Pith. sign in

REVIEW 4 major objections 5 minor 44 references

Supervised Similarity for Firm Linkages

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that firm linkages defined by characteristic-vector similarity can drive momentum spillover, and that a quantum-cognition learned distance beats Euclidean distance, lifting the 252-day Sharpe ratio from 0.73 to 1.10.

desk verdict A plausible application of quantum-inspired distance learning to equity momentum spillover, undermined by absent significance tests and a black-box covariance estimator. read the letter →

arxiv 2506.19856 v1 pith:VKIWQISI submitted 2025-06-09 q-fin.ST cs.LGquant-ph

Ryan Samson , Adrian Banner , Luca Candelori , Sebastien Cottrell , Tiziana Di Matteo , Paul Duchnowski , Vahagn Kirakosyan , Jose Marques
show 4 more authors
Kharen Musaelian Stefano Pasquali Ryan Stever Dario Villani
This is my paper
classification q-fin.STcs.LGquant-ph
keywords momentumspilloverfirmlinkagescharacteristicvectorsimilaritylearningquantumcognitionmachinedistancemetricequityreturnpredictabilitysupervised
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that the economic links between firms can be proxied by the similarity of their characteristic vectors—accounting and valuation ratios such as book-to-price, leverage, and profit margin. It names this proxy Characteristic Vector Linkages (CVLs) and tests two ways of turning the proxy into a distance: plain Euclidean distance on the characteristic vectors, and a supervised Quantum Cognition Machine Learning (QCML) distance that maps each firm's characteristics into a quantum state and measures proximity by quantum fidelity. Both distances feed a momentum spillover signal—a firm's expected return is influenced by the lagged returns of firms with similar characteristics—and both produce positive-Sharpe, market-neutral portfolios over January 2014 to June 2024. The central claim is that QCML similarity outperforms Euclidean similarity, especially at long input horizons: the 252-day input return QCML signal reaches a Sharpe ratio of 1.10 versus 0.73 for the Euclidean signal, with more than twice the signal half-life. If this claim holds, supervised similarity learning offers a practical way to extract more persistent cross-firm relationships from public fundamental data.

What carries the argument

The central object is the Characteristic Vector Linkage (CVL), defined as the pairwise similarity of firms computed from a vector of characteristics, and the key machinery is the QCML distance. QCML maps each firm's characteristic vector $x_{t,j}$ to the ground state $\psi_{t,j}$ of an error Hamiltonian $H(x_{t,j},\{A_c\}) = \sum_c (A_c - x^c_{t,j} I)^2$, where the $A_c$ are learned Hermitian observables; with a learned target observable $B$, the model is trained to forecast 63-day forward returns. Despite the name, this is a classical algorithm built on quantum-state mathematics. Proximity between states is measured by quantum fidelity $f(\psi_i,\psi_j)=|\langle \psi_i | \psi_j \rangle|^2$, converted to the Bures distance $D_{\mathrm{QCML}}=\sqrt{2-2|\langle \psi_i | \psi_j \rangle|}$, and then to similarity $S=e^{-\gamma D^2}$. The momentum spillover signal for firm $j$ is $f_{l,t,j} = \sum_i w_{t,j,i} r_{t-l:t-1,i}$ with weights $w_{t,j,i}=S_{j,i}/\sum_i S_{j,i}$, evaluated in daily mean-variance optimal portfolios with zero exposure to standard controls. The Euclidean variant runs the same pipeline with $D$ equal to the Euclidean distance of the raw characteristic vectors; apart from rescaling $\gamma$ so the two distances have comparable medians, the central difference is the learned versus unlearned distance.

