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The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns

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Cited by:

  1. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
  2. Rungmaitree, Pattamon & Boateng, Agyenim & Ahiabor, Frederick & Lu, Qinye, 2022. "Political risk, hedge fund strategies, and returns: Evidence from G7 countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
  3. Ding, Jing & Jiang, Lei & Liu, Xiaohui & Peng, Liang, 2023. "Nonparametric tests for market timing ability using daily mutual fund returns," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
  4. Ding, Yi & Li, Yingying & Liu, Guoli & Zheng, Xinghua, 2024. "Stock co-jump networks," Journal of Econometrics, Elsevier, vol. 239(2).
  5. Huang, Haitao & Jiang, Lei & Leng, Xuan & Peng, Liang, 2023. "Bootstrap analysis of mutual fund performance," Journal of Econometrics, Elsevier, vol. 235(1), pages 239-255.
  6. Hoang, Khoa & Huang, Ronghong & Truong, Helen, 2023. "Resurrecting the market factor: A case of data mining across international markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
  7. Sun, Chuanping, 2024. "Factor correlation and the cross section of asset returns: A correlation-robust machine learning approach," Journal of Empirical Finance, Elsevier, vol. 77(C).
  8. Malakhov, Alexey & Riley, Timothy B. & Yan, Qing, 2024. "Do hedge funds bet against beta?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1507-1525.
  9. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
  10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  11. Samuel YM Ze‐To, 2022. "Fundamental index aligned and excess market return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 592-614, April.
  12. Wang, Jianqiu & Wu, Ke & Tong, Guoshi & Chen, Dongxu, 2023. "Nonlinearity in the cross-section of stock returns: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 174-205.
  13. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
  14. Jules H van Binsbergen & Xiao Han & Alejandro Lopez-Lira, 2023. "Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases," The Review of Financial Studies, Society for Financial Studies, vol. 36(6), pages 2361-2396.
  15. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
  16. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
  17. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
  18. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
  19. Siddhartha Chib & Simon C. Smith, 2024. "Factor Selection and Structural Breaks," Finance and Economics Discussion Series 2024-037, Board of Governors of the Federal Reserve System (U.S.).
  20. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
  21. Choy, Siu Kai & Lewis, Craig & Tan, Yongxian, 2023. "Can the changes in fundamentals explain the attenuation of anomalies?," Journal of Financial Economics, Elsevier, vol. 149(2), pages 142-160.
  22. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
  23. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
  24. Calice, Giovanni & Lin, Ming-Tsung, 2021. "Exploring risk premium factors for country equity returns," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 294-322.
  25. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
  26. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
  27. Cho, Thummim, 2018. "Turning alphas into betas: arbitrage and the cross-section of risk," LSE Research Online Documents on Economics 118915, London School of Economics and Political Science, LSE Library.
  28. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
  29. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
  30. Lars Heinrich & Martin Zurek, 2019. "Alpha forecasting in factor investing: discriminating between the informational content of firm characteristics," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(3), pages 243-275, September.
  31. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
  32. Penman, Stephen & Zhu, Julie, 2022. "An accounting-based asset pricing model and a fundamental factor," Journal of Accounting and Economics, Elsevier, vol. 73(2).
  33. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
  34. Baba-Yara, Fahiz & Boons, Martijn & Tamoni, Andrea, 2024. "Persistent and transitory components of firm characteristics: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 154(C).
  35. Harvey, Campbell R. & Liu, Yan, 2021. "Lucky factors," Journal of Financial Economics, Elsevier, vol. 141(2), pages 413-435.
  36. He, Shuoyuan & Narayanamoorthy, Ganapathi (Gans), 2020. "Earnings acceleration and stock returns," Journal of Accounting and Economics, Elsevier, vol. 69(1).
  37. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2024. "Evaluating asset pricing anomalies: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 70(PB).
  38. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
  39. Andrew Y. Chen & Alejandro Lopez-Lira & Tom Zimmermann, 2022. "Does Peer-Reviewed Research Help Predict Stock Returns?," Papers 2212.10317, arXiv.org, revised Jun 2024.
  40. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  41. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
  42. Zhaobo Zhu & Licheng Sun & Jun Tu, 2021. "Earnings momentum meets short‐term return reversal," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(S1), pages 2379-2405, April.
