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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. 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.

  2. 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. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    2. 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.
    3. 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.
    4. Martin, Ian & Nagel, Stefan, 2019. "Market Efficiency in the Age of Big Data," CEPR Discussion Papers 14235, C.E.P.R. Discussion Papers.
    5. Rossi, Stefano & Weber, Michael & Michaely, Roni, 2019. "Signaling Safety," CEPR Discussion Papers 14174, C.E.P.R. Discussion Papers.
    6. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    7. 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.
    8. 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.
    9. 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.
    10. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.

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

    Cited by:

    1. 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.
    2. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
    3. 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).
    4. 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).
    5. 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).
    6. Maysam Khodayari Gharanchaei & Prabhu Prasad Panda & Xilin Chen, 2024. "Quantitative Investment Diversification Strategies via Various Risk Models," Papers 2407.01550, arXiv.org.
    7. 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).
    8. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    9. Michael Weber, 2016. "Cash Flow Duration and the Term Structure of Equity Returns," NBER Working Papers 22520, National Bureau of Economic Research, Inc.
    10. Chris Florackis & Christodoulos Louca & Roni Michaely & Michael Weber, 2020. "Cybersecurity Risk," NBER Working Papers 28196, National Bureau of Economic Research, Inc.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    20. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    21. 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.
    22. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    23. 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.
    24. 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).
    25. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    26. 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.).
    27. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    28. 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.
    29. 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.
    30. Langlois, Hugues, 2020. "Measuring skewness premia," Journal of Financial Economics, Elsevier, vol. 135(2), pages 399-424.
    31. Martin Lettau & Markus Pelger, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," NBER Working Papers 24858, National Bureau of Economic Research, Inc.
    32. 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.
    33. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    34. 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.
    35. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
    36. 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).
    37. Mohrschladt, Hannes & Nolte, Sven, 2018. "A new risk factor based on equity duration," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 126-135.
    38. 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).
    39. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    40. Nagel, Stefan & Santosh, Shrihari & Kozak, Serhiy, 2017. "Shrinking the Cross Section," CEPR Discussion Papers 12463, C.E.P.R. Discussion Papers.
    41. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
    42. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    43. 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.
    44. Penaranda, Francisco & Sentana, Enrique, 2024. "Portfolio management with big data," CEPR Discussion Papers 19314, C.E.P.R. Discussion Papers.
    45. Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
    46. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2019. "Taming the Factor Zoo: A Test of New Factors," NBER Working Papers 25481, National Bureau of Economic Research, Inc.
    47. 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.
    48. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    49. 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).
    50. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    51. Paul Schneider & Christian Wagner & Josef Zechner, 2019. "Low Risk Anomalies?," Swiss Finance Institute Research Paper Series 19-50, Swiss Finance Institute.
    52. 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.
    53. Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Monetary Momentum," CESifo Working Paper Series 6648, CESifo.
    54. 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.
    55. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    56. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    57. 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).
    58. 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.
    59. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    60. Rossi, Stefano & Weber, Michael & Michaely, Roni, 2019. "Signaling Safety," CEPR Discussion Papers 14174, C.E.P.R. Discussion Papers.
    61. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    62. Atif Ellahie, 2021. "Earnings beta," Review of Accounting Studies, Springer, vol. 26(1), pages 81-122, March.
    63. 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.
    64. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org, revised Sep 2024.
    65. 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.
    66. Damir Filipovic & Paul Schneider, 2024. "Fundamental properties of linear factor models," Papers 2409.02521, arXiv.org, revised Oct 2024.
    67. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
    68. 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.
    69. 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.
    70. Firoozye, Nikan & Tan, Vincent & Zohren, Stefan, 2023. "Canonical portfolios: Optimal asset and signal combination," Journal of Banking & Finance, Elsevier, vol. 154(C).
    71. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    72. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    73. 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).
    74. 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).
    75. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
    76. 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.
    77. Bagnara, Matteo & Goodarzi, Milad, 2023. "Clustering-based sector investing," SAFE Working Paper Series 397, Leibniz Institute for Financial Research SAFE.
    78. 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.
    79. Bryzgalova, Svetlana & Huang, Jiantao & Julliard, Christian, 2020. "Bayesian solutions for the factor zoo: we just ran two quadrillion models," LSE Research Online Documents on Economics 118924, London School of Economics and Political Science, LSE Library.
    80. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    81. 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.
    82. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    83. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
    84. 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.
    85. 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.
    86. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
    87. 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.
    88. 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.
    89. 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.
    90. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    91. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
    92. 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.
    93. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    94. 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.
    95. 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.
    96. 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.
    97. 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.
    98. 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.
    99. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    100. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    101. 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).
    102. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
    103. 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).
    104. 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).
    105. 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.
    106. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    107. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    108. 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.
    109. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
    110. 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.
    111. 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).
    112. 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).
    113. B. Li & S. Boubaker & Z. Liu & W. Louhichi & Y. Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Post-Print hal-04435519, HAL.
    114. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    115. Tu, Xueyong & Li, Bin, 2024. "Robust portfolio selection with smart return prediction," Economic Modelling, Elsevier, vol. 135(C).
    116. 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).
    117. Kelly, Bryan T. & Moskowitz, Tobias J. & Pruitt, Seth, 2021. "Understanding momentum and reversal," Journal of Financial Economics, Elsevier, vol. 140(3), pages 726-743.
    118. 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.
    119. Bagnara, Matteo, 2024. "The economic value of cross-predictability: A performance-based measure," SAFE Working Paper Series 424, Leibniz Institute for Financial Research SAFE.
    120. 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.
    121. 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.
    122. 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.
    123. 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.
    124. Kang, Yong Joo & Park, Dojoon & Eom, Young Ho, 2024. "Global contagion of US COVID-19 panic news," Emerging Markets Review, Elsevier, vol. 59(C).
    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. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    127. 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.
    128. 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.
    129. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
    130. 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.
    131. 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).
    132. 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).
    133. 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.
    134. 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.
    135. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
    136. 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.).
    137. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    138. 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.

