IDEAS home Printed from https://ideas.repec.org/f/c/pne394.html
   My authors  Follow this author

Andreas Neuhierl

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 6391, CESifo.

    Mentioned in:

    1. > Econometrics > Big Data

Working papers

  1. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Economic Forecasts Using Many Noises," Papers 2312.05593, arXiv.org, revised Dec 2023.

    Cited by:

    1. Yonghe Lu & Yanrong Yang & Terry Zhang, 2024. "Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy," Papers 2411.18830, arXiv.org.

  2. Andreas Neuhierl & Michael Weber, 2020. "Monetary Momentum," Working Papers 2020-39, Becker Friedman Institute for Research In Economics.

    Cited by:

    1. Farshid Abdi & Botao Wu, 2018. "Informed Corporate Credit Market Before Monetary Policy Surprises: Explaining Pre-FOMC Stock Market Movements," Working Papers on Finance 1828, University of St. Gallen, School of Finance.

  3. Alexander M. Chinco & Andreas Neuhierl & Michael Weber, 2019. "Estimating The Anomaly Base Rate," NBER Working Papers 26493, National Bureau of Economic Research, Inc.

    Cited by:

    1. Alex Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2022. "A New Test of Risk Factor Relevance," Journal of Finance, American Finance Association, vol. 77(4), pages 2183-2238, August.
    2. Cujean, Julien & Andrei, Daniel & Fournier, Mathieu, 2019. "The Low-Minus-High Portfolio and the Factor Zoo," CEPR Discussion Papers 14153, C.E.P.R. Discussion Papers.
    3. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    4. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    5. Rossi, Stefano & Weber, Michael & Michaely, Roni, 2019. "Signaling Safety," CEPR Discussion Papers 14174, C.E.P.R. Discussion Papers.
    6. 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.
    7. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    8. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    9. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    10. 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.
    11. Matteo Bagnara, 2024. "Asset Pricing and Machine Learning: A critical review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 27-56, February.

  4. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 6391, CESifo.

    Cited by:

    1. Cakici, Nusret & Zaremba, Adam, 2024. "What drives stock returns across countries? Insights from machine learning models," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    2. 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).
    3. Alexander M. Chinco & Andreas Neuhierl & Michael Weber, 2019. "Estimating The Anomaly Base Rate," NBER Working Papers 26493, National Bureau of Economic Research, Inc.
    4. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    5. Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.
    6. Paul Schneider & Christian Wagner & Josef Zechner, 2019. "Low Risk Anomalies?," Swiss Finance Institute Research Paper Series 19-50, Swiss Finance Institute.
    7. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
    8. Bandi, Federico M. & Chaudhuri, Shomesh E. & Lo, Andrew W. & Tamoni, Andrea, 2021. "Spectral factor models," Journal of Financial Economics, Elsevier, vol. 142(1), pages 214-238.
    9. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
    10. Andreas Neuhierl & Michael Weber, 2020. "Monetary Momentum," Working Papers 2020-39, Becker Friedman Institute for Research In Economics.
    11. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
    12. Alex Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2022. "A New Test of Risk Factor Relevance," Journal of Finance, American Finance Association, vol. 77(4), pages 2183-2238, August.
    13. Jinghai He & Cheng Hua & Chunyang Zhou & Zeyu Zheng, 2025. "Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information," Papers 2501.17992, arXiv.org.
    14. Bryzgalova, Svetlana & Huang, Jiantao & Julliard, Christian, 2023. "Bayesian solutions for the factor zoo: we just ran two quadrillion models," LSE Research Online Documents on Economics 126151, London School of Economics and Political Science, LSE Library.
    15. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    16. 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).
    17. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
    18. Molero-González, L. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A. & García-Medina, A., 2023. "Market Beta is not dead: An approach from Random Matrix Theory," Finance Research Letters, Elsevier, vol. 55(PA).
    19. Angelidis, Timotheos & Sakkas, Athanasios & Tessaromatis, Nikolaos, 2025. "Predicting commodity returns: Time series vs. cross sectional prediction models," Journal of Commodity Markets, Elsevier, vol. 38(C).
    20. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    21. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    22. Kent Daniel & David Hirshleifer & Lin Sun, 2020. "Short- and Long-Horizon Behavioral Factors," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1673-1736.
    23. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    24. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    25. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    26. Qian, Yihe & Zhang, Yang, 2025. "Long-term forecasting in asset pricing: Machine learning models’ sensitivity to macroeconomic shifts and firm-specific factors," The North American Journal of Economics and Finance, Elsevier, vol. 78(C).
    27. Maysam Khodayari Gharanchaei & Prabhu Prasad Panda & Xilin Chen, 2024. "Quantitative Investment Diversification Strategies via Various Risk Models," Papers 2407.01550, arXiv.org.
    28. Christian Fieberg & Lars Hornuf & Gerrit Liedtke & Thorsten Poddig, 2020. "Are Characteristics Covariances? A Comment on Instrumented Principal Component Analysis," CESifo Working Paper Series 8377, CESifo.
    29. David A. Mascio & Marat Molyboga & Frank J. Fabozzi, 2023. "The battle of the factors: Macroeconomic variables or investor sentiment?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2280-2291, December.
    30. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2020. "Estimation of large dimensional conditional factor models in finance," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 219-282, Elsevier.
    31. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
    32. 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.
    33. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    34. O’Sullivan, Conall & Papavassiliou, Vassilios G. & Wafula, Ronald Wekesa & Boubaker, Sabri, 2024. "New insights into liquidity resiliency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    35. 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.
    36. Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
    37. Kwon, Tae Yeon, 2025. "Feature importance in linear models with ensemble machine learning: A study of the Fama and French five-factor model," Finance Research Letters, Elsevier, vol. 71(C).
    38. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    39. Bo Li & Sabri Boubaker & Zhenya Liu & Waël Louhichi & Yao Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 527-559, August.
    40. Dohyun Chun & Jongho Kang & Jihun Kim, 2024. "Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
    41. Lettau, Martin & Pelger, Markus, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," CEPR Discussion Papers 13049, C.E.P.R. Discussion Papers.
    42. Michael Weber, 2016. "Cash Flow Duration and the Term Structure of Equity Returns," NBER Working Papers 22520, National Bureau of Economic Research, Inc.
    43. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    44. Sak, Halis & Huang, Tao & Chng, Michael T., 2024. "Exploring the factor zoo with a machine-learning portfolio," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    45. 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.
    46. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    47. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
    48. 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.
    49. Chris Florackis & Christodoulos Louca & Roni Michaely & Michael Weber, 2020. "Cybersecurity Risk," NBER Working Papers 28196, National Bureau of Economic Research, Inc.
    50. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    51. Dong, C. & Li, S., 2021. "Specification Lasso and an Application in Financial Markets," Cambridge Working Papers in Economics 2139, Faculty of Economics, University of Cambridge.
    52. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," CEMA Working Papers 678, China Economics and Management Academy, Central University of Finance and Economics.
    53. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    54. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
    55. Rossi, Stefano & Weber, Michael & Michaely, Roni, 2019. "Signaling Safety," CEPR Discussion Papers 14174, C.E.P.R. Discussion Papers.
    56. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
    57. Yuxiao Jiao & Guofu Zhou & Wu Zhu & Yingzi Zhu, 2025. "Interpretable Factors of Firm Characteristics," Papers 2508.02253, arXiv.org.
    58. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    59. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    60. Atif Ellahie, 2021. "Earnings beta," Review of Accounting Studies, Springer, vol. 26(1), pages 81-122, March.
    61. Kang, Yong Joo & Park, Dojoon & Eom, Young Ho, 2024. "Global contagion of US COVID-19 panic news," Emerging Markets Review, Elsevier, vol. 59(C).
    62. 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).
    63. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2025. "Volatility forecasting and volatility-timing strategies: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 75(C).
    64. Dreher, Sandra & Eichfelder, Sebastian & Noth, Felix, 2022. "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," arqus Discussion Papers in Quantitative Tax Research 276, arqus - Arbeitskreis Quantitative Steuerlehre.
    65. Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
    66. 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.
    67. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org, revised Sep 2024.
    68. 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.
    69. 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.
    70. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    71. Damir Filipovic & Paul Schneider, 2024. "Fundamental properties of linear factor models," Papers 2409.02521, arXiv.org, revised Feb 2025.
    72. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    73. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
    74. 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.
    75. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.
    76. Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
    77. Shirui Wang & Tianyang Zhang, 2024. "Predictability of commodity futures returns with machine learning models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 302-322, February.
    78. Marcos López de Prado & Joseph Simonian & Francesco A. Fabozzi & Frank J. Fabozzi, 2025. "Enhancing Markowitz's portfolio selection paradigm with machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 319-340, March.
    79. Firoozye, Nikan & Tan, Vincent & Zohren, Stefan, 2023. "Canonical portfolios: Optimal asset and signal combination," Journal of Banking & Finance, Elsevier, vol. 154(C).
    80. 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.
    81. Rafael Branco & Alexandre Rubesam & Mauricio Zevallos, 2024. "Forecasting realized volatility: Does anything beat linear models?," Post-Print hal-04835657, HAL.
    82. Fabrizio Ghezzi & Anindo Sarkar & Thomas Quistgaard Pedersen & Allan Timmermann, 2025. "Optimal asset allocation and nonlinear return predictability from the dividend-price ratio," Annals of Operations Research, Springer, vol. 346(1), pages 415-445, March.
    83. Pesce, Simone & Errico, Marco & Pollio, Luigi, 2025. "Nonlinearities and heterogeneity in firms response to aggregate fluctuations: what can we learn from machine learning?," Working Paper Series 3107, European Central Bank.
    84. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
    85. Li, Zhiyong & Wan, Yifan & Wang, Tianyi & Yu, Mei, 2023. "Factor-timing in the Chinese factor zoo: The role of economic policy uncertainty," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    86. 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).
    87. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    88. Cong Wang, 2024. "Stock return prediction with multiple measures using neural network models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    89. 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.
    90. Cong, Lin William & George, Nathan Darden & Wang, Guojun, 2023. "RIM-based value premium and factor pricing using value-price divergence," Journal of Banking & Finance, Elsevier, vol. 149(C).
