Scaled PCA: A New Approach to Dimension Reduction
Author
Abstract
Suggested Citation
Download full text from publisher
Other versions of this item:
- 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.
References listed on IDEAS
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020.
"Dissecting Characteristics Nonparametrically,"
Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Finance, European Finance Association, vol. 33(5), pages 2326-2377.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," NBER Working Papers 23227, National Bureau of Economic Research, Inc.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2018. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 7187, CESifo.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 6391, CESifo.
- Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
- Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
- 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.
- Bryan Kelly & Seth Pruitt & Yinan Su, 2018. "Characteristics Are Covariances: A Unified Model of Risk and Return," NBER Working Papers 24540, National Bureau of Economic Research, Inc.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2018.
"Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices,"
Working Paper Series of the Department of Economics, University of Konstanz
2018-07, Department of Economics, University of Konstanz.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2020. "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Working Paper series 20-03, Rimini Centre for Economic Analysis.
- Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2019. "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Papers 1906.05545, arXiv.org.
- Hai Lin & Chunchi Wu & Guofu Zhou, 2018. "Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach," Management Science, INFORMS, vol. 64(9), pages 4218-4238, September.
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
- Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models,"
Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Jushan Bai & Serena Ng, 2004.
"A PANIC Attack on Unit Roots and Cointegration,"
Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
- Jushan Bai & Serena Ng, 2001. "A Panic Attack on Unit Roots and Cointegration," Economics Working Paper Archive 469, The Johns Hopkins University,Department of Economics.
- Jushan Bai & Serena Ng, 2001. "A PANIC Attack on Unit Roots and Cointegration," Boston College Working Papers in Economics 519, Boston College Department of Economics.
- 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.
- 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.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, July.
- Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
- Markus Pelger, 2020. "Understanding Systematic Risk: A High‐Frequency Approach," Journal of Finance, American Finance Association, vol. 75(4), pages 2179-2220, August.
- Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
- Ludvigson, Sydney C. & Ng, Serena, 2007.
"The empirical risk-return relation: A factor analysis approach,"
Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
- Sydney C. Ludvigson & Serena Ng, 2005. "The Empirical Risk-Return Relation: A Factor Analysis Approach," NBER Working Papers 11477, National Bureau of Economic Research, Inc.
- Sydney Ludvigson & Serena Ng, 2006. "The Empirical Risk-Return Relation: a factor analysis approach," 2006 Meeting Papers 236, Society for Economic Dynamics.
- Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
- Nathaniel Light & Denys Maslov & Oleg Rytchkov, 2017. "Aggregation of Information About the Cross Section of Stock Returns: A Latent Variable Approach," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1339-1381.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Gregory Connor & Matthias Hagmann & Oliver Linton, 2012. "Efficient Semiparametric Estimation of the Fama–French Model and Extensions," Econometrica, Econometric Society, vol. 80(2), pages 713-754, March.
- Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015.
"Investor Sentiment Aligned: A Powerful Predictor of Stock Returns,"
The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
- Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," CEMA Working Papers 676, China Economics and Management Academy, Central University of Finance and Economics.
- Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
- Stefano Giglio & Dacheng Xiu, 2021. "Asset Pricing with Omitted Factors," Journal of Political Economy, University of Chicago Press, vol. 129(7), pages 1947-1990.
- Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Kuppenheimer, Gregory & Shelly, Stuart & Strauss, Jack, 2023. "Can machine learning identify sector-level financial ratios that predict sector returns?," Finance Research Letters, Elsevier, vol. 57(C).
- Liu, Shan & Li, Ziwei, 2023. "Macroeconomic attention and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 57(C).
- Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
- Lu, Xinjie & Ma, Feng & Wang, Tianyang & Wen, Fenghua, 2023. "International stock market volatility: A data-rich environment based on oil shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 184-215.
- Fang, Puyi & Gao, Zhaoxing & Tsay, Ruey S., 2023. "Supervised kernel principal component analysis for forecasting," Finance Research Letters, Elsevier, vol. 58(PA).
- Jixiang, Zhang & Feng, Ma, 2024. "Video apps user engagement and stock market volatility: Evidence from China," Finance Research Letters, Elsevier, vol. 64(C).
- Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Jun 2024.
- Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
- Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
- Shuo-Chieh Huang & Ruey S. Tsay, 2024. "Time Series Forecasting with Many Predictors," Mathematics, MDPI, vol. 12(15), pages 1-20, July.
- 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.
- Shulin Shen & Yiyi Zhao & Jindong Pang, 2024. "Local Housing Market Sentiments and Returns: Evidence from China," The Journal of Real Estate Finance and Economics, Springer, vol. 68(3), pages 488-522, April.
- Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
- Lu, Fei & Ma, Feng & Guo, Qiang, 2023. "Less is more? New evidence from stock market volatility predictability," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Wang, Jiashun & Wang, Jiqian & Ma, Feng, 2024. "International commodity market and stock volatility predictability: Evidence from G7 countries," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 62-71.
- Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
- Yongan Xu & Chao Liang, 2024. "Does extreme climate concern drive equity premiums? Evidence from China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
- Fameliti Stavroula & Skintzi Vasiliki, 2024. "Macroeconomic attention and commodity market volatility," Empirical Economics, Springer, vol. 67(5), pages 1967-2007, November.
- Lu, Fei & Ma, Feng & Feng, Lin, 2024. "Carbon dioxide emissions and economic growth: New evidence from GDP forecasting," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
- Zhang, Xincheng, 2024. "Country-level energy-related uncertainties and stock market returns: Insights from the U.S. and China," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
- Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023. "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, vol. 237(2).
- Tan, Xilong & Tao, Yubo, 2023. "Trend-based forecast of cryptocurrency returns," Economic Modelling, Elsevier, vol. 124(C).
- Lu, Xinjie & Lang, Qiaoqi, 2023. "Categorial economic policy uncertainty indices or Twitter-based uncertainty indices? Evidence from Chinese stock market," Finance Research Letters, Elsevier, vol. 55(PB).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhaoxing Gao & Ruey S. Tsay, 2023. "Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors," Papers 2307.07689, arXiv.org.
- Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022.
"Eigenvalue tests for the number of latent factors in short panels,"
Swiss Finance Institute Research Paper Series
22-81, Swiss Finance Institute.
- Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
- Catherine Doz & Peter Fuleky, 2019.
"Dynamic Factor Models,"
Working Papers
2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
- Catherine Doz & Peter Fuleky, 2020. "Dynamic Factor Models," PSE-Ecole d'économie de Paris (Postprint) halshs-02491811, HAL.
- Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
- Catherine Doz & Peter Fuleky, 2020. "Dynamic Factor Models," Post-Print halshs-02491811, HAL.
- Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
- Stefano Giglio & Dacheng Xiu, 2017. "Inference on Risk Premia in the Presence of Omitted Factors," NBER Working Papers 23527, National Bureau of Economic Research, Inc.
- Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
- Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Economic Forecasts Using Many Noises," Papers 2312.05593, arXiv.org, revised Dec 2023.
- 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.
- Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
- Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
- Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019.
"A diagnostic criterion for approximate factor structure,"
Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
- Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
- Patrick Gagliardini & Elisa Ossola & O. Scaillet, 2016. "A Diagnostic Criterion for Approximate Factor Structure," Swiss Finance Institute Research Paper Series 16-51, Swiss Finance Institute, revised Dec 2016.
- Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
- 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.
- Matteo Barigozzi & Marc Hallin, 2023.
"Dynamic Factor Models: a Genealogy,"
Papers
2310.17278, arXiv.org, revised Jan 2024.
- Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Working Papers ECARES 2023-15, ULB -- Universite Libre de Bruxelles.
- Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
- Vigo Pereira, Caio, 2021.
"Portfolio efficiency with high-dimensional data as conditioning information,"
International Review of Financial Analysis, Elsevier, vol. 77(C).
- 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.
- Francisco Corona & Pilar Poncela & Esther Ruiz, 2017.
"Determining the number of factors after stationary univariate transformations,"
Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
- Corona, Francisco & Poncela, Maria Pilar, 2016. "Determining the number of factors after stationary univariate transformations," DES - Working Papers. Statistics and Econometrics. WS ws1602, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- 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).
- 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.
- Fan, Jianqing & Ke, Yuan & Liao, Yuan, 2021.
"Augmented factor models with applications to validating market risk factors and forecasting bond risk premia,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 269-294.
- Jianqing Fan & Yuan Ke & Yuan Liao, 2016. "Augmented Factor Models with Applications to Validating Market Risk Factors and Forecasting Bond Risk Premia," Papers 1603.07041, arXiv.org, revised Sep 2018.
- Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
- Jian Chen & Jiaquan Yao & Qunzi Zhang & Xiaoneng Zhu, 2023. "Global Disaster Risk Matters," Management Science, INFORMS, vol. 69(1), pages 576-597, January.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cuf:wpaper:678. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Qiang Gao (email available below). General contact details of provider: https://edirc.repec.org/data/emcufcn.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.