  4. 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. Semyon Malamud & Andreas Schrimpf, 2016. "Intermediation Markups and Monetary Policy Passthrough," Swiss Finance Institute Research Paper Series 16-75, Swiss Finance Institute.
    5. Lettau, Martin & Ludvigson, Sydney & Bianchi, Francesco, 2018. "Monetary Policy and Asset Valuation," CEPR Discussion Papers 12671, C.E.P.R. Discussion Papers.
    6. Ali Ozdagli & Michael Weber & Michael Weber, 2017. "Monetary Policy through Production Networks: Evidence from the Stock Market," CESifo Working Paper Series 6486, CESifo.
    7. Lakdawala, Aeimit & Schaffer, Matthew, 2019. "Federal reserve private information and the stock market," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 34-49.
    8. 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.
    9. Hüning, Hendrik, 2020. "Swiss National Bank communication and investors’ uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    10. 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. 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.
    3. Jozef Barunik & Josef Kurka, 2021. "Risks of heterogeneously persistent higher moments," Papers 2104.04264, arXiv.org, revised Mar 2024.
    4. Jozef Barunik & Lukas Vacha, 2023. "The Dynamic Persistence of Economic Shocks," Papers 2306.01511, arXiv.org.

  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. Penaranda, Francisco & Sentana, Enrique, 2024. "Portfolio management with big data," CEPR Discussion Papers 19314, C.E.P.R. Discussion Papers.
    3. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    4. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.