  43. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
  44. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
  45. Vu Le Tran & Guillaume Coqueret, 2023. "ESG news spillovers across the value chain," Post-Print hal-04325746, HAL.
  46. Claire Y. C. Liang & Rengong Zhang, 2020. "Post-earnings announcement drift and parameter uncertainty: evidence from industry and market news," Review of Quantitative Finance and Accounting, Springer, vol. 55(2), pages 695-738, August.
  47. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
  48. Allaudeen Hameed & Jing Xie & Yuxiang Zhong, 2024. "Preferences for dividends and stock returns around the world," Working Papers 202405, University of Macau, Faculty of Business Administration.
  49. Cici, Gjergji & Zhang, Pei (Alex), 2021. "On the valuation skills of corporate bond mutual funds," CFR Working Papers 21-05, University of Cologne, Centre for Financial Research (CFR).
  50. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
  51. Du, Kai & Huddart, Steven & Jiang, Xin Daniel, 2023. "Lost in standardization: Effects of financial statement database discrepancies on inference," Journal of Accounting and Economics, Elsevier, vol. 76(1).
  52. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
  53. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
  54. Kristoffer Pons Bertelsen, 2022. "The Prior Adaptive Group Lasso and the Factor Zoo," CREATES Research Papers 2022-05, Department of Economics and Business Economics, Aarhus University.
  55. Bank, Matthias & Insam, Franz, 2021. "Corporate aging and changes in the pricing of stock characteristics," Finance Research Letters, Elsevier, vol. 42(C).
  56. He, Jingbin & Ma, Xinru, 2023. "Is corporate social responsibility engagement influenced by nearby firms? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 86(C).
  57. Han, Yufeng & Huang, Dashan & Huang, Dayong & Zhou, Guofu, 2022. "Expected return, volume, and mispricing," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1295-1315.
  58. Ka Kei Chan & Ming‐Tsung Lin & Qinye Lu, 2024. "Corporate credit default swap systematic factors," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1224-1256, July.
  59. Bali, Turan G. & Beckmeyer, Heiner & Moerke, Mathis & Weigert, Florian, 2021. "Option return predictability with machine learning and big data," CFR Working Papers 21-08, University of Cologne, Centre for Financial Research (CFR).
  60. Anja Vinzelberg & Benjamin R. Auer, 2022. "Unprofitability of food market investments," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 2887-2910, October.
  61. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
  62. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
  63. Souza, Thiago de Oliveira, 2018. "Size-related premiums," Discussion Papers on Economics 3/2018, University of Southern Denmark, Department of Economics.
  64. Chen Zhang, 2022. "Asset Pricing and Deep Learning," Papers 2209.12014, arXiv.org.
  65. Liu, Chenye & Wu, Ying & Zhu, Dongming, 2022. "Price overreaction to up-limit events and revised momentum strategies in the Chinese stock market," Economic Modelling, Elsevier, vol. 114(C).
  66. Wang, Feifei & Yan, Xuemin Sterling, 2021. "Downside risk and the performance of volatility-managed portfolios," Journal of Banking & Finance, Elsevier, vol. 131(C).
  67. Back, Kerry & Crotty, Kevin & Kazempour, Seyed Mohammad, 2022. "Validity, tightness, and forecasting power of risk premium bounds," Journal of Financial Economics, Elsevier, vol. 144(3), pages 732-760.
  68. Ilan Cooper & Liang Ma & Paulo Maio, 2022. "What Does the Cross‐Section Tell About Itself? Explaining Equity Risk Premia with Stock Return Moments," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 73-118, February.
  69. Luo, Di, 2022. "ESG, liquidity, and stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
  70. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
  71. Nusret Cakici & Christian Fieberg & Daniel Metko & Adam Zaremba, 2024. "Do Anomalies Really Predict Market Returns? New Data and New Evidence," Review of Finance, European Finance Association, vol. 28(1), pages 1-44.
  72. Kim, Jang Ho & Han, Jiwoon & Kang, Taehyeon & Fabozzi, Frank J., 2023. "A machine learning approach for comparing the largest firm effect," Emerging Markets Review, Elsevier, vol. 54(C).