What would settle it

Reconstruct the same momentum spillover portfolios with a fully published covariance estimator, such as a standard shrinkage estimator, and recompute the 252-day and combined Sharpe ratios; if the QCML advantage over Euclidean disappears or reverses, the claimed outperformance rests on the undisclosed estimator rather than on the learned similarity.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that Characteristic Vector Linkages are a working basis for momentum spillover, and that learning the distance function with QCML makes the linkages more robust. Firms are represented by a vector of $C$ characteristics; QCML learns $C$ Hermitian observables $A_c$ and a target observable $B$ so that each firm's ground state $\psi_{t,j}$ of the error Hamiltonian $H(x_{t,j},\{A_c\}) = \sum_c (A_c - x^c_{t,j} I)^2$ predicts 63-day forward returns. The distance between two firms is the Bures distance built from quantum fidelity, $D_{\mathrm{QCML}} = \sqrt{2 - 2|\langle \psi_i | \psi_j \rangle|}$, and similarity is $e^{-\gamma D^2}$. Portfolios formed from the resulting spillover signal—lagged returns weighted by similarity—are market neutral and neutralized against analyst coverage, momentum, size, and industry controls. The paper reports that QCML similarity beats Euclidean similarity for every input return horizon, with the largest edge at 252 days (Sharpe 1.10 vs 0.73) and in the combined signal (1.42 vs 1.24), while also yielding materially longer signal half-lives, which it reads as evidence that the learned relationships are more persistent and less noisy.

Load-bearing premise

The Sharpe ratios are computed with a daily covariance matrix estimated by a proprietary, undisclosed technique, so if that estimator is miscalibrated or non-reproducible, the reported performance advantage of QCML over Euclidean similarity is not independently verifiable.

Editorial extensions

If this is right

  • Characteristic Vector Linkages formed from Euclidean distance on accounting and valuation ratios are alone enough to construct positive-Sharpe momentum spillover portfolios, with full-sample Sharpe ratios between 0.71 and 1.35 depending on the input return horizon.
  • Supervised QCML similarity improves on Euclidean similarity for every input horizon tested, and the improvement grows with the horizon: the 252-day input return Sharpe rises from 0.73 to 1.10 while the signal half-life rises from 36.3 to 90.9 days.
  • The combined 21/63/126/252-day QCML signal reaches a Sharpe of 1.42 versus 1.24 for Euclidean, with roughly 1.6 times the half-life.
  • Because the QCML parameters are trained once on data from October 2007 through August 2013 and then held static, periodic retraining or online updating is an available path to further gains.
  • Both approaches weaken in the January 2021 through June 2024 sub-period, but the QCML signals retain a modest edge and draw less of their performance from the strongest sub-period.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct robustness test would replace the proprietary covariance estimator with a fully published estimator and re-run the strategies; the reported gap between QCML and Euclidean could shrink or vanish, because the covariance matrix enters every Sharpe ratio.
  • Because QCML is trained on 63-day forward returns, the learned similarity encodes a return-horizon-specific notion of relatedness; training on earnings surprises or revenue growth would likely produce different linkages, a variation the paper itself leaves open.
  • The longer half-lives of the QCML signals suggest the learned linkages are more persistent; one could test this by checking whether top QCML-linked pairs coincide with observable supply-chain or shared-analyst links, or by measuring their co-movement after June 2024.
  • The distance-learning recipe is asset-class agnostic; porting it to corporate bonds, currencies, or risk clustering would test whether the improvement over Euclidean distance generalizes beyond US equities.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces Characteristic Vector Linkages (CVLs) as a proxy for firm linkages based on vectors of firm characteristics, and constructs momentum spillover trading signals from two similarity measures: a simple Euclidean similarity and a learned similarity based on Quantum Cognition Machine Learning (QCML). The QCML model is trained on 63-day forward returns using data from October 2007 through August 2013, with parameters held fixed during the evaluation period from January 2014 through June 2024. The authors report Sharpe ratios for portfolios formed from the spillover signals at 21-, 63-, 126-, and 252-day input horizons and a combined signal, and claim that QCML similarity outperforms Euclidean similarity, especially at the 252-day horizon (full-sample Sharpe 1.10 vs. 0.73) and for the combined signal (1.42 vs. 1.24).

Significance. If established, the result would be a useful contribution to the literature on supervised similarity learning for cross-firm return predictability, showing that a learned representation can improve on raw-feature similarity for momentum spillover strategies. The paper has genuine strengths: the temporal split is clean (training ends in 2013, testing starts in 2014), the QCML parameters are static out-of-sample, an ensemble of 50 seeds is used, and the portfolio construction controls for standard characteristics such as size, beta, momentum, and analyst coverage. However, the central claim is not yet supported with adequate statistical evidence, depends on an unreproducible covariance estimator, and contains internal numerical inconsistencies. These issues must be addressed before the outperformance claim can be evaluated.

