Research libraries and data sources used by professionals and academics, with what each is best for. Several are run by fund managers; we say where. Listed, not scored. We score videos (VettedVids score), books (Book Rating) and channels (Channel Rating).
Kenneth R. French Data Library
Kenneth R. French, Tuck School of Business at Dartmouth
Best for: Free downloadable factor returns (Fama-French 3- and 5-factor, momentum and many sorted portfolios) for the US and international regions, with construction notes.
Who writes it: Maintained by the co-author of the Fama-French factor models; the standard data source in academic factor research.
Caution: Raw data with little explanation; returns are gross of trading costs and taxes and are not investable as shown.
Description of Fama/French 5 factors (2x3) · Detail for monthly momentum factor
AQR Research and Data Sets
AQR Capital Management
Best for: Journal articles, working papers and free data sets on value, momentum, defensive/low-beta, quality, trend following and factor timing, many by authors of the papers on this page.
Who writes it: Authors include academics who publish in peer-reviewed finance journals (e.g. The Journal of Finance, Journal of Financial Economics).
Caution: Run by a fund manager that sells factor, trend-following and alternative strategies; research is high quality but comes from an interested party.
Contrarian Factor Timing is Deceptively Difficult · A Century of Evidence on Trend-Following Investing · Data Sets
Alpha Architect blog
Alpha Architect
Best for: Plain-language summaries of academic factor papers, including the replication debate, factor momentum and value/momentum research.
Who writes it: Firm founded by Wesley Gray (presented as 'Dr. Wes Gray'); posts usually cite and link the underlying papers.
Caution: Run by a fund manager: the firm offers ETFs, separately managed accounts and pooled vehicles built on the strategies it writes about.
Is There a Replication Crisis in Finance? · More on the Factor Investing Replication Debate · Momentum Everywhere, Including in Factors
RAFI research archive (formerly Research Affiliates)
RAFI Indices (VettaFi, a TMX company); originally published by Research Affiliates
Best for: Research on factor valuations, smart beta pitfalls and crowding, a sceptical counterweight to factor marketing.
Who writes it: Articles by Rob Arnott and colleagues, with authors and dates shown on each article.
Caution: Run by an index provider that licenses fundamental-index strategies; Research Affiliates' advisory business is now Syzygy Asset Management, and old researchaffiliates.com links may redirect.
How Can 'Smart Beta' Go Horribly Wrong? · Alice's Adventures in Factorland: Three Blunders That Plague Factor Investing
SSRN Financial Economics Network
SSRN (Elsevier)
Best for: Free preprints of most of the finance papers on this page, often years before journal publication.
Who writes it: Main preprint server for academic finance.
Caution: Working papers are not peer reviewed and may change or never be published; check for a journal version.
Financial Economics Network
Robeco quantitative research
Robeco
Best for: Research articles on factor models, low-volatility and the 'factor zoo', including critiques of the Fama-French five-factor model.
Who writes it: In-house quantitative research team; the site lists the team's own research papers.
Caution: Run by a fund manager that sells quantitative and factor funds.
Fama-French 5-factor model: five major concerns · Honey, we shrunk the factor zoo · Our groundbreaking papers
Newfound Research library (Flirting with Models)
Newfound Research
Best for: Detailed quantitative notes on trend following, managed futures, tail hedging, rebalancing and 'timing luck' in factor portfolios.
Who writes it: Written by Newfound's research team under Chief Investment Officer Corey Hoffstein.
Caution: Run by a fund manager; notes often relate to strategies the firm and its partners manage.
What Is Managed Futures? · Tail Hedging · Rebalance Timing Luck: The (Dumb) Luck of Smart Beta
CFA Institute Research Foundation
CFA Institute Research Foundation
Best for: Free monographs and literature reviews on expected returns, factor investing and asset allocation.
Who writes it: Non-profit research arm of CFA Institute.
Caution: Written for investment professionals; authors often work for asset managers.
