly and Jiang (2014, RFS)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "1,020",
"Description": "Tail risk beta",
"Journal": "RFS",
"FormCategory": "continuous",
"DataCategory": "Price",
"EconomicCategory": "risk",
"SampleStart": "1963",
"SampleEnd": "2010",
"Acronym2": "BetaTailRisk",
"EvidenceSummary": "Tab4A t-stat 2.48",
"KeyTable": "4A EW",
"TestInOP": "port sort",
"Sign": "1.0",
"Return": "0.33",
"TStat": "2.48",
"StockWeight": "EW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "12.0",
"Filter": "abs(prc)>5",
"Definition": "Each month, compute the 5th percentile over daily returns over all firms. For all daily return observations with return below that 5th percentile, compute the average of (log(ret/5th percentile of cross-sectional return distribution). Call that average tailEX. BetaTailRisk is the coefficient of a 120-month rolling regression of a firm's stock return on tailEX. Exclude if price less than 5 or share code greater than 11.",
"Notes": "Also works VW or monthly",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/BetaTailRisk.py"
},
{
"signalname": "betaVIX",
"Category": "Predictor",
"AuthorYear": "Ang et al. (2006, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "6,323",
"Description": "Systematic volatility",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Price",
"EconomicCategory": "volatility",
"SampleStart": "1986",
"SampleEnd": "2000",
"Acronym2": "betaVIX",
"EvidenceSummary": "t=3.9 in port sort",
"KeyTable": "1A",
"TestInOP": "port sort",
"Sign": "-1.0",
"Return": "1.04",
"TStat": "3.9",
"StockWeight": "VW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Coefficient on daily change in the VIX of a 1-month rolling window regression of daily stock excess returns on market return and the daily change in the CBOE S&P 100 volatility index (downloaded from FRED). Require at least 15 non-missing observations.",
"Notes": "Tab I has port sorts",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/betaVIX.py"
},
{
"signalname": "BM",
"Category": "Predictor",
"AuthorYear": "Stattman (1980, Other)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "1,555",
"Description": "Book to market, original (Stattman 1980)",
"Journal": "Other",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "valuation",
"SampleStart": "1962",
"SampleEnd": "1976",
"Acronym2": "BM",
"EvidenceSummary": "risk-adjusted portfolio independence test",
"KeyTable": "3",
"TestInOP": "risk-adjusted portfolio independence test",
"Sign": "1.0",
"Return": "",
"TStat": "",
"StockWeight": "EW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "exchcd%in%c(1,2)",
"Definition": "Log of tangible book equity (ceqt) over market equity matched at FYE",
"Notes": "OP actually forms portfolios at the end of March based on December FYE data and drops stocks with non-Dec FYE. But for consistency with FF1992, we use June as the port form month (the irony is noted). OP also drops certain SIC codes, which we should implement at the portfolio stage, in time. We previously were citing Rosenberg, Reid, Lanstein 1985, but Stattman 1980 should get more credit. RRL does not describe its methods in much detail too.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/BM.py"
},
{
"signalname": "BMdec",
"Category": "Predictor",
"AuthorYear": "Fama and French (1992, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "5,940",
"Description": "Book to market using December ME",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "valuation",
"SampleStart": "1963",
"SampleEnd": "1990",
"Acronym2": "BMdec",
"EvidenceSummary": "t=5.71 in univariate reg",
"KeyTable": "3 Ln(BE/ME)",
"TestInOP": "univariate reg",
"Sign": "1.0",
"Return": "0.5",
"TStat": "5.71",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "BM using most recent December value of market equity.",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/BMdec.py"
},
{
"signalname": "BookLeverage",
"Category": "Predictor",
"AuthorYear": "Fama and French (1992, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "29,625",
"Description": "Book leverage (annual)",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "leverage",
"SampleStart": "1963",
"SampleEnd": "1990",
"Acronym2": "BookLev",
"EvidenceSummary": "t=5.3 in mv reg",
"KeyTable": "3 Ln(A/BE)",
"TestInOP": "mv reg",
"Sign": "-1.0",
"Return": "",