major comments (4)
  1. [Section 6.2, Tables 2 and 4] The central claim that QCML similarity outperforms Euclidean similarity is supported only by point estimates of full-sample Sharpe ratios. No standard errors, confidence intervals, or tests of the equality of Sharpe ratios are reported, despite the paper stating that the two signals have average daily cross-sectional correlations of 0.74-0.78. Because the 252-day signal uses overlapping returns and has a half-life of 90.9 days for QCML and 36.3 days for Euclidean, the effective number of independent observations is far below the roughly 2,500 daily observations in the test period, so the 0.37 Sharpe gap may be within sampling noise. A formal test, such as a bootstrap or HAC-based test of the Sharpe ratio difference, or a Diebold-Mariano test on the daily returns, is required, and the multiple testing across four horizons and three subperiods should be acknowledged.
  2. [Section 5.5, footnote 4] The portfolio construction uses a daily covariance matrix estimated with a technique proprietary to Duality Group, whose details are not provided. Because the portfolio weights are w = V^{-1} R f, every reported return and Sharpe ratio in Tables 2 and 4 depends on this unobservable matrix. As a result, the central comparison is not independently verifiable or reproducible. The authors should either replace the proprietary estimator with a fully specified standard covariance estimator, provide the code or estimates, or demonstrate that the headline QCML-versus-Euclidean comparison is robust to a range of reasonable covariance estimators.
  3. [Conclusion vs. Section 6.2] The conclusion reports the 252-day Sharpe comparison as 1.12 versus 0.76 for QCML versus Euclidean, and the combined signal as 1.43 versus 1.26, while Section 6.2 reports the same full-sample comparisons as 1.10 versus 0.73 and 1.42 versus 1.24. This internal inconsistency in the key quantitative claim must be corrected; as written, it is unclear which set of numbers is the authoritative result and undermines confidence in the reported precision.
  4. [Section 5.5] The paper does not deduct transaction costs, yet the abstract and conclusion describe the strategies as 'profitable.' Since the portfolios are smoothed over 21 days but the similarity matrices and forecasts are computed daily, turnover is likely substantial. Without reporting average turnover or break-even transaction costs, the economic significance of the Sharpe ratios is not established, particularly for the practical value of the strategy that the abstract promises.
minor comments (5)
  1. [Section 5.2] The sentence 'from October 2017 through June 2024' appears to be a typo for October 2007, since the QCML training period in Section 5.3 is October 2007 through August 2013 and the test period starts in January 2014.
  2. [Section 3.1, Equation (7)] The loss function is written as f(y_t,j, x_t,j, {A_c}, B, w), but the functional form of f is never explicitly defined; please define f or restructure the equation so the argument list matches the expression shown.
  3. [Conclusion] The conclusion states that Euclidean Sharpe ratios range from 0.73 to 1.37, but Table 2 includes a full-sample value of 0.71 for the 126-day signal; the range should be corrected.
  4. [References] Reference [35] is cited merely as 'Risk.net' without a title, volume, or page numbers; a complete citation is needed for a published working paper.
  5. [Section 3.1] The sentence 'choices of N in [4,32] have been seen to give optimal cross-validated accuracy' should be clarified as reporting prior experimental experience rather than a result of this paper, since this paper only reports results for N=12.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation found; the QCML-vs-Euclidean comparison is an out-of-sample empirical result, with only minor non-load-bearing self-citation.

full rationale

The paper's derivation chain is not circular. The QCML similarity is trained on 63-day forward returns during October 2007 through August 2013, and the momentum spillover signals are constructed and evaluated on the separate January 2014 through June 2024 test period using lagged returns as inputs and future returns as the evaluation target. The learned similarity is not defined in terms of the test-period outcome, and the Sharpe ratios are computed from portfolios whose weights depend on the similarity and on a covariance estimate, not on the training loss. The Euclidean and QCML signals are highly correlated (0.74-0.78), and the claimed outperformance is a point estimate without significance testing, but that is a statistical robustness concern, not circularity. The paper cites prior QCML work by overlapping authors [10, 25, 38] for the framework, but the current paper implements and tests QCML itself, so the self-citation is not load-bearing evidence for the central empirical claim. The proprietary covariance estimator (Section 5.5) and the inconsistent Sharpe numbers between Section 6.2 and the Conclusion are separate reproducibility and accuracy issues, not circular steps. Therefore no specific reduction of a prediction to its inputs by construction was found; the score reflects only the minor self-citation cluster.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central claim rests on the assumption that characteristic similarity implies economic linkage, on the existence of momentum spillover, on the inductive hypothesis that a QCML representation trained on forward returns will generalize, and on an undisclosed covariance estimator. The free parameters are mostly standard hyperparameters, but gamma_QCML and the target horizon are choices that directly shape the learning problem without a reported sensitivity analysis.