Expected Returns on Major Asset Classes (Ilmanen, 2012) · Factor Investing and Asset Allocation: A Business Cycle Perspective
Rational Reminder
PWL Capital (Canada)
Best for: Long-form podcast interviews with factor researchers, with episode pages and show notes.
Who writes it: Guests include Eugene Fama, Kenneth French and Andrew Chen.
Caution: Hosts are portfolio managers at PWL Capital, a Canadian firm that builds client portfolios; Canada-centric on tax and products.
Episode 200: Prof. Eugene Fama · Episode 100: Prof. Kenneth French: Expect the Unexpected · Episode 316: Andrew Chen on cross-sectional asset pricing
What the research found, for and against: the factor evidence, how much of it survives replication and publication, trend following, private markets, fees and hedging (37 sources). Many core papers come from researchers at firms that sell factor funds; we say where. General information, not financial advice.
Understand the factor evidenceEvidence: Cohort or observational study
Size and value: the original Fama-French evidence
Fama and French (1992) found that firm size and book-to-market equity together captured much of the variation in average US stock returns, while the link between market beta and average return was flat. Their 1993 paper turned this into a factor model: market, size and value factors for stocks plus maturity and default factors for bonds, which together appeared to explain average returns on stocks and bonds.
Limits: US stocks, roughly 1963 to 1990 for the main tests; in-sample results that the later papers on this page test for durability.
Fama & French 1992, The Journal of Finance · Fama & French 1993, Journal of Financial Economics
Understand the factor evidenceEvidence: Cohort or observational study
The five-factor model adds profitability and investment
A five-factor model capturing size, value, profitability and investment patterns described average US stock returns better than the three-factor model. Once profitability and investment were added, the value factor became redundant in the authors' sample.
Limits: US stocks from 1963 to about 2013; the authors report the model fails for small stocks that invest heavily despite low profitability, and the 'value is redundant' result is sample-specific.
Fama & French 2015, Journal of Financial Economics
Understand the factor evidenceEvidence: Cohort or observational study
Momentum: past winners kept winning for 3 to 12 months
Buying stocks with strong past returns and selling those with poor past returns produced significant positive returns over 3- to 12-month holding periods, not explained by systematic risk. Part of the excess return reversed over the following two years.
Limits: US stocks, 1965 to 1989; gross of trading costs, and momentum strategies trade heavily.
Jegadeesh & Titman 1993, The Journal of Finance
Understand the factor evidenceEvidence: Cohort or observational study
Value and momentum across eight markets and asset classes
The authors found value and momentum return premia in eight markets and asset classes, including stocks in several regions, country indices, bonds, currencies and commodities. Value and momentum were negatively correlated with each other, and each was correlated across asset classes, which the authors read as evidence of common global risks.
Limits: Sample periods differ by market and end around 2011; authors were affiliated with AQR, an investment manager that runs value and momentum strategies.
Asness, Moskowitz & Pedersen 2013, The Journal of Finance
Understand the factor evidenceEvidence: Cohort or observational study
Gross profitability predicts returns about as well as value
Gross profits divided by assets predicted the cross-section of average returns about as well as book-to-market. Profitable firms earned higher average returns despite higher valuation ratios, and controlling for profitability markedly improved value strategies, especially among large, liquid stocks.
Limits: Mainly US stocks from 1963; later 'quality' and profitability factors use different definitions, so results do not transfer one-for-one.
Novy-Marx 2013, Journal of Financial Economics
Understand the factor evidenceEvidence: Cohort or observational study
Betting against beta, and a challenge to it
Frazzini and Pedersen found high-beta assets had low risk-adjusted returns in US equities, 20 international equity markets, Treasuries, corporate bonds and futures, and that a leveraged long-low-beta, short-high-beta (BAB) factor earned significant positive risk-adjusted returns. Novy-Marx and Velikov argued that BAB's reported performance comes largely from non-standard construction choices that effectively equal-weight stocks and tilt toward very small companies, profitability and investment.
Limits: Needs leverage and shorting; Frazzini and Pedersen are AQR-affiliated; the two papers disagree on how the factor should be built.