    91. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    92. 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.
    93. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    94. Yonghe Lu & Yanrong Yang & Terry Zhang, 2024. "Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy," Papers 2411.18830, arXiv.org.
    95. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    96. 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.
    97. 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).
    98. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
    99. Deshui Yu & Yayi Yan, 2023. "Joint dynamics of stock returns and cash flows: A time‐varying present‐value framework," Financial Management, Financial Management Association International, vol. 52(3), pages 513-541, September.
    100. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    101. Gehrig, Thomas & Sögner, Leopold, 2022. "Extending the Demand System Approach to Asset Pricing," CEPR Discussion Papers 17743, C.E.P.R. Discussion Papers.
    102. Celso Brunetti & Marc Joëts & Valérie Mignon, 2024. "Reasons Behind Words: OPEC Narratives and the Oil Market," Finance and Economics Discussion Series 2024-003, Board of Governors of the Federal Reserve System (U.S.).
    103. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    104. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    105. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    106. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    107. Molero González, Laura & Cerqueti, Roy & Mattera, Raffaele & Sánchez Granero, Miguel Ángel & Trinidad Segovia, Juan Evangelista, 2025. "Analyzing clustered factors in the cryptocurrency market with Random Matrix Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 665(C).
    108. Antonio Garcia-Amate & Laura Molero-González & Miguel Angel Sánchez-Granero & Juan Evangelista Trinidad-Segovia & Andres García-Medina, 2024. "Testing the significance of pricing factors of oil and gas companies," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-18, December.
    109. Caio Vigo Pereira, 2020. "Portfolio Efficiency with High-Dimensional Data as Conditioning Information," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202015, University of Kansas, Department of Economics, revised Sep 2020.
    110. Bagnara, Matteo & Goodarzi, Milad, 2023. "Clustering-based sector investing," SAFE Working Paper Series 397, Leibniz Institute for Financial Research SAFE.
    111. 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.).
    112. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    113. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
    114. Alessi, Lucia & Balduzzi, Pierluigi & Savona, Roberto, 2019. "Anatomy of a Sovereign Debt Crisis: CDS Spreads and Real-Time Macroeconomic Data," JRC Working Papers in Economics and Finance 2019-03, Joint Research Centre, European Commission.
    115. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    116. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    117. Wolfgang Breuer & Andreas Knetsch, 2023. "Recent trends in the digitalization of finance and accounting," Journal of Business Economics, Springer, vol. 93(9), pages 1451-1461, November.
    118. Langlois, Hugues, 2020. "Measuring skewness premia," Journal of Financial Economics, Elsevier, vol. 135(2), pages 399-424.
    119. Auer, Benjamin R. & Schuhmacher, Frank & Niemann, Sebastian, 2023. "Cloning mutual fund returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 31-37.
    120. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    121. Avramov, D. & Ge, S. & Li, S. & Linton, O. B., 2025. "Dual Industry Effects and Cross-Stock Predictability," Janeway Institute Working Papers 2506, Faculty of Economics, University of Cambridge.
    122. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    123. Shunyao Wang & Ming Cheng & Christina Dan Wang, 2025. "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks," Papers 2505.06864, arXiv.org.
    124. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
    125. Yuhan Cheng & Heyang Zhou & Yanchu Liu, 2025. "Large Language Models and Futures Price Factors in China," Papers 2509.23609, arXiv.org.
    126. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    127. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    128. Tian, Guangning & Peng, Yuchao & Du, Huancheng & Meng, Yuhao, 2024. "Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?," Energy Economics, Elsevier, vol. 139(C).
    129. Alois Weigand, 2019. "Machine learning in empirical asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 93-104, March.
    130. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    131. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    132. Madhura Dasgupta & Samarth Gupta, 2024. "What Determines Enterprise Borrowing from Self Help Groups? An Interpretable Supervised Machine Learning Approach," Journal of Financial Services Research, Springer;Western Finance Association, vol. 66(1), pages 77-99, August.
    133. 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.
    134. Huang, Dashan & Li, Jiangyuan & Wang, Liyao, 2021. "Are disagreements agreeable? Evidence from information aggregation," Journal of Financial Economics, Elsevier, vol. 141(1), pages 83-101.
    135. Wang, Chuyu & Zhang, Guanglong, 2025. "In the shadows of opacity: Firm information quality and latent factor model performance," International Review of Financial Analysis, Elsevier, vol. 100(C).
    136. Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
    137. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    138. Jia, Yuecheng & Wu, Yangru & Yan, Shu & Liu, Yuzheng, 2023. "A seesaw effect in the cryptocurrency market: Understanding the return cross predictability of cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 74(C).
    139. 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).
    140. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam & Cakici, Nusret, 2025. "A factor model for the cross-section of country equity risk premia," Journal of Banking & Finance, Elsevier, vol. 171(C).
    141. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    142. Liu, Tingting & Lu, Zhongjin (Gene) & Shu, Tao & Wei, Fengrong, 2022. "Unique bidder-target relatedness and synergies creation in mergers and acquisitions," Journal of Corporate Finance, Elsevier, vol. 73(C).
    143. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Linda Schilling, 2025. "The Uncertainty of Machine Learning Predictions in Asset Pricing," Papers 2503.00549, arXiv.org.
    144. Mohrschladt, Hannes & Nolte, Sven, 2018. "A new risk factor based on equity duration," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 126-135.
    145. 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).
    146. Hongyi Liu, 2025. "Deep Learning for Conditional Asset Pricing Models," Papers 2509.04812, arXiv.org.
    147. 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.
    148. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    149. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
    150. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
    151. 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.
    152. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-Learning the Skill of Mutual Fund Managers," CEPR Discussion Papers 18129, C.E.P.R. Discussion Papers.
    153. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2025. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR), revised 2025.
    154. Shanyan Lai, 2025. "Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks," Papers 2505.01921, arXiv.org, revised May 2025.
    155. Bryan Kelly & Semyon Malamud & Kangying Zhou, 2024. "The Virtue of Complexity in Return Prediction," Journal of Finance, American Finance Association, vol. 79(1), pages 459-503, February.
    156. Guilherme V. Moura & Andr'e P. Santos & Hudson S. Torrent, 2025. "Variable selection for minimum-variance portfolios," Papers 2508.14986, arXiv.org.
    157. Bang, Jeongseok & Kang, Yeonchan & Ryu, Doojin, 2024. "Potential pricing factors in the Korean market," Finance Research Letters, Elsevier, vol. 67(PB).
    158. Jian'an Zhang, 2025. "FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management," Papers 2510.02986, arXiv.org.
    159. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    160. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
    161. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    162. Tu, Xueyong & Li, Bin, 2024. "Robust portfolio selection with smart return prediction," Economic Modelling, Elsevier, vol. 135(C).
    163. 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).
    164. Penaranda, Francisco & Sentana, Enrique, 2024. "Portfolio management with big data," CEPR Discussion Papers 19314, C.E.P.R. Discussion Papers.
    165. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J. & Uppal, Raman, 2017. "A Portfolio Perspective on the Multitude of Firm Characteristics," CEPR Discussion Papers 12417, C.E.P.R. Discussion Papers.
    166. Matteo Bagnara, 2024. "Asset Pricing and Machine Learning: A critical review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 27-56, February.
    167. Kelly, Bryan T. & Moskowitz, Tobias J. & Pruitt, Seth, 2021. "Understanding momentum and reversal," Journal of Financial Economics, Elsevier, vol. 140(3), pages 726-743.
    168. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
    169. 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.
    170. Andrew Y. Chen & Mihail Velikov, 2020. "Zeroing in on the Expected Returns of Anomalies," Finance and Economics Discussion Series 2020-039, Board of Governors of the Federal Reserve System (U.S.).
    171. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    172. Haixiang Yao & Shenghao Xia & Hao Liu, 2024. "Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1770-1794, September.
    173. Avramov, D. & Ge, S. & Li, S. & Linton, O. B., 2025. "Dual Industry Effects and Cross-Stock Predictability," Cambridge Working Papers in Economics 2512, Faculty of Economics, University of Cambridge.
    174. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    175. Jing-Zhi Huang & Zhan Shi, 2023. "Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance," Management Science, INFORMS, vol. 69(3), pages 1780-1804, March.
    176. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    177. Bagnara, Matteo, 2024. "The economic value of cross-predictability: A performance-based measure," SAFE Working Paper Series 424, Leibniz Institute for Financial Research SAFE.
    178. Lu, Zhongjin & Malliaris, Steven & Qin, Zhongling, 2023. "Heterogeneous liquidity providers and night-minus-day return predictability," Journal of Financial Economics, Elsevier, vol. 148(3), pages 175-200.