free parameters (5)
  • gamma_QCML = 16
    Scaling parameter in the similarity kernel (equation 10). Chosen so that gamma_QCML * D_QCML^2 matches the median of gamma_Euclidean * D_Euclidean^2 over the training data (Section 4). Affects the concentration of similarity values and thus the signals.
  • gamma_Euclidean = 1
    Scaling parameter for Euclidean similarity. The paper states it verified results are not sensitive and a value of 1 gives close to optimal results (Section 4). It is still a chosen constant.
  • Hilbert space dimension N = 12
    Hyperparameter of QCML models (Section 3.1). Larger N lowers training loss but risks overfitting; N=12 is reported as a good choice across problems. The paper does not provide a sensitivity analysis for the present data.
  • Loss weight w = not stated
    Hyperparameter in the QCML loss (equation 7), balancing prediction error and input coherence. The paper says results are similar with w=0, but does not give the value used for the main results.
  • Target horizon for QCML training = 63 days
    The target variable is 63-day forward returns, cross-sectionally z-scored (Section 5.3). This is a modeling choice not derived from data; the paper notes other targets could be used.
assumptions (5)
  • domain assumption Similarity of characteristic vectors is a valid proxy for economic linkages that transmit return shocks with a lag.
    Section 2: 'we propose that, across a broad set of characteristics, the more similar the factor scores of any pair of firms, the more economically similar the underlying pair of firms truly is'. This assumption underpins both similarity measures.
  • domain assumption The momentum spillover effect exists and can be captured by weighting lagged returns by firm similarity.
    Section 5.4 builds the signal as a similarity-weighted average of lagged returns; the paper relies on prior literature [1, 12, 24, 32] for the existence of momentum spillover.
  • ad hoc to paper The QCML ground state representation, trained to predict 63-day forward returns, yields a similarity measure that is more informative than the raw features out-of-sample.
    This is the key inductive hypothesis of the paper (Section 5.3). It is not proven; it is supported only by the empirical backtest.
  • ad hoc to paper The proprietary covariance estimator provides an appropriate risk model for portfolio construction.
    Section 5.5: 'covariance is estimated using a technique proprietary to Duality Group, the details of which are not relevant for this article'. The paper assumes this does not materially affect the results.
  • standard math Standard linear algebra and quantum mechanics formalism as used in QCML are valid.
    The fidelity and Bures distance definitions rely on standard quantum information results (Nielsen and Chuang [27], Spehner et al. [42]).
invented entities (1)
  • Characteristic Vector Linkages (CVLs) independent evidence
    purpose: A named proxy for firm linkages defined as the similarity of firm characteristic vectors (Section 2).
    The paper provides backtested momentum spillover Sharpe ratios as evidence that CVLs capture meaningful linkages. However, the concept is essentially a relabeling of standard characteristic-based similarity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Supervised Similarity for Firm Linkages." pith.science (2026). https://pith.science/paper/VKIWQISI

@misc{pith2026250619856,
  author       = {Pith},
  title        = {Pith review of: Supervised Similarity for Firm Linkages},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VKIWQISI}},
  note         = {Machine review of arXiv:2506.19856}
}
read the original abstract

We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by applying Quantum Cognition Machine Learning (QCML) to similarity learning. We demonstrate that both methods can be used to construct profitable momentum spillover trading strategies, but QCML similarity outperforms the simpler Euclidean similarity.

Figures

Figures reproduced from arXiv: 2506.19856 by the authors.