  73. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
  74. Vincent, Kendro & Hsu, Yu-Chin & Lin, Hsiou-Wei, 2021. "Investment styles and the multiple testing of cross-sectional stock return predictability," Journal of Financial Markets, Elsevier, vol. 56(C).
  75. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
  76. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
  77. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
  78. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
  79. Cynthia M. Gong & Di Luo & Huainan Zhao, 2021. "Liquidity risk and the beta premium," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(4), pages 789-814, December.
  80. Ma, Tian & Liao, Cunfei & Jiang, Fuwei, 2024. "Factor momentum in the Chinese stock market," Journal of Empirical Finance, Elsevier, vol. 75(C).
  81. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  82. Guo, Li & Li, Frank Weikai & John Wei, K.C., 2020. "Security analysts and capital market anomalies," Journal of Financial Economics, Elsevier, vol. 137(1), pages 204-230.
  83. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
  84. Jiang, Hao & Vayanos, Dimitri & Zheng, Lu, 2020. "Tracking biased weights: asset pricing implications of value-weighted indexing," LSE Research Online Documents on Economics 118847, London School of Economics and Political Science, LSE Library.
  85. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
  86. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
  87. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
  88. Liao, Cunfei & Luo, Qianlin & Tang, Guohao, 2021. "Aggregate liquidity premium and cross-sectional returns: Evidence from China," Economic Modelling, Elsevier, vol. 104(C).
  89. Jiawei Wang & Zhen Chen, 2023. "Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
  90. Benjamin R. Auer & Tobias Hiller, 2021. "Cost gap, Shapley, or nucleolus allocation: Which is the best game‐theoretic remedy for the low‐risk anomaly?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(4), pages 876-884, June.
  91. Thuy Duong Dang & Fabian Hollstein & Marcel Prokopczuk & Zhiguo He, 2023. "Which Factors for Corporate Bond Returns?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(4), pages 615-652.
  92. Yuming Li, 2023. "Asset Pricing and Microcaps," Annals of Economics and Finance, Society for AEF, vol. 24(1), pages 119-140, May.
  93. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
  94. Adam Farago & Erik Hjalmarsson, 2023. "Small Rebalanced Portfolios Often Beat the Market over Long Horizons," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(2), pages 307-342.
  95. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
  96. Stadtmüller, Immo & Auer, Benjamin R. & Schuhmacher, Frank, 2022. "On the benefits of active stock selection strategies for diversified investors," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 342-354.
  97. Bohan Ma & Yushan Xue & Yuan Lu & Jing Chen, 2023. "Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks," Papers 2401.06139, arXiv.org, revised Jun 2024.
  98. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  99. Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
  100. Alexandre Belloni & Mingli Chen & Oscar Hernan Madrid Padilla & Zixuan & Wang, 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," Papers 1912.02151, arXiv.org, revised Aug 2022.
  101. Kazuhiro Hiraki & George Skiadopoulos, 2023. "The Contribution of Transaction Costs to Expected Stock Returns: A Novel Measure," Working Papers 946, Queen Mary University of London, School of Economics and Finance.
  102. Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi & Richard Y. D. Xu, 2022. "Time‐varying neural network for stock return prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 3-18, January.
  103. Jacobs, Heiko & Müller, Sebastian, 2020. "Anomalies across the globe: Once public, no longer existent?," Journal of Financial Economics, Elsevier, vol. 135(1), pages 213-230.
  104. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
  105. Konan Chan & Mei‐Xuan Li & Chu‐Bin Lin & Yanzhi Wang, 2022. "Organization capital effect in stock returns—The role of R&D," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(7-8), pages 1237-1263, July.
  106. Martin Zurek & Lars Heinrich, 2021. "Bottom-up versus top-down factor investing: an alpha forecasting perspective," Journal of Asset Management, Palgrave Macmillan, vol. 22(1), pages 11-29, February.
  107. Wan, Runzhe & Li, Yingying & Lu, Wenbin & Song, Rui, 2024. "Mining the factor zoo: Estimation of latent factor models with sufficient proxies," Journal of Econometrics, Elsevier, vol. 239(2).
  108. Shimon Kogan & Vitaly Meursault, 2021. "Corporate Disclosure: Facts or Opinions?," Working Papers 21-40, Federal Reserve Bank of Philadelphia.