  5. Andreas Neuhierl & Michael Weber & Michael Weber, 2016. "Monetary Policy and the Stock Market: Time-Series Evidence," CESifo Working Paper Series 6199, CESifo.

    Cited by:

    1. Schmeling, Maik & Schrimpf, Paul & Kroencke, Tim, 2019. "The FOMC Risk Shift," CEPR Discussion Papers 14037, C.E.P.R. Discussion Papers.
    2. Eksi, Ozan & Tas, Bedri Kamil Onur, 2017. "Unconventional monetary policy and the stock market’s reaction to Federal Reserve policy actions," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 136-147.
    3. Ali Ozdagli & Mihail Velikov, 2016. "Show me the money: the monetary policy risk premium," Working Papers 16-27, Federal Reserve Bank of Boston.
    4. Andreas Schrimpf & Semyon Malamud, 2017. "Intermediation Markups and Monetary Policy Passthrough," 2017 Meeting Papers 812, Society for Economic Dynamics.
    5. Ali Ozdagli & Michael Weber & Michael Weber, 2017. "Monetary Policy through Production Networks: Evidence from the Stock Market," CESifo Working Paper Series 6486, CESifo.
    6. Lakdawala, Aeimit & Schaffer, Matthew, 2019. "Federal reserve private information and the stock market," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 34-49.
    7. Caporin, Massimiliano & Pelizzon, Loriana & Plazzi, Alberto, 2020. "Does monetary policy impact international market co-movements?," SAFE Working Paper Series 276, Leibniz Institute for Financial Research SAFE.
    8. Bianchi, Francesco & Lettau, Martin & Ludvigson, Sydney, 2017. "Monetary Policy and Asset Valuation," CEPR Discussion Papers 12275, C.E.P.R. Discussion Papers.
    9. Chopra, Ritika & Sharma, Gagan Deep & Pereira, Vijay, 2024. "Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction," Technovation, Elsevier, vol. 135(C).
    10. Hüning, Hendrik, 2020. "Swiss National Bank communication and investors’ uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    11. Peter Tillmann, 2020. "Financial Markets and Dissent in the ECB’s Governing Council," MAGKS Papers on Economics 202048, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

Articles

  1. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.