Figure 1
Figure 1. Cumulative returns to the Euclidean similarity CVL signals with varying input return horizons. Strategy portfolios are market neutral and have zero exposure to Analyst Connected Stock Momentum, Analyst Coverage, Beta, Momentum, Short Term Reversal, Size, Subindustry Momentum, Revenue to Price, and GICS Industry Groups, and thus have no linear contribution of those features to returns. Returns have been scaled by ful… view at source ↗
Figure 2
Figure 2. Cumulative returns to the QCML similarity CVL signals with varying input return horizons. Strategy portfolios are market neutral and have zero exposure to Analyst Connected Stock Momentum, Analyst Coverage, Beta, Momentum, Short Term Reversal, Size, Subindustry Momentum, Revenue to Price, and GICS Industry Groups, and thus have no linear contribution of those features to returns. Returns have been scaled by full sam… view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

44 extracted references · 41 canonical work pages

  1. [1]

    Shared analyst coverage: Unifying momentum spillover effects.Journal of Financial Economics, 136(3):649–675, 2020

    Usman Ali and David Hirshleifer. Shared analyst coverage: Unifying momentum spillover effects.Journal of Financial Economics, 136(3):649–675, 2020

    work page 2020

  2. [2]

    Asness, Andrea Frazzini, and Lasse H

    Clifford S. Asness, Andrea Frazzini, and Lasse H. Pedersen. Low-risk investing without industry bets.Financial Analysts Journal, 70(4):24–41, 2014

    work page 2014

  3. [3]

    Asness, Roger B

    Clifford S. Asness, Roger B. Porter, and Ross L. Stevens. Predicting stock returns using industry-relative firm characteristics.Capital Markets eJournal, 2000

    work page 2000

  4. [4]

    Rolf W. Banz. The relationship between return and market value of common stocks.Journal of Financial Economics, 9(1):3–18, 1981

    work page 1981

  5. [5]

    Barbee, Sandip Mukherji, and Gary A

    William C. Barbee, Sandip Mukherji, and Gary A. Raines. Do sales–price and debt–equity explain stock returns better than book–market and firm size?Financial Analysts Journal, 52:56–60, 1996

    work page 1996

  6. [6]

    Bartram, J¨ urgen Branke, Giuliano De Rossi, and Mehrshad Motahari

    S¨ ohnke M. Bartram, J¨ urgen Branke, Giuliano De Rossi, and Mehrshad Motahari. Machine learning for active portfolio management.The Journal of Financial Data Science, 3(3):9–30, 2021

    work page 2021

  7. [7]

    The relationship between earnings’ yield, market value and return for nyse common stocks: Further evidence.Journal of Financial Economics, 12(1):129–156, 1983

    Sanjoy Basu. The relationship between earnings’ yield, market value and return for nyse common stocks: Further evidence.Journal of Financial Economics, 12(1):129–156, 1983

    work page 1983

  8. [8]

    Cambridge University Press, 2006

    Ingemar Bengtsson and Karol Zyczkowski.Geometry of Quantum States: An Introduction to Quantum Entanglement, pages 187–191. Cambridge University Press, 2006

    work page 2006

Show all 44 references
  1. [9]

    Debt/equity ratio and expected common stock returns: Empirical evidence.The Journal of Finance, 43(2):507–528, 1988

    Laxmi Chand Bhandari. Debt/equity ratio and expected common stock returns: Empirical evidence.The Journal of Finance, 43(2):507–528, 1988

    1988

  2. [10]

    Abanov, Jeffrey Berger, Cameron J

    Luca Candelori, Alexander G. Abanov, Jeffrey Berger, Cameron J. Hogan, Vahagn Ki- rakosyan, Kharen Musaelian, Ryan Samson, James E.T. Smith, Dario Villani, Martin T. Wells, and Mengjia Xu. Robust estimation of the intrinsic dimension of data sets with quan- tum cognition machi...

    2025

  3. [11]

    On persistence in mutual fund performance.Journal of Finance, 52(1):57–82, 1997

    Mark Carhart. On persistence in mutual fund performance.Journal of Finance, 52(1):57–82, 1997

    1997

  4. [12]

    Economic links and predictable returns.The Journal of Finance, 63(4):1977–2011, 2008

    Lauren Cohen and Andrea Frazzini. Economic links and predictable returns.The Journal of Finance, 63(4):1977–2011, 2008

    1977

  5. [13]

    Implied equity duration: A new measure of equity risk.Review of Accounting Studies, 9(2):197–228, 06 2004

    Patricia Dechow, Richard Sloan, and Mark Soliman. Implied equity duration: A new measure of equity risk.Review of Accounting Studies, 9(2):197–228, 06 2004