Frazzini & Pedersen 2014, Journal of Financial Economics · Novy-Marx & Velikov 2022, Journal of Financial Economics
Understand the factor evidenceEvidence: Cohort or observational study
Where size, value and momentum returns come from, and over time
Long positions made up almost all of size profits, about 60% of value profits and about half of momentum profits. The value premium shrank with firm size and was weak among the largest stocks, momentum showed no reliable link to size, and the authors found little evidence that these returns changed with trading costs or institutional ownership over time.
Limits: 86 years of US data plus about 40 years from four international equity markets and five asset classes; authors affiliated with AQR.
Israel & Moskowitz 2013, Journal of Financial Economics
Understand the factor evidenceEvidence: Cohort or observational study
Published anomalies weaken after publication
Across 97 published return predictors, portfolio returns were 26% lower out-of-sample and 58% lower after publication. The authors attribute about 32 percentage points of the decline to investors trading on the published findings, with the rest an upper bound for data mining.
Limits: US cross-sectional predictors, gross of costs; decay varies widely by predictor.
McLean & Pontiff 2016, The Journal of Finance
Understand the factor evidenceEvidence: Theory or logical argument
Factor discoveries need a higher statistical bar
Given hundreds of factors tested on the same data, the authors argue the usual significance threshold is too low and that a new factor should clear a t-statistic above 3.0. They argue that most claimed research findings in financial economics are likely false.
Limits: A methodological argument; the right threshold depends on assumptions about how many tests were run, which Jensen, Kelly & Pedersen (2023) dispute.
Harvey, Liu & Zhu 2016, The Review of Financial Studies
Understand the factor evidenceEvidence: Cohort or observational study
Most of 452 anomalies failed to replicate under stricter tests
With microcaps reduced by using NYSE breakpoints and value-weighted returns, 65% of 452 anomalies failed a t-value hurdle of 1.96, rising to 82% at a multiple-testing hurdle of 2.78. Even anomalies that replicated were much smaller than originally reported.
Limits: US stocks; conclusions depend on the value-weighting and breakpoint choices, and the authors promote their own competing q-factor model.
Hou, Xue & Zhang 2020, The Review of Financial Studies
Understand the factor evidenceEvidence: Cohort or observational study
A counter-argument: most factors do replicate
Using a Bayesian model of factor replication, the authors found that most asset pricing factors can be replicated, cluster into 13 themes, and work out-of-sample in a new data set covering 93 countries. They argue the large number of factors strengthens rather than weakens the evidence.
Limits: Conclusions rest on the Bayesian prior and method; co-author Pedersen is an AQR principal; the data are public at jkpfactors.com.
Jensen, Kelly & Pedersen 2023, The Journal of Finance
Put factors to workEvidence: Cohort or observational study
Fund 'hot hands' were mostly momentum and costs
In a sample free of survivor bias, common factors (including one-year momentum) and expenses almost fully explained persistence in equity mutual fund returns. The only unexplained persistence was among the worst funds continuing to underperform, and the results did not support skilled managers.
Limits: US equity mutual funds, 1962 to 1993; the paper introduced the four-factor model commonly used to judge factor funds.
Carhart 1997, The Journal of Finance
Put factors to workEvidence: Cohort or observational study
Timing factors by valuation: mixed evidence
Asness and co-authors found that timing styles by their value spreads looked promising alone but added mostly extra value exposure, and gave disappointing results inside a portfolio that already held value. A later century-long study by AQR researchers of value, momentum, carry and defensive premia across six asset classes found that premia vary over time and are mildly predictable, with little relation to macroeconomic risks and some evidence of overfitting; but significant profitability eluded a range of timing strategies once realistic data lags and trading costs were counted.
Limits: Both papers come from AQR researchers and use different signals; neither tests a retail fund.
