Analyzing credit spread changes using explainable artificial intelligence
Author
Abstract
Suggested Citation
DOI: 10.1016/j.irfa.2024.103315
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
- Zhiguo He & Paymon Khorrami & Zhaogang Song, 2022.
"Commonality in Credit Spread Changes: Dealer Inventory and Intermediary Distress,"
The Review of Financial Studies, Society for Financial Studies, vol. 35(10), pages 4630-4673.
- Zhiguo He & Paymon Khorrami & Zhaogang Song, 2019. "Commonality in Credit Spread Changes: Dealer Inventory and Intermediary Distress," NBER Working Papers 26494, National Bureau of Economic Research, Inc.
- 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.
- Siamak Javadi & Ali Nejadmalayeri & Timothy L Krehbiel, 2018. "Do FOMC Actions Speak Loudly? Evidence from Corporate Bond Credit Spreads [Communication and monetary policy]," Review of Finance, European Finance Association, vol. 22(5), pages 1877-1909.
- Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
- Amihud, Yakov & Mendelson, Haim, 1986. "Asset pricing and the bid-ask spread," Journal of Financial Economics, Elsevier, vol. 17(2), pages 223-249, December.
- 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.
- Tong Suk Kim & Jae Won Park & Yuen Jung Park, 2017. "Macroeconomic Conditions and Credit Default Swap Spread Changes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(8), pages 766-802, August.
- Manzoni, Katiuscia, 2002. "Modeling credit spreads: An application to the sterling Eurobond market," International Review of Financial Analysis, Elsevier, vol. 11(2), pages 183-218.
- Johannes Hauptmann & Anja Hoppenkamps & Aleksey Min & Franz Ramsauer & Rudi Zagst, 2014. "Forecasting market turbulence using regime-switching models," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 28(2), pages 139-164, May.
- John Y. Campbell & Samuel B. Thompson, 2008.
"Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?,"
The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
- Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
- Breitenfellner, Bastian & Wagner, Niklas, 2012. "Explaining aggregate credit default swap spreads," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 18-29.
- Rodolfo Martell, 2008. "Understanding Common Factors in Domestic and International Bond Spreads," Review of Finance, European Finance Association, vol. 12(2), pages 365-389.
- Pierre Collin-Dufresn & Robert S. Goldstein & J. Spencer Martin, 2001. "The Determinants of Credit Spread Changes," Journal of Finance, American Finance Association, vol. 56(6), pages 2177-2207, December.
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.- Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
- Yilie Huang & Yanwei Jia & Xun Yu Zhou, 2024. "Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study," Papers 2412.16175, arXiv.org.
- Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
- Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
- Faria, Gonçalo & Verona, Fabio, 2024. "Enhancing forecast accuracy through frequencydomain combination: Applications to financial and economic indicators," Bank of Finland Research Discussion Papers 14/2024, Bank of Finland.
- Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
- Jozef Barunik & Martin Hronec & Ondrej Tobek, 2024. "Predicting the distributions of stock returns around the globe in the era of big data and learning," Papers 2408.07497, arXiv.org.
- Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
- Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Nov 2024.
- Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023.
"The commodity risk premium and neural networks,"
Journal of Empirical Finance, Elsevier, vol. 74(C).
- Joelle Miffre & Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2023. "The commodity risk premium and neural networks," Post-Print hal-04322519, HAL.
- Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023.
"Targeting predictors in random forest regression,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
- Adel Javanmard & Jingwei Ji & Renyuan Xu, 2024. "Multi-Task Dynamic Pricing in Credit Market with Contextual Information," Papers 2410.14839, arXiv.org, revised Oct 2024.
- DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
- Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
- Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023.
"Deep learning and technical analysis in cryptocurrency market,"
Finance Research Letters, Elsevier, vol. 54(C).
- Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Deep Learning And Technical Analysis In Cryptocurrency Market," Working Papers halshs-03917333, HAL.
- Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
- Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
- 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).
- Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021.
"Can Machine Learning Help to Select Portfolios of Mutual Funds?,"
Working Papers
1245, Barcelona School of Economics.
- 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.
More about this item
Keywords
Credit spread changes; Random forest; Partial dependence plot; H-statistic; SHAP values; Hedging;All these keywords.
JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
Statistics
Access and download statisticsCorrections
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:eee:finana:v:94:y:2024:i:c:s1057521924002473. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.