    2004

  6. [14]

    Value-glamour and accruals mispricing: One anomaly or two?The Accounting Review, 79(2):355–385, 2004

    Hemang Desai, Shivaram Rajgopal, and Mohan Venkatachalam. Value-glamour and accruals mispricing: One anomaly or two?The Accounting Review, 79(2):355–385, 2004

    2004

  7. [15]

    Common risk factors in the returns on stocks and bonds

    Eugene Fama and Kenneth French. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1):3–56, 1993. 12

    1993

  8. [16]

    Fama and James D

    Eugene F. Fama and James D. MacBeth. Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81:607 – 636, 1973

    1973

  9. [17]

    Do investors overvalue firms with bloated balance sheets?Journal of Accounting and Economics, 38:297–331, 2004

    David Hirshleifer, Kewei Hou, Siew Hong Teoh, and Yinglei Zhang. Do investors overvalue firms with bloated balance sheets?Journal of Accounting and Economics, 38:297–331, 2004. Conference Issue on Research on Market Efficiency, Valuation, and Mispricing: Risk, Behav- ioral, an...

    2004

  10. [18]

    Replicating anomalies.The Review of Financial Studies, 33(5):2019–2133, 2020

    Kewei Hou, Chen Xue, and Lu Zhang. Replicating anomalies.The Review of Financial Studies, 33(5):2019–2133, 2020

    2019

  11. [19]

    Returns to buying winners and selling losers: Implications for stock market efficiency.Journal of Finance, 48(1):65–91, 1993

    Narasimhan Jegadeesh and Sheridan Titman. Returns to buying winners and selling losers: Implications for stock market efficiency.Journal of Finance, 48(1):65–91, 1993

    1993

  12. [20]

    Is there a replication crisis in finance?The Journal of Finance, 78(5):2465–2518, 2023

    Theis Ingerslev Jensen, Bryan Kelly, and Lasse Heje Pedersen. Is there a replication crisis in finance?The Journal of Finance, 78(5):2465–2518, 2023

    2023

  13. [21]

    Supervised similarity learning for corporate bonds using random forest proximities, 2022

    Jerinsh Jeyapaulraj, Dhruv Desai, Peter Chu, Dhagash Mehta, Stefano Pasquali, and Philip Sommer. Supervised similarity learning for corporate bonds using random forest proximities, 2022

    2022

  14. [22]

    Taxable income, future earnings, and equity values.The Accounting Review, 79(4):1039–1074, 10 2004

    Baruch Lev and Doron Nissim. Taxable income, future earnings, and equity values.The Accounting Review, 79(4):1039–1074, 10 2004

    2004

  15. [23]

    Tim Loughran and Jay W. Wellman. New evidence on the relation between the enter- prise multiple and average stock returns.Journal of Financial and Quantitative Analysis, 46(6):1629–1650, 2011

    2011

  16. [24]

    Moskowitz and Mark Grinblatt

    Tobias J. Moskowitz and Mark Grinblatt. Do industries explain momentum?The Journal of Finance, 54(4):1249–1290, 1999

    1999

  17. [25]

    Quantum cognition machine learning: AI needs quantum, 2024

    Kharen Musaelian et al. Quantum cognition machine learning: AI needs quantum, 2024. https://www.qognitive.io/papers/QCML%20-%20Qognitive,%20Inc.pdf

    2024

  18. [26]

    On spectral clustering: Analysis and an algo- rithm

    Andrew Ng, Michael Jordan, and Yair Weiss. On spectral clustering: Analysis and an algo- rithm. In T. Dietterich, S. Becker, and Z. Ghahramani, editors,Advances in Neural Infor- mation Processing Systems, volume 14. MIT Press, 2001

    2001

  19. [27]

    Nielsen and Isaac L

    Michael A. Nielsen and Isaac L. Chuang.Quantum Computation and Quantum Information. Cambridge University Press, 2000

    2000

  20. [28]

    Phillips

    Hern´ an Ortiz-Molina and Gordon M. Phillips. Real asset illiquidity and the cost of capital. Journal of Financial and Quantitative Analysis, 49(1):1–32, 2014

    2014

  21. [29]

    Cash holdings, risk, and expected returns.Journal of Financial Eco- nomics, 104(1):162–185, 2012