Asness, Chandra, Ilmanen & Israel 2017, The Journal of Portfolio Management · Ilmanen, Israel, Moskowitz, Thapar & Lee 2021, Journal of Investment Management · Ilmanen et al. 2019 (working paper), SSRN
Put factors to workEvidence: Industry or index-provider data
Smart-beta returns inflated by rising valuations
Research Affiliates argued that, net of changes in valuation levels, many factor returns were much lower than recent performance suggested, and that performance-chasing inflows had pushed valuations up. They warned of a reasonable probability of a 'smart beta crash' as factor-tilt strategies grew popular.
Limits: Practitioner research from a firm then selling fundamental-index strategies (since renamed; the archive is now at rafi.com); AQR's 2017 paper disputes how overvalued factors were.
Arnott, Beck, Kalesnik & West 2016, Research Affiliates · Arnott et al. 2016, SSRN
Quant and rules-based strategiesEvidence: Cohort or observational study
Momentum crashes
Momentum strategies had strong average returns but suffered infrequent, persistent strings of large losses. These crashes were partly forecastable: they occurred in panic states after market declines, when volatility was high and the market rebounded. A dynamic version that scaled exposure using forecasts of momentum's mean and variance roughly doubled the static strategy's Sharpe ratio in the authors' tests.
Limits: US stocks from 1927 plus international equities and other asset classes; the dynamic strategy is a backtest.
Daniel & Moskowitz 2016, Journal of Financial Economics
Quant and rules-based strategiesEvidence: Cohort or observational study
Time-series momentum and a century of trend following
Moskowitz, Ooi and Pedersen found that each of 58 liquid equity index, currency, commodity and bond futures showed returns persisting for 1 to 12 months and partly reversing later; a diversified portfolio did best in extreme markets. Hurst, Ooi and Pedersen rebuilt a similar strategy back to 1880 and found it was consistently profitable over the 137 years studied.
Limits: The long history is a simulation built from historical prices, not fund returns; authors were AQR principals when the papers were published.
Moskowitz, Ooi & Pedersen 2012, Journal of Financial Economics · Hurst, Ooi & Pedersen 2017, The Journal of Portfolio Management
Quant and rules-based strategiesEvidence: Theory or logical argument
Backtest overfitting and the deflated Sharpe ratio
Bailey and López de Prado show that testing many strategy variants and reporting only the best leads to overly optimistic performance expectations. Their deflated Sharpe ratio adjusts for the number of trials and for non-normal returns. A companion paper argues that backtests which do not report how many configurations were tried cannot be properly assessed.
Limits: Statistical methods, not a test of any one strategy; the adjustment needs the true number of trials, which outside readers rarely know.
Bailey & López de Prado 2014, The Journal of Portfolio Management · Bailey, Borwein, López de Prado & Zhu 2014, Notices of the American Mathematical Society
Alternatives and private marketsEvidence: Cohort or observational study
Private equity vs public markets: it depends on the data
Kaplan and Schoar found average private equity fund returns net of fees roughly equal to the S&P 500, with wide dispersion and strong persistence across a firm's successive funds. Using newer Burgiss data on nearly 1,400 US buyout and venture funds, Harris, Jenkinson and Kaplan found buyout funds beat the S&P 500 by 20% to 27% over a fund's life (more than 3% a year), while venture funds beat public equities in the 1990s but lagged in the 2000s.
Limits: US funds; results swing with the data source (Venture Economics showed lower returns) and the benchmark, and an S&P 500 comparison does not adjust for buyout leverage or small-company exposure.
Kaplan & Schoar 2005, The Journal of Finance · Harris, Jenkinson & Kaplan 2014, The Journal of Finance
Alternatives and private marketsEvidence: Cohort or observational study
Private equity fees and the 'inconvenient fact'
Phalippou and Gottschalg estimated average net-of-fee fund performance at 3% a year below the S&P 500 (6% after risk adjustment), with fees of about 6% a year and reported performance inflated by valuations of unsold investments. Phalippou (2020) found private equity returns roughly matched public equity indices since about 2006, with average net multiples of about 1.55 to 1.63 (about 11% a year), while an estimated US$230 billion in performance fees went largely to a small number of people.
Limits: The 2009 paper uses older funds and is disputed by later data; the 2020 paper is a single-author critique that discusses industry rebuttals; IRR, multiple and PME methods give different answers.
Phalippou & Gottschalg 2009, The Review of Financial Studies · Phalippou 2020, The Journal of Investing
Alternatives and private marketsEvidence: Cohort or observational study
Hedge fund investors kept far less than '2 and 20' implies
Over a 22-year sample of hedge funds, the effective incentive fee rate was about 50% rather than the headline 20%, because about 60% of the gains on which incentive fees were paid were later offset by losses. The working-paper version estimated investors kept 36 cents of every dollar earned above a risk-free hurdle, before risk adjustment.
Limits: Funds in commercial hedge fund databases (5,917 funds in the working paper); before risk adjustment; one author disclosed a past role as a hedge fund general partner.
Ben-David, Birru & Rossi 2026, The Review of Corporate Finance Studies · Ben-David, Birru & Rossi 2020 (working paper), NBER Working Paper 27454
Alternatives and private marketsEvidence: Cohort or observational study
Commodity futures as an asset class
An equal-weighted index of fully collateralized commodity futures from July 1959 to December 2004 earned about the same return and Sharpe ratio as US equities, was negatively correlated with stock and bond returns, and was positively correlated with inflation. A ten-year follow-up by the same research group found the conclusions largely held out-of-sample.
Limits: Equal-weighted, monthly-rebalanced index; investable commodity funds weight and roll differently, and correlations rose temporarily in the 2008 crisis.
Gorton & Rouwenhorst 2006, Financial Analysts Journal · Bhardwaj, Gorton & Rouwenhorst 2015, NBER Working Paper 21243
Hedging and tail riskEvidence: Cohort or observational study
Covered calls: where the return actually comes from
Index covered calls earned their risk-adjusted returns mainly from equity exposure plus a short-volatility premium, but also carried an uncompensated 'equity reversal' exposure that made up about a quarter of risk for little reward. The short-volatility part had a realized Sharpe ratio near 1.0 but contributed under 10% of risk.
Limits: US equity index covered calls; the decomposition is the authors' own model; published while the authors were at AQR.
Israelov & Nielsen 2015, Financial Analysts Journal
Hedging and tail riskEvidence: Cohort or observational study
Protective puts often protect less than expected
In simulations and a study of the CBOE S&P 500 5% Put Protection Index, typical protective put programmes were quite ineffective at reducing drawdowns compared with simply holding less equity. Unless purchases and expiries happened to line up with drawdowns, puts could increase drawdowns and volatility per unit of expected return.
Limits: US S&P 500 options; covers typical rolling put programmes, not every tail-hedge design (some practitioners argue rules for selling puts early change results).
Israelov 2018, The Journal of Alternative Investments
Expected returnsEvidence: Cohort or observational study
Equity premium forecasts mostly failed out-of-sample
Reexamining dividend yields, earnings yields, book-to-market, interest rates and other predictors, Welch and Goyal found the forecasting models had failed both in-sample and out-of-sample over the prior 30 years and would not have helped an investor time the market. Campbell and Thompson responded that forecasts with simple sign restrictions beat the historical average, though by a small margin.
Limits: US equity premium, data to 2005; the debate is over small out-of-sample gains.
Welch & Goyal 2008, The Review of Financial Studies · Campbell & Thompson 2008, The Review of Financial Studies
Expected returnsEvidence: Theory or logical argument
Estimating expected returns: four inputs, humbly combined
Ilmanen describes expected-return judgments as mixing historical averages, finance theory, forward-looking indicators such as yields and valuation ratios, and discretionary views. He argues forward-looking indicators have a better forecasting record than historical averages, but that forecasts are noisy and tactical tilts should be a small part of a portfolio's risk.
Limits: A CFA Institute Research Foundation monograph drawn from the author's book, not a peer-reviewed study; the author has long worked at AQR.
Ilmanen 2012, CFA Institute Research Foundation