  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. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    2. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    3. 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. Schmeling, Maik & Schrimpf, Paul & Kroencke, Tim, 2019. "The FOMC Risk Shift," CEPR Discussion Papers 14037, C.E.P.R. Discussion Papers.
    2. Yuriy Gorodnichenko & Tho Pham & Oleksandr Talavera, 2023. "The Voice of Monetary Policy," American Economic Review, American Economic Association, vol. 113(2), pages 548-584, February.
    3. 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.
    4. 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.
    5. 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).
    6. Fadda, Pietro & Hanifi, Rayane & Istrefi, Klodiana & Penalver, Adrian, 2022. "Central Bank Communication of Uncertainty," CEPR Discussion Papers 17728, C.E.P.R. Discussion Papers.
    7. Jung, Alexander & Kühl, Patrick, 2021. "Can central bank communication help to stabilise inflation expectations?," Working Paper Series 2547, European Central Bank.
    8. 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).
    9. 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.
    10. 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.
    11. D'Acunto, Francesco & Hoang, Daniel & Paloviita, Maritta & Weber, Michael, 2020. "Effective policy communication: Targets versus instruments," Bank of Finland Research Discussion Papers 17/2020, Bank of Finland.
    12. Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Monetary Momentum," CESifo Working Paper Series 6648, CESifo.
    13. 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.
    14. 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.
    15. Klodiana Istrefi & Florens Odendahl & Giulia Sestieri, 2022. "ECB Communication and its Impact on Financial Markets," Working papers 859, Banque de France.
    16. 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.
    17. 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).
    18. Jung, Alexander, 2023. "Are monetary policy shocks causal to bank health? Evidence from the euro area," Journal of Macroeconomics, Elsevier, vol. 75(C).
    19. Istrefi, Klodiana & Odendahl, Florens & Sestieri, Giulia, 2022. "Fed Communication on Financial Stability Concerns and Monetary Policy Decisions: Revelations from Speeches," CEPR Discussion Papers 17671, C.E.P.R. Discussion Papers.
    20. Yu, Zhen & Liu, Wei & Yang, Fuyu, 2023. "A central bankers’ sentiment index of global financial cycle," Finance Research Letters, Elsevier, vol. 57(C).
    21. Ge Gao & Alex Nikolsko‐Rzhevskyy & Oleksandr Talavera, 2023. "Can central banks be heard over the sound of gunfire?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(S1), pages 183-203, December.
    22. Hüpper, Florian & Kempa, Bernd, 2023. "Inflation targeting and inflation communication of the Federal Reserve: Words and deeds," Journal of Macroeconomics, Elsevier, vol. 75(C).
    23. Liu, Hong & Tang, Xiaoxiao & Zhou, Guofu, 2022. "Recovering the FOMC risk premium," Journal of Financial Economics, Elsevier, vol. 145(1), pages 45-68.
    24. 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.
    25. Chao Ying, 2020. "The Pre-FOMC Announcement Drift and Private Information: Kyle Meets Macro-Finance," 2020 Papers pyi149, Job Market Papers.

  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. 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.
    2. 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.
    3. Stefan Feuerriegel & Nicolas Prollochs, 2018. "Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation," Papers 1805.03308, arXiv.org.
    4. 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.
    5. 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.
    6. Terrence HENDERSHOTT & Dmitry LIVDAN & Norman SCHUERHOFF, 2014. "Are Institutions Informed About News?," Swiss Finance Institute Research Paper Series 14-49, Swiss Finance Institute.
    7. Edmans, Alex & Xu, Moqi & Goncalves-Pinto, Luis & Wang, Yanbo, 2014. "Strategic News Releases in Equity Vesting Months," CEPR Discussion Papers 10144, C.E.P.R. Discussion Papers.
    8. 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.
    9. 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.
    10. 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.
    11. Khurshid Ahmad & JingGuang Han & Elaine Hutson & Colm Kearney & Sha Liu, 2016. "Media-expressed negative tone and firm-level stock returns," Open Access publications 10197/8208, Research Repository, University College Dublin.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. Frank, Murray Z. & Sanati, Ali, 2018. "How does the stock market absorb shocks?," Journal of Financial Economics, Elsevier, vol. 129(1), pages 136-153.
    18. 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.
    19. 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.
    20. 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.
    21. Wu, Zekun & Borochin, Paul & Golec, Joseph, 2024. "Informed options trading before FDA drug advisory meetings," Journal of Corporate Finance, Elsevier, vol. 84(C).
    22. 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).
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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).
    31. Kammoun, Manel & Power, Gabriel J. & Tandja M, Djerry C., 2022. "Capital market reactions to project finance loans," Finance Research Letters, Elsevier, vol. 45(C).
    32. 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.

  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. 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.
    2. Stefan Feuerriegel & Helmut Prendinger, 2018. "News-based trading strategies," Papers 1807.06824, arXiv.org.
    3. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    4. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Discussion Papers 46/2020, Deutsche Bundesbank.
    5. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 235-256.
    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. 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.
    8. 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.

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.

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