    Berardino Palazzo. Cash holdings, risk, and expected returns.Journal of Financial Eco- nomics, 104(1):162–185, 2012

    2012

  22. [30]

    Geographic lead-lag effects.The Review of Financial Studies, 33(10):4721–4770, 01 2020

    Christopher A Parsons, Riccardo Sabbatucci, and Sheridan Titman. Geographic lead-lag effects.The Review of Financial Studies, 33(10):4721–4770, 01 2020

    2020

  23. [31]

    Penman, Scott A

    Stephen H. Penman, Scott A. Richardson, and ˙Irem Tuna. The book-to-price effect in stock returns: Accounting for leverage.Journal of Accounting Research, 45(2):427–467, 2007

    2007

  24. [32]

    Does corporate headquarters location matter for stock returns?The Journal of Finance, 61(4):1991–2015, 2006

    Christo Pirinsky and Qinghai Wang. Does corporate headquarters location matter for stock returns?The Journal of Finance, 61(4):1991–2015, 2006

    1991

  25. [33]

    Pothos and Jerome R

    Emmanuel M. Pothos and Jerome R. Busemeyer. Quantum cognition.Annual Review of Psychology, 73(1):749–778, 2022

    2022

  26. [34]

    Richardson, Richard G

    Scott A. Richardson, Richard G. Sloan, Mark T. Soliman, and Irem Tuna. Accrual reliability, earnings persistence and stock prices.Journal of Accounting and Economics, 39(3):437–485, 2005. 13

    2005

  27. [35]

    Supervised similarity for high-yield bonds, 2025

    Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan, Dhagash Mehta, Kharen Musaelian, Stefano Pasquali, Ryan Samson, and Martin T Wells. Supervised similarity for high-yield bonds, 2025. Risk.net

    2025

  28. [36]

    Persuasive evidence of market ineffi- ciency.The Journal of Portfolio Management, 11(3):9–16, 1985

    Barr Rosenberg, Kenneth Reid, and Ronald Lanstein. Persuasive evidence of market ineffi- ciency.The Journal of Portfolio Management, 11(3):9–16, 1985

    1985

  29. [37]

    A meta-analysis of supervised and unsupervised machine learning algorithms and their application to active portfolio management.Expert Systems with Applications, 271:126611, 2025

    Ayari Salah and Gatfaoui Hayette. A meta-analysis of supervised and unsupervised machine learning algorithms and their application to active portfolio management.Expert Systems with Applications, 271:126611, 2025

    2025

  30. [38]

    Quantum cognition machine learning: financial forecasting, 2024

    Ryan Samson, Jeffrey Berger, Luca Candelori, Vahagn Kirakosyan, Kharen Musaelian, and Dario Villani. Quantum cognition machine learning: financial forecasting, 2024. Risk.net

    2024

  31. [39]

    Capital asset prices: A theory of market equilibrium under conditions of risk.Journal of Finance, 19(3):425–442, 1964

    William Sharpe. Capital asset prices: A theory of market equilibrium under conditions of risk.Journal of Finance, 19(3):425–442, 1964

    1964

  32. [40]

    Richard G. Sloan. Do stock prices fully reflect information in accruals and cash flows about future earnings?The Accounting Review, 71(3):289–315, July 1996

    1996

  33. [41]

    Mark T. Soliman. The use of dupont analysis by market participants.Corporate Finance: Valuation, 2007

    2007

  34. [42]

    Springer International Publishing, Cham, 2017

    Dominique Spehner, Fabrizio Illuminati, Miguel Orszag, and Wojciech Roga.Geometric Mea- sures of Quantum Correlations with Bures and Hellinger Distances, pages 105–157. Springer International Publishing, Cham, 2017

    2017

  35. [43]

    Corporate real estate holdings and the cross-section of stock returns.The Review of Financial Studies, 23(6):2268–2302, 03 2010

    Selale Tuzel. Corporate real estate holdings and the cross-section of stock returns.The Review of Financial Studies, 23(6):2268–2302, 03 2010

    2010

  36. [44]

    Company similarity using large language models, 2023

    Dimitrios Vamvourellis, M´ at´ e Toth, Snigdha Bhagat, Dhruv Desai, Dhagash Mehta, and Stefano Pasquali. Company similarity using large language models, 2023. 14

    2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.