    Cited by:

    1. Louis R. Piccotti, 2022. "Portfolio returns and consumption growth covariation in the frequency domain, real economic activity, and expected returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 45(3), pages 513-549, September.
    2. Jozef Barunik & Josef Kurka, 2021. "Risks of heterogeneously persistent higher moments," Papers 2104.04264, arXiv.org, revised Mar 2024.
    3. Bandi, Federico M. & Su, Yinan, 2025. "Conditional spectral methods," Journal of Econometrics, Elsevier, vol. 248(C).
    4. Brennan, M.J. & Taylor, Alex P., 2023. "Expected returns and risk in the stock market," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 276-300.
    5. Jozef Barunik & Lukas Vacha, 2023. "The Dynamic Persistence of Economic Shocks," Papers 2306.01511, arXiv.org, revised Jun 2025.
    6. Stein, Tobias, 2024. "Forecasting the equity premium with frequency-decomposed technical indicators," International Journal of Forecasting, Elsevier, vol. 40(1), pages 6-28.
    7. Yan, Han & Liu, Bin & Zhu, Xingting & Wu, Yan, 2024. "Systemic risk monitoring model from the perspective of public information arrival," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

  2. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    See citations under working paper version above.
  3. Soohun Kim & Robert A Korajczyk & Andreas Neuhierl & Wei JiangEditor, 2021. "Arbitrage Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2813-2856.

    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. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    3. Ruofan Xu & Qingliang Fan, 2025. "Single-Index Quantile Factor Model with Observed Characteristics," Papers 2506.19586, arXiv.org.
    4. Daniele Massacci & Lucio Sarno & Lorenzo Trapani & Pierluigi Vallarino, 2025. "A general randomized test for Alpha," Papers 2507.17599, arXiv.org.
    5. Shu, Lei & Hao, Yifan & Chen, Yu & Yang, Qing, 2025. "SFQRA: Scaled factor-augmented quantile regression with aggregation in conditional mean forecasting," Journal of Multivariate Analysis, Elsevier, vol. 207(C).
    6. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    7. Xu, R. & Fan, Q., 2025. "Single-Index Quantile Factor Model with Observed Characteristics," Janeway Institute Working Papers 2524, Faculty of Economics, University of Cambridge.
    8. Lin William Cong & Guanhao Feng & Jingyu He & Xin He, 2022. "Growing the Efficient Frontier on Panel Trees," NBER Working Papers 30805, National Bureau of Economic Research, Inc.
    9. Xu, R. & Fan, Q., 2025. "Single-Index Quantile Factor Model with Observed Characteristics," Cambridge Working Papers in Economics 2562, Faculty of Economics, University of Cambridge.
    10. Penaranda, Francisco & Sentana, Enrique, 2024. "Portfolio management with big data," CEPR Discussion Papers 19314, C.E.P.R. Discussion Papers.
    11. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.

  4. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.

    Cited by:

    1. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    2. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    3. Mahtab Athari & Atsuyuki Naka & Abdullah Noman, 2025. "Forecasting stock returns with sum-of-the-parts methodology: international evidence," Journal of Asset Management, Palgrave Macmillan, vol. 26(1), pages 91-114, February.
    4. Ana Sofia Monteiro & Helder Sebastião & Nuno Silva, 2025. "Prediction and Allocation of Stocks, Bonds, and REITs in the US Market," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1191-1230, March.
    5. Leandro dos Santos Maciel & Ricardo Franceli da Silva, 2025. "Market Efficiency and Equity Risk Premium Predictability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 3064-3091, July.
    6. Ciner, Cetin, 2022. "Predicting the equity market risk premium: A model selection approach," Economics Letters, Elsevier, vol. 215(C).

  5. 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.
    See citations under working paper version above.
  6. Neuhierl, Andreas & Weber, Michael, 2019. "Monetary policy communication, policy slope, and the stock market," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 140-155.

    Cited by:

    1. Yuriy Gorodnichenko & Tho Pham & Oleksandr Talavera, 2021. "The Voice of Monetary Policy," Discussion Papers 21-02, Department of Economics, University of Birmingham.
    2. Schmeling, Maik & Schrimpf, Paul & Kroencke, Tim, 2019. "The FOMC Risk Shift," CEPR Discussion Papers 14037, C.E.P.R. Discussion Papers.
    3. Andreas Neuhierl & Michael Weber, 2020. "Monetary Momentum," Working Papers 2020-39, Becker Friedman Institute for Research In Economics.
    4. Samuel M. Hartzmark & David H. Solomon, 2025. "Market-Wide Predictable Price Pressure," American Economic Review, American Economic Association, vol. 115(9), pages 3171-3213, September.
    5. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-Francois, 2020. "Central banks in parliaments: a text analysis of the parliamentary hearings of the Bank of England, the European Central Bank and the Federal Reserve," Working Paper Series 2442, European Central Bank.
    6. Liu, Hong & Tang, Xiaoxiao & Zhou, Guofu, 2022. "Recovering the FOMC risk premium," Journal of Financial Economics, Elsevier, vol. 145(1), pages 45-68.
    7. Martin T. Bohl & Dimitrios Kanelis & Pierre L. Siklos, 2022. "How Central Bank Mandates Influence Content and Tone of Communication Over Time," CQE Working Papers 9622, Center for Quantitative Economics (CQE), University of Muenster.
    8. Alessandro Casini & Adam McCloskey, 2025. "Identification, Estimation and Inference in High-Frequency Event Study Regressions," CEIS Research Paper 608, Tor Vergata University, CEIS, revised 28 Jul 2025.
    9. Gómez-Cram, Roberto & Grotteria, Marco, 2022. "Real-time price discovery via verbal communication: Method and application to Fedspeak," Journal of Financial Economics, Elsevier, vol. 143(3), pages 993-1025.
    10. Ahrens, Maximilian & Erdemlioglu, Deniz & Mcmahon, Michael & Neely, Christopher J & Yang, Xiye, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," CEPR Discussion Papers 18191, C.E.P.R. Discussion Papers.
    11. Frankie Chau & Rataporn Deesomsak & Raja Shaikh, 2025. "Does Fed communication affect uncertainty and risk aversion?," Review of Quantitative Finance and Accounting, Springer, vol. 64(2), pages 713-756, February.
    12. Linas Jurkšas & Rokas Kaminskas & Deimantė Vasiliauskaitė, 2024. "ECB monetary policy communication events: Do they move euro area yields?," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 596-625, April.
    13. Hunter Ng, 2024. "Strategic Control of Facial Expressions by the Fed Chair," Papers 2410.20214, arXiv.org.
    14. Klodiana Istrefi & Florens Odendahl & Giulia Sestieri, 2021. "Fed communication on financial stability concerns and monetary policy decisions: revelations from speeches," Working Papers 2110, Banco de España.
    15. Granziera, Eleanora & Larsen, Wegard H. & Meggiorini, Greta & Melosi, Leonardo, 2025. "Speaking of Inflation : The Influence of Fed Speeches on Expectations," The Warwick Economics Research Paper Series (TWERPS) 1555, University of Warwick, Department of Economics.
    16. Dimitrios Kanelis & Pierre L. Siklos, 2025. "The ECB press conference statement: deriving a new sentiment indicator for the euro area," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 652-664, January.
    17. Han, Xun & Ma, Sichao & Peng, Yuchao & Xie, Xinyan, 2022. "Central bank communication, corporate maturity mismatch and innovation," International Review of Financial Analysis, Elsevier, vol. 84(C).
    18. Fadda, Pietro & Hanifi, Rayane & Istrefi, Klodiana & Penalver, Adrian, 2022. "Central Bank Communication of Uncertainty," CEPR Discussion Papers 17728, C.E.P.R. Discussion Papers.
    19. Klodiana Istrefi & Florens Odendahl & Giulia Sestieri, 2022. "ECB Communication and its Impact on Financial Markets," Working papers 859, Banque de France.
    20. Eleonora Granziera & Vegard H. Larsen & Greta Meggiorini & Leonardo Melosi, 2025. "Speaking Of Inflation: The Influence Of Fed Speeches On Expectations," Working Papers No 07/2025, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    21. Ibrahim Ayoade Adekunle & Anthony Emeka Elekeokwuri & Serifat Olukorede Onayemi, 2020. "Stability in Stock Market Prices and Monetary Policy in Nigeria; What Does the Empirics Say?," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 2-13, August.
    22. Jung, Alexander & Kühl, Patrick, 2021. "Can central bank communication help to stabilise inflation expectations?," Working Paper Series 2547, European Central Bank.
    23. D'Acunto, Francesco & Hoang, Daniel & Paloviita, Maritta & Weber, Michael, 2021. "Effective policy communication: Targets versus instruments," Working Paper Series in Economics 147, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    24. Paul Hubert & Rose Portier, 2025. "The Signaling Effects of Tightening and Easing Monetary Policy," Working papers 999, Banque de France.
    25. Ma, Chaoqun & Tian, Yonggang & Hsiao, Shisong & Deng, Liurui, 2022. "Monetary policy shocks and Bitcoin prices," Research in International Business and Finance, Elsevier, vol. 62(C).
    26. Ge Gao & Alex Nikolsko-Rzhevskyy & Oleksandr Talavera, 2023. "Can Central Banks Be Heard Over the Sound of Gunfire?," Discussion Papers 23-09, Department of Economics, University of Birmingham.
    27. Chao Ying, 2020. "The Pre-FOMC Announcement Drift and Private Information: Kyle Meets Macro-Finance," 2020 Papers pyi149, Job Market Papers.
    28. Michael McMahon, 2024. "Lessons for Monetary Policy Communication: Communication, Getting Through and Expectation Formation," RBA Annual Conference Papers acp2024-01, Reserve Bank of Australia, revised May 2025.
    29. Moench, Emanuel & Stein, Tobias, 2019. "Comment on “Monetary Policy Communication, Policy Slope, and the Stock Market” by Andreas Neuhierl and Michael Weber," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 156-161.
    30. Bohl, Martin T. & Kanelis, Dimitrios & Siklos, Pierre L., 2023. "Central bank mandates: How differences can influence the content and tone of central bank communication," Journal of International Money and Finance, Elsevier, vol. 130(C).
    31. Golez, Benjamin & Matthies, Ben, 2025. "Fed information effects: Evidence from the equity term structure," Journal of Financial Economics, Elsevier, vol. 165(C).
    32. Samuel Kaplan & Efstathios Polyzos & David Tercero-Lucas, 2025. "Crypto Listens: Asymmetric Reactions to Text-based Signals in Central Bank Communications," Working Papers 365, Red Nacional de Investigadores en Economía (RedNIE).
    33. Leombroni, Matteo & Vedolin, Andrea & Venter, Gyuri & Whelan, Paul, 2021. "Central bank communication and the yield curve," Journal of Financial Economics, Elsevier, vol. 141(3), pages 860-880.
    34. Lin, Jianhao & Fan, Jiacheng & Zhang, Yifan, 2025. "Information Dissemination and the Monetary Policy Uncertainty Premium: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 171(C).
    35. Jung, Alexander, 2023. "Are monetary policy shocks causal to bank health? Evidence from the euro area," Journal of Macroeconomics, Elsevier, vol. 75(C).
    36. Yu, Zhen & Liu, Wei & Yang, Fuyu, 2023. "A central bankers’ sentiment index of global financial cycle," Finance Research Letters, Elsevier, vol. 57(C).
    37. Anastasios Megaritis & Alexandros Kontonikas & Nikolaos Vlastakis & Athanasios Triantafyllou, 2025. "The term structure of interest rates as predictor of stock market volatility," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 3212-3229, July.
    38. Hüpper, Florian & Kempa, Bernd, 2023. "Inflation targeting and inflation communication of the Federal Reserve: Words and deeds," Journal of Macroeconomics, Elsevier, vol. 75(C).

