Giglio, S., Kelly, B., &, xiu, D. (2022). Factor models, machine learning, and asset pricing. Annual Review of Financial Economics, 14, 337-368.

Kelly, Bryan T. and Xiu, Dacheng, Financial Machine Learning (July 1,2023). Available at SSRN: https://ssrn.com/abstract=4501707 or http://dx.doi.org/10.2139/ssrn.45017073. 

Nagel, S. (2021). Machine learning in asset pricing (Vol. 1). Princeton University Press.

Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3),535-574.

Loughran, T., & McDonald, B. (2016) Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187-1230.

ESG,word embedding:  Li, K., F. Mai, R. Shen, and X. Yan. 2021. Measuring corporate culture using machine learning. Review of Financial Studies 34:3265-315.

Mullainathan, S., and J. Spiess. 2017. Machine learning: An applied econometric approach. Journal of Economic Perspectives 31:87-106.

Erel, I., Stern, L. H., Tan, C., & " " M. S. (2021). Selecting directors using machine learning. The Review of Financial Studies, 34(7), 3226-3264.

ML model, Factor zoo:  Gu, S., Kelly, B., & Ke Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.

ML model, factor zoo: Giglio, S., Y. Liao, and D