"TStat": "5.34",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Total assets (at) divided by book value of equity plus deferred taxes (txditc) and preferred stock. Equity is shareholder equity (seq) if available, or book equity (ceq) plus preferred stock (pstk, if missing pstkrv, if missing pstkl), or total assets minus total liabilities (lt).",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/BookLeverage.py"
},
{
"signalname": "BPEBM",
"Category": "Predictor",
"AuthorYear": "Penman, Richardson and Tuna (2007, JAR)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "550",
"Description": "Leverage component of BM",
"Journal": "JAR",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "leverage",
"SampleStart": "1963",
"SampleEnd": "2001",
"Acronym2": "BMlev",
"EvidenceSummary": "t=4.1 in univariate reg",
"KeyTable": "1D",
"TestInOP": "univariate reg",
"Sign": "-1.0",
"Return": "",
"TStat": "4.1",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "abs(prc)>5",
"Definition": "BP - EBM, where BP = (ceq + tstkp - dvpa)/(shrout*abs(prc)), and EBM is defined above. Exclude if price less than 5.",
"Notes": "B/P-NOA/P^NOA in OP. Table 1D shows strong port sort. Tab3A shows t-stat = 4.1 in uni reg. OP drops extreme obs but we don't.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/BPEBM.py"
},
{
"signalname": "Cash",
"Category": "Predictor",
"AuthorYear": "Palazzo (2012, JFE)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "500",
"Description": "Cash to assets",
"Journal": "JFE",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "asset composition",
"SampleStart": "1972",
"SampleEnd": "2009",
"Acronym2": "Cash",
"EvidenceSummary": "t=2.14 in port sort but strong with adjustments",
"KeyTable": "4",
"TestInOP": "port sort",
"Sign": "1.0",
"Return": "0.69",
"TStat": "2.14",
"StockWeight": "EW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Ratio of quarterly cash and short-term investments (cheq) and total assets (atq).",
"Notes": "Table 4 has long-short returns with t-stat of 2.14 for raw EW. Much stronger after factor adjustments. Other tables show double sorts, alphas, Table 4, Panel A, column DeltaCH",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/Cash.py"
},
{
"signalname": "CashProd",
"Category": "Predictor",
"AuthorYear": "Chandrashekar and Rao (2009, WP)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "2",
"Description": "Cash Productivity",
"Journal": "WP",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "profitability alt",
"SampleStart": "1963",
"SampleEnd": "2003",
"Acronym2": "CashProd",
"EvidenceSummary": "t=3.6 in regression",
"KeyTable": "4A \\eta",
"TestInOP": "mv reg",
"Sign": "-1.0",
"Return": "",
"TStat": "3.6",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Calculate market value of equity (mve_c) as absolute price (prc) times number of shares outstanding (shrout). Cash productivity is equal to the difference between mve_c and total assets (at) divided by cash and short-term investments (che).",
"Notes": "Stats are from WP version.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/CashProd.py"
},
{
"signalname": "CBOperProf",
"Category": "Predictor",
"AuthorYear": "Ball et al. (2016, JFE)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "533",
"Description": "Cash-based operating profitability",
"Journal": "JFE",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "profitability",
"SampleStart": "1963",
"SampleEnd": "2014",
"Acronym2": "ProfCash",
"EvidenceSummary": "t=3.2 in port sort",
"KeyTable": "4A Cash-based 10-1",
"TestInOP": "port sort",
"Sign": "1.0",
"Return": "0.47",
"TStat": "3.17",
"StockWeight": "VW",
"LSQuantile": "0.1",
"QuantileFilter": "NYSE",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Revenue (revt) minus cost (cogs) - (administrative expenses (xsga) - R&D expenses (xrd)) minus annual change in receivables (rect), annual change in investment (invt) and annual change in prepaid expenses, plus annual change in current deferred revenue (drc), long-term deferred revenue (drlt), accounts payable (ap) and accrued expenses (xacc), all divided by total assets (at) in year t. Replace all variables in the numerator with 0 if they are missing. Exclude if share code is greater 11, market value of equity, BM or total assets are missing, or if SIC code between 6000 and 6999.",
"Notes": "This is operating prof with working cap and R&D adjustments.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/CBOperProf.py"
},
{
"signalname": "CF",
"Category": "Predictor",
"AuthorYear": "Lakonishok, Shleifer, Vishny (1994, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "7,650",