  109. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factors’ CPDAG Roots and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_18, University of São Paulo (FEA-USP).
  110. Chen, Ding & Guo, Biao & Zhou, Guofu, 2023. "Firm fundamentals and the cross-section of implied volatility shapes," Journal of Financial Markets, Elsevier, vol. 63(C).
  111. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
  112. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
  113. Yufeng Han & Dayong Huang & Guofu Zhou, 2021. "Anomalies enhanced: A portfolio rebalancing approach," Financial Management, Financial Management Association International, vol. 50(2), pages 371-424, June.
  114. Qiao, Fang, 2024. "Do analysts disseminate anomaly information in China?," Journal of Banking & Finance, Elsevier, vol. 165(C).
  115. Vu Le Tran & Guillaume Coqueret, 2023. "ESG news spillovers across the value chain," Financial Management, Financial Management Association International, vol. 52(4), pages 677-710, December.
  116. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
  117. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
  118. Liu, Chenxi & Kang, Mengyao, 2024. "Is the cash-returns relationship risk induced?," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
  119. Jiang, Hao & Li, Sophia Zhengzi & Wang, Hao, 2021. "Pervasive underreaction: Evidence from high-frequency data," Journal of Financial Economics, Elsevier, vol. 141(2), pages 573-599.
  120. Frank Schuhmacher & Hendrik Kohrs & Benjamin R. Auer, 2021. "Justifying Mean-Variance Portfolio Selection when Asset Returns Are Skewed," Management Science, INFORMS, vol. 67(12), pages 7812-7824, December.
  121. Kai Du & Xin Daniel Jiang, 2020. "Connections between the Market Pricing of Accruals Quality and Accounting‐Based Anomalies," Contemporary Accounting Research, John Wiley & Sons, vol. 37(4), pages 2087-2119, December.
  122. Hwang, Soosung & Cho, Youngha & Noh, Sanha, 2022. "The cost of overconfidence in public information," International Review of Financial Analysis, Elsevier, vol. 79(C).
  123. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
  124. Han, Chulwoo & Kang, Jangkoo & Kim, Sun Yung, 2022. "Betting against analyst target price," Journal of Financial Markets, Elsevier, vol. 59(PB).
  125. van Binsbergen, Jules H. & Boons, Martijn & Opp, Christian C. & Tamoni, Andrea, 2023. "Dynamic asset (mis)pricing: Build-up versus resolution anomalies," Journal of Financial Economics, Elsevier, vol. 147(2), pages 406-431.
  126. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
  127. Hediger, Simon & Michel, Loris & Näf, Jeffrey, 2022. "On the use of random forest for two-sample testing," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  128. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
  129. Shimizu, Hidehiko & Shiohama, Takayuki, 2020. "Constructing inverse factor volatility portfolios: A risk-based asset allocation for factor investing," International Review of Financial Analysis, Elsevier, vol. 68(C).
  130. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
  131. Lisa R. Goldberg & Saad Mouti, 2019. "Sustainable Investing and the Cross-Section of Returns and Maximum Drawdown," Papers 1905.05237, arXiv.org, revised Dec 2023.
  132. Chiang, I-Hsuan Ethan & Kirby, Chris & Nie, Ziye Zoe, 2021. "Short-term reversals, short-term momentum, and news-driven trading activity," Journal of Banking & Finance, Elsevier, vol. 125(C).
  133. Byun, Suk-Joon & Cho, Sangheum & Kim, Da-Hea, 2024. "Can a machine learn from behavioral biases? Evidence from stock return predictability of deep learning models," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
  134. Dong, Bingbing & Jiang, Lei & Liu, Jinyu & Zhu, Yifeng, 2022. "Liquidity in the cryptocurrency market and commonalities across anomalies," International Review of Financial Analysis, Elsevier, vol. 81(C).
  135. Pawel Dlotko & Wanling Qiu & Simon Rudkin, 2019. "Financial ratios and stock returns reappraised through a topological data analysis lens," Papers 1911.10297, arXiv.org.
  136. Ping Li & Jiahong Li & Dong Wang, 2024. "Anomaly Identification and Premium Mining: Evidence from Chinese Urban Construction Investment Bonds," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(4), pages 945-974, December.
  137. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
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