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Guanhao Feng
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.Working papers
- Lin William Cong & Guanhao Feng & Jingyu He & Junye Li, 2023.
"Sparse Modeling Under Grouped Heterogeneity with an Application to Asset Pricing,"
NBER Working Papers
31424, National Bureau of Economic Research, Inc.
Cited by:
- Xiong, Youlin & Shen, Jun & Yoon, Seong-Min & Dong, Xiyong, 2024. "Macroeconomic determinants of the long-term correlation between stock and exchange rate markets in China: A DCC-MIDAS-X approach considering structural breaks," Finance Research Letters, Elsevier, vol. 61(C).
- Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching," Papers 2408.12863, arXiv.org.
- 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.
Cited by:
- 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).
- Gaetan Bakalli & St'ephane Guerrier & Olivier Scaillet, 2022. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Papers 2208.00972, arXiv.org.
- Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2021. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Swiss Finance Institute Research Paper Series 21-09, Swiss Finance Institute.
- Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Post-Print hal-04325655, HAL.
- Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching," Papers 2408.12863, arXiv.org.
- 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).
- 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.
- Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
- 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.
Cited by:
- Wolfgang Breuer & Jannis Bischof & Christian Hofmann & Jochen Hundsdoerfer & Hans-Ulrich Küpper & Marko Sarstedt & Philipp Schreck & Tim Weitzel & Peter Witt, 2023. "Recent developments in Business Economics," Journal of Business Economics, Springer, vol. 93(6), pages 989-1013, August.
- 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.
- 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.
- Chiah, Mardy & Long, Huaigang & Zaremba, Adam & Umar, Zaghum, 2023. "Trade competitiveness and the aggregate returns in global stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
- 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.
- Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021.
"Measurement of factor strength: Theory and practice,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
- Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2020. "Measurement of Factor Strenght: Theory and Practice," CESifo Working Paper Series 8146, CESifo.
- Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2020. "Measurement of Factor Strength: Theory and Practice," Monash Econometrics and Business Statistics Working Papers 7/20, Monash University, Department of Econometrics and Business Statistics.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024.
"High-Dimensional Granger Causality Tests with an Application to VIX and News,"
Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
- Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022.
"A Panel Clustering Approach to Analyzing Bubble Behavior,"
Economics and Statistics Working Papers
1-2022, Singapore Management University, School of Economics.
- Yanbo Liu & Peter C. B. Phillips & Jun Yu, 2023. "A Panel Clustering Approach To Analyzing Bubble Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1347-1395, November.
- Yanbo Liu & Peter C. B. Phillips & Jun Yu, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Cowles Foundation Discussion Papers 2323, Cowles Foundation for Research in Economics, Yale University.
- 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.
- Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
- Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
- 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).
- Gaetan Bakalli & St'ephane Guerrier & Olivier Scaillet, 2022. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Papers 2208.00972, arXiv.org.
- Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2021. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Swiss Finance Institute Research Paper Series 21-09, Swiss Finance Institute.
- Gaetan Bakalli & Stéphane Guerrier & Olivier Scaillet, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Post-Print hal-04325655, HAL.
- Lin, Qi, 2022. "Understanding idiosyncratic momentum in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
- Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
- Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019.
"Estimation of large dimensional conditional factor models in finance,"
Working Papers
unige:125031, University of Geneva, Geneva School of Economics and Management.
- Patrick Gagliardini & Elisa Ossola & O. Scaillet, 2019. "Estimation of Large Dimensional Conditional Factor Models in Finance," Swiss Finance Institute Research Paper Series 19-46, Swiss Finance Institute.
- Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
- Vafai, Nima & Rakowski, David, 2024. "The sources of portfolio volatility and mutual fund performance," International Review of Financial Analysis, Elsevier, vol. 91(C).
- Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021.
"Estimating the anomaly base rate,"
Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
- Alexander M. Chinco & Andreas Neuhierl & Michael Weber, 2019. "Estimating The Anomaly Base Rate," NBER Working Papers 26493, National Bureau of Economic Research, Inc.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2021.
"Machine Learning and Factor-Based Portfolio Optimization,"
Papers
2107.13866, arXiv.org.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
- Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
- Stanislav Anatolyev & Anna Mikusheva, 2018.
"Factor models with many assets: strong factors, weak factors, and the two-pass procedure,"
Papers
1807.04094, arXiv.org, revised Apr 2019.
- Anatolyev, Stanislav & Mikusheva, Anna, 2022. "Factor models with many assets: Strong factors, weak factors, and the two-pass procedure," Journal of Econometrics, Elsevier, vol. 229(1), pages 103-126.
- Ian Martin & Stefan Nagel, 2019.
"Market Efficiency in the Age of Big Data,"
CESifo Working Paper Series
8015, CESifo.
- Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
- Martin, Ian & Nagel, Stefan, 2019. "Market Efficiency in the Age of Big Data," CEPR Discussion Papers 14235, C.E.P.R. Discussion Papers.
- Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," LSE Research Online Documents on Economics 112960, London School of Economics and Political Science, LSE Library.
- Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," NBER Working Papers 26586, National Bureau of Economic Research, Inc.
- Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," LSE Research Online Documents on Economics 119289, London School of Economics and Political Science, LSE Library.
- 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.
- 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.
- Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023. "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, vol. 54(C).
- Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
- Chi-Ming Ho, 2023. "Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
- Han, SeungOh, 2024. "Hedging strategies for U.S. factor and sector exchange-traded funds during geopolitical events," Finance Research Letters, Elsevier, vol. 65(C).
- Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
- Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
- Solène Collot & Tobias Hemauer, 2021. "A literature review of new methods in empirical asset pricing: omitted-variable and errors-in-variable bias," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 77-100, March.
- Andrew Detzel & Robert Novy‐Marx & Mihail Velikov, 2023. "Model Comparison with Transaction Costs," Journal of Finance, American Finance Association, vol. 78(3), pages 1743-1775, June.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018.
"Empirical Asset Pricing via Machine Learning,"
NBER Working Papers
25398, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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.
- 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).
- José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
- Abhimanyu Gupta & Myung Hwan Seo, 2023.
"Robust Inference on Infinite and Growing Dimensional Time‐Series Regression,"
Econometrica, Econometric Society, vol. 91(4), pages 1333-1361, July.
- Abhimanyu Gupta & Myung Hwan Seo, 2019. "Robust Inference on Infinite and Growing Dimensional Time Series Regression," Papers 1911.08637, arXiv.org, revised Apr 2023.
- Thuy Duong Dang & Fabian Hollstein & Marcel Prokopczuk & Zhiguo He, 2023. "Which Factors for Corporate Bond Returns?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(4), pages 615-652.
- 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.
- Weijia Peng & Chun Yao, 2023. "Sector-level equity returns predictability with machine learning and market contagion measure," Empirical Economics, Springer, vol. 65(4), pages 1761-1798, October.
- Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation," Papers 2107.05201, arXiv.org, revised Oct 2021.
- Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
- Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
- Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
- Pablo Solórzano-Taborga & Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Data Envelopment Analysis and Multifactor Asset Pricing Models," IJFS, MDPI, vol. 8(2), pages 1-18, April.
- Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
- Frank Kleibergen & Zhaoguo Zhan, 2022. "Misspecification and Weak Identification in Asset Pricing," Papers 2206.13600, arXiv.org.
- Rubesam, Alexandre, 2022.
"Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market,"
Emerging Markets Review, Elsevier, vol. 51(PB).
- Alexandre Rubesam, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Post-Print hal-03707365, HAL.
- 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.).
- Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
- José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2020. "Models for Expected Returns with Statistical Factors," JRFM, MDPI, vol. 13(12), pages 1-17, December.
- 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.
- 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.
- Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019.
"High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing,"
The Warwick Economics Research Paper Series (TWERPS)
1230, University of Warwick, Department of Economics.
- Alexandre Belloni & Mingli Chen & Oscar Hernan Madrid Padilla & Zixuan & Wang, 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," Papers 1912.02151, arXiv.org, revised Aug 2022.
- Sang Il Lee & Seong Joon Yoo, 2019. "Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets," Papers 1903.06478, arXiv.org, revised Sep 2019.
- Konstantin Gorgen & Abdolreza Nazemi & Melanie Schienle, 2022. "Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates," Papers 2206.06026, arXiv.org.
- Lin, Qi, 2021. "The q5 model and its consistency with the intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 127(C).
- Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
- Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
- Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
- 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.
- 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.
- Konan Chan & Mei‐Xuan Li & Chu‐Bin Lin & Yanzhi Wang, 2022. "Organization capital effect in stock returns—The role of R&D," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(7-8), pages 1237-1263, July.
- Conall O'Sullivan & Vassilios G. Papavassiliou & Ronald Wekesa Wafula & Sabri Boubaker, 2024.
"New Insights into Liquidity Resiliency,"
Post-Print
hal-04432411, HAL.
- 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).
- Alla Petukhina & Simon Trimborn & Wolfgang Karl Härdle & Hermann Elendner, 2021.
"Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies,"
Quantitative Finance, Taylor & Francis Journals, vol. 21(11), pages 1825-1853, November.
- Alla Petukhina & Simon Trimborn & Wolfgang Karl Hardle & Hermann Elendner, 2020. "Investing with Cryptocurrencies -- evaluating their potential for portfolio allocation strategies," Papers 2009.04461, arXiv.org, revised Sep 2020.