"Description": "Cash flow to market",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "valuation",
"SampleStart": "1968",
"SampleEnd": "1990",
"Acronym2": "CF2Price",
"EvidenceSummary": "t=3.4 in port sort",
"KeyTable": "6 panel 1",
"TestInOP": "port sort",
"Sign": "1.0",
"Return": "0.658333333",
"TStat": "3.379",
"StockWeight": "EW",
"LSQuantile": "0.1",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "exchcd%in%c(1,2)",
"Definition": "Net income (ib) plus depreciation (dp) divided by market equity. Exclude NASDAQ stocks.",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/CF.py"
},
{
"signalname": "cfp",
"Category": "Predictor",
"AuthorYear": "Desai, Rajgopal, Venkatachalam (2004, AR)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "649",
"Description": "Operating Cash flows to price",
"Journal": "AR",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "valuation",
"SampleStart": "1973",
"SampleEnd": "1997",
"Acronym2": "CFOper2Price",
"EvidenceSummary": "t=2.77 in port sort",
"KeyTable": "2E R1",
"TestInOP": "port sort",
"Sign": "1.0",
"Return": "1.275",
"TStat": "2.77",
"StockWeight": "EW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",
"Definition": "Operating cash-flow (oancf) divided by market value of equity. If operating cash-flow is missing, replace by difference betwee net income (ib) and level of accruals, where the latter is the annual change in current assets (act) minus the annual change in cash and short-term investments (che), minus the annual change in current liabilities (lct) plus the annual change in debt in current liabilities (dlc) plus the annual change in payable income taxes (txp) plus depreciation (dp).",
"Notes": "",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/cfp.py"
},
{
"signalname": "ChangeInRecommendation",
"Category": "Predictor",
"AuthorYear": "Jegadeesh et al. (2004, JF)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "4_lack_data",
"GScholarCites202509": "1,466",
"Description": "Change in recommendation",
"Journal": "JF",
"FormCategory": "continuous",
"DataCategory": "Analyst",
"EconomicCategory": "recommendation",
"SampleStart": "1985",
"SampleEnd": "1998",
"Acronym2": "ChRecomm",
"EvidenceSummary": "p<0.01 in LS port, but we lack the data",
"KeyTable": "3C",
"TestInOP": "LS nonstandard data",
"Sign": "1.0",
"Return": "0.225",
"TStat": "",
"StockWeight": "EW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "1.0",
"StartMonth": "12.0",
"Filter": "",
"Definition": "keep last ireccd each month, then average across analysts for each firm-month. Define opscore as 6-ireccd. Signal is opscore - last month's opscore.",
"Notes": "OP sample is 1985-1998 using Zack's, but our IBES recommendations only begins in 1993. OP is binary, but we follow MP. Even though sample is super short for us, it seems to work, and is even mostly monotonic.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/ChangeInRecommendation.py"
},
{
"signalname": "ChAssetTurnover",
"Category": "Predictor",
"AuthorYear": "Soliman (2008, AR)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "919",
"Description": "Change in Asset Turnover",
"Journal": "AR",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "sales growth",
"SampleStart": "1984",
"SampleEnd": "2002",
"Acronym2": "ATurnGr",
"EvidenceSummary": "t=5 in mv reg",
"KeyTable": "7 Model 1 DeltaATO",
"TestInOP": "mv reg",
"Sign": "1.0",
"Return": "",
"TStat": "5.12",
"StockWeight": "EW",
"LSQuantile": "",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "abs(prc)>5",
"Definition": "Annual change in AssetTurnover (defined above). Exclude if price less than 5.",
"Notes": "Tab 7 Delta ATO, t-stat 2.5 with controls, no sorts.",
"CodeLink": "https://github.com/OpenSourceAP/CrossSection/blob/master/Signals/pyCode/Predictors/ChAssetTurnover.py"
},
{
"signalname": "ChEQ",
"Category": "Predictor",
"AuthorYear": "Lockwood and Prombutr (2010, JFR)",
"Predictability": "Clearly Significant (Predictor)",
"Quality": "1_good",
"GScholarCites202509": "111",
"Description": "Growth in book equity",
"Journal": "JFR",
"FormCategory": "continuous",
"DataCategory": "Accounting",
"EconomicCategory": "investment",
"SampleStart": "1964",
"SampleEnd": "2007",
"Acronym2": "BEgrowth",
"EvidenceSummary": "t=5.38 in EW port sort",
"KeyTable": "4A SUSG",
"TestInOP": "port sort",
"Sign": "-1.0",
"Return": "0.8",
"TStat": "5.38",
"StockWeight": "EW",
"LSQuantile": "0.2",
"QuantileFilter": "",
"PortfolioPeriod": "12.0",
"StartMonth": "6.0",
"Filter": "",