  7. Neuhierl, Andreas & Scherbina, Anna & Schlusche, Bernd, 2013. "Market Reaction to Corporate Press Releases," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1207-1240, August.

    Cited by:

    1. Blazej Prusak & Marcin Potrykus, 2020. "Short-term Price Reaction to Involuntary Bankruptcies Filed in Bad Faith: Empirical Evidence from Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 873-889.
    2. Chen, Feilong & Choi, Sungchul & Fu, Chengbo & Nycholat, Joshua, 2021. "Too high to get it right: The effect of cannabis legalization on the performance of cannabis-related stocks," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 715-734.
    3. Alexander Kerl & Carolin Schürg & Andreas Walter, 2014. "The impact of Financial Times Deutschland news on stock prices: post-announcement drifts and inattention of investors," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 28(4), pages 409-436, November.
    4. Edmans, Alex & Goncalves-Pinto, Luis & Groen-Xu, Moqi & Wang, Yanbo, 2018. "Strategic news releases in equity vesting months," LSE Research Online Documents on Economics 88301, London School of Economics and Political Science, LSE Library.
    5. Benjamin Segal & Dan Segal, 2016. "Are managers strategic in reporting non-earnings news? Evidence on timing and news bundling," Review of Accounting Studies, Springer, vol. 21(4), pages 1203-1244, December.
    6. Brian Cadman & Richard Carrizosa & Xiaoxia Peng, 2020. "Inducement grants, hiring announcements, and adverse selection for new CEOs," Review of Accounting Studies, Springer, vol. 25(1), pages 279-312, March.
    7. Zhang, Heng-Guo & CAO, Tingting & Li, Houxuan & Xu, Tiantian, 2021. "Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence," Energy Economics, Elsevier, vol. 95(C).
    8. Valentina Lagasio & Marina Brogi, 2021. "Market reaction to banks’ interim press releases: an event study analysis," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(1), pages 95-119, March.
    9. David T. Angenendt & Bernhard Ganglmair, 2025. "Competition and the Strategic Disclosure of Innovation: Theory and Evidence from Patent Applications," CRC TR 224 Discussion Paper Series crctr224_2025_664, University of Bonn and University of Mannheim, Germany.
    10. Wu, Zekun & Borochin, Paul & Golec, Joseph, 2024. "Informed options trading before FDA drug advisory meetings," Journal of Corporate Finance, Elsevier, vol. 84(C).
    11. Stefan Feuerriegel & Nicolas Prollochs, 2018. "Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation," Papers 1805.03308, arXiv.org.
    12. Hendershott, Terrence & Livdan, Dmitry & Schürhoff, Norman, 2015. "Are institutions informed about news?," Journal of Financial Economics, Elsevier, vol. 117(2), pages 249-287.
    13. Mohamed Al Guindy & James P. Naughton & Ryan Riordan, 2024. "The evolution of corporate twitter usage," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 51(3-4), pages 819-845, March.
    14. Yang, Shanxiang & Liu, Zhechen & Wang, Xinjie, 2020. "News sentiment, credit spreads, and information asymmetry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    15. Aman, Hiroyuki & Moriyasu, Hiroshi, 2017. "Volatility and public information flows: Evidence from disclosure and media coverage in the Japanese stock market," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 660-676.
    16. Carlos Pérez Montes & Jorge E. Galán & María Bru & Julio Gálvez & Alberto García & Carlos González & Samuel Hurtado & Nadia Lavín & Eduardo Pérez Asenjo & Irene Roibás, 2023. "Systemic analysis framework for the impact of economic and financial risks," Occasional Papers 2311, Banco de España.
    17. Caglayan, Mustafa Onur & Xue, Wenjun & Zhang, Liwen, 2020. "Global investigation on the country-level idiosyncratic volatility and its determinants," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 143-160.
    18. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    19. Kuang-Hsun Shih & Fu-Ju Yang & Jhih-Ta Shih & Yi-Hsien Wang, 2020. "Patent Litigation, Competitive Dynamics, and Stock Market Volatility," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    20. Liebmann, Michael & Orlov, Alexei G. & Neumann, Dirk, 2016. "The tone of financial news and the perceptions of stock and CDS traders," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 159-175.
    21. Siikanen, Milla & Kanniainen, Juho & Valli, Jaakko, 2017. "Limit order books and liquidity around scheduled and non-scheduled announcements: Empirical evidence from NASDAQ Nordic," Finance Research Letters, Elsevier, vol. 21(C), pages 264-271.
    22. Chun-Teck Lye & Tuan-Hock Ng & Kwee-Pheng Lim & Chin-Yee Gan, 2020. "Investor protection and market reaction to unusual market activity replies," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 16(8), pages 2034-2069, July.
    23. Jeon, Yoontae & McCurdy, Thomas H. & Zhao, Xiaofei, 2022. "News as sources of jumps in stock returns: Evidence from 21 million news articles for 9000 companies," Journal of Financial Economics, Elsevier, vol. 145(2), pages 1-17.
    24. Frank, Murray Z. & Sanati, Ali, 2018. "How does the stock market absorb shocks?," Journal of Financial Economics, Elsevier, vol. 129(1), pages 136-153.
    25. Chen, Sipeng & Li, Gang, 2023. "Why does option-implied volatility forecast realized volatility? Evidence from news events," Journal of Banking & Finance, Elsevier, vol. 156(C).
    26. Edward A. E. Jones & Anthony K. Kyiu & Hao Li, 2021. "Earnings informativeness and trading frequency: Evidence from African markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1064-1086, January.
    27. Kammoun, Manel & Power, Gabriel J. & Tandja M, Djerry C., 2022. "Capital market reactions to project finance loans," Finance Research Letters, Elsevier, vol. 45(C).
    28. Anna Scherbina & Bernd Schlusche, 2016. "Economic linkages inferred from news stories and the predictability of stock returns," AEI Economics Working Papers 873600, American Enterprise Institute.
    29. Johannes Luger & Sebastian Raisch & Markus Schimmer, 2018. "Dynamic Balancing of Exploration and Exploitation: The Contingent Benefits of Ambidexterity," Organization Science, INFORMS, vol. 29(3), pages 449-470, June.
    30. Aaron J. Mandell, 2022. "The value of tunneling: Evidence from master limited partnership formations," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(1-2), pages 355-380, January.
    31. María Gutiérrez & Nino Papiashvili & Josep A. Tribó & Antonio B. Vazquez, 2020. "Managerial incentives for attracting attention," European Financial Management, European Financial Management Association, vol. 26(4), pages 896-937, September.
    32. John S. Howe & Thibaut G. Morillon, 2017. "Do Mergers and Acquisitions Affect Information Asymmetry in the Banking Sector?," NFI Working Papers 2017-WP-01, Indiana State University, Scott College of Business, Networks Financial Institute.
    33. Błażej Prusak & Marcin Potrykus, 2021. "Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland," Risks, MDPI, vol. 9(3), pages 1-14, March.
    34. Prusak Błażej & Potrykus Marcin, 2022. "Stock price reaction to an arrangement approval in restructuring proceedings – the case of Poland," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(3), pages 279-298, September.