- Pascal Böni & Heinz Zimmermann, 2021. "Are stock prices driven by expected growth rather than discount rates? Evidence based on the COVID-19 crisis," Risk Management, Palgrave Macmillan, vol. 23(1), pages 1-29, June.
- Mörstedt, Torsten & Lutz, Bernhard & Neumann, Dirk, 2024. "Cross validation based transfer learning for cross-sectional non-linear shrinkage: A data-driven approach in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(2), pages 670-685.
- Dichev, Ilia & Huang, Xinyi & Lee, Donald K.K & Zhao, Jianxin, 2023. "You have a point - but a point is not enough: The case for distributional forecasts of earnings," SocArXiv 4b2y8, Center for Open Science.
- Guettler, Andre & Naeem, Mahvish & Norden, Lars & Van Doornik, Bernardus, 2024.
"Pre-publication revisions of bank financial statements: A novel way to monitor banks?,"
Journal of Financial Intermediation, Elsevier, vol. 58(C).
- Andre Guettler & Mahvish Naeem & Lars Norden & Bernardus Van Doornik, 2024. "Pre-Publication Revisions of Bank Financial Statements: a novel way to monitor banks?," Working Papers Series 590, Central Bank of Brazil, Research Department.
- Wang, Xiaowei & Wang, Rui & Zhang, Yichun, 2024. "Is there more to asset price linkages in China than meets the eye: Cross-asset momentum and the role of hybrid funds," International Review of Financial Analysis, Elsevier, vol. 95(PA).
- R. Douglas Martin & Daniel Z. Xia, 2022. "Efficient bias robust regression for time series factor models," Journal of Asset Management, Palgrave Macmillan, vol. 23(3), pages 215-234, May.
- Zacharias Sautner & Laurence Van Lent & Grigory Vilkov & Ruishen Zhang, 2023. "Firm‐Level Climate Change Exposure," Journal of Finance, American Finance Association, vol. 78(3), pages 1449-1498, June.
- Billio, Monica & Caporin, Massimiliano & Panzica, Roberto & Pelizzon, Loriana, 2023.
"The impact of network connectivity on factor exposures, asset pricing, and portfolio diversification,"
International Review of Economics & Finance, Elsevier, vol. 84(C), pages 196-223.
- Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
- Qingliang Fan & Ruike Wu & Yanrong Yang, 2024. "Shocks-adaptive Robust Minimum Variance Portfolio for a Large Universe of Assets," Papers 2410.01826, arXiv.org.
- José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2021. "How to Explain the Cross-Section of Equity Returns through Common Principal Components," Mathematics, MDPI, vol. 9(9), pages 1-22, April.
- 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).
- Jie Fang & Jianwu Lin & Shutao Xia & Yong Jiang & Zhikang Xia & Xiang Liu, 2020. "Neural Network-based Automatic Factor Construction," Papers 2008.06225, arXiv.org, revised Oct 2020.
- Baumeister, Christiane, 2021.
"Measuring Market Expectations,"
CEPR Discussion Papers
16520, C.E.P.R. Discussion Papers.
- Christiane Baumeister, 2021. "Measuring Market Expectations," NBER Working Papers 29232, National Bureau of Economic Research, Inc.
- Christiane Baumeister, 2021. "Measuring Market Expectations," CESifo Working Paper Series 9305, CESifo.
- Christiane Baumeister, 2021. "Measuring Market Expectations," Working Papers 202163, University of Pretoria, Department of Economics.
- Manuel Ammann & Mathis Mörke, 2019. "Credit Variance Risk Premiums," Working Papers on Finance 1908, University of St. Gallen, School of Finance.
- Jushan Bai & Jiangtao Duan & Xu Han, 2022.
"Likelihood ratio test for structural changes in factor models,"
Papers
2206.08052, arXiv.org, revised Dec 2023.
- Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
- Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
- 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.
- Rama K. Malladi, 2024. "Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1021-1045, March.
- Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factors’ CPDAG Roots and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_18, University of São Paulo (FEA-USP).
- Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
- Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
- 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).
- 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.
- 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.
- Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2022. "Machine-Learning the Skill of Mutual Fund Managers," NBER Working Papers 29723, National Bureau of Economic Research, Inc.
- Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
- Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
- Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
- Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
- Liyuan Cui & Guanhao Feng & Yongmiao Hong, 2024. "Regularized Gmm For Time‐Varying Models With Applications To Asset Pricing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(2), pages 851-883, May.
- Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
- Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
- Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
- Fabian Krause & Jan-Peter Calliess, 2024. "End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture," Papers 2402.08233, arXiv.org.
- Paul Schneider & Christian Wagner & Josef Zechner, 2019.
"Low Risk Anomalies?,"
Swiss Finance Institute Research Paper Series
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202310, University of Liverpool, Department of Economics.
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Articles
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"The market for English Premier League (EPL) odds,"
Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(4), pages 167-178, December.
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