  8. G. Bamberg & A. Neuhierl, 2012. "Growth Optimal Investment Strategy: The Impact of Reallocation Frequency and Heavy Tails," German Economic Review, Verein für Socialpolitik, vol. 13(2), pages 228-240, May.

    Cited by:

    1. Svetlozar Rachev & Stoyan Stoyanov & Stefan Mittnik & Frank J. Fabozzi & Abootaleb Shirvani, 2017. "Behavioral Finance -- Asset Prices Predictability, Equity Premium Puzzle, Volatility Puzzle: The Rational Finance Approach," Papers 1710.03211, arXiv.org, revised Feb 2020.

  9. Andreas Neuhierl & Bernd Schlusche, 2011. "Data Snooping and Market-Timing Rule Performance," Journal of Financial Econometrics, Oxford University Press, vol. 9(3), pages 550-587, Summer.

    Cited by:

    1. Kevin Rink, 2025. "The role of technical chart patterns in the early Bitcoin market: intraday evidence from the Mt.Gox transaction dataset," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-67, December.
    2. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 235-256.
    3. Andriosopoulos, Kostas & Doumpos, Michael & Papapostolou, Nikos C. & Pouliasis, Panos K., 2013. "Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 16-34.
    4. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    5. Flavio Ivo Riedlinger & João Nicolau, 2020. "The Profitability in the FTSE 100 Index: A New Markov Chain Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 61-81, March.
    6. Damian Pastor & Pavel Kisela & Viliam Kovac & Tomas Sabol & Viliam Vajda, 2015. "Application Of Market Valuation Models In Portfolio Management," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 12(1), pages 154-165, DEcember.
    7. Stefan Feuerriegel & Helmut Prendinger, 2018. "News-based trading strategies," Papers 1807.06824, arXiv.org.
    8. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Discussion Papers 46/2020, Deutsche Bundesbank.
    9. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.

Chapters

  1. Roland Eisenhuth & Dermot Murphy & Andreas Neuhierl, 2018. "Casino game markets," Chapters, in: Victor J. Tremblay & Elizabeth Schroeder & Carol Horton Tremblay (ed.), Handbook of Behavioral Industrial Organization, chapter 10, pages 257-290, Edward Elgar Publishing.

    Cited by:

    1. Daske, Thomas, 2019. "Efficient Incentives in Social Networks: "Gamification" and the Coase Theorem," EconStor Preprints 193148, ZBW - Leibniz Information Centre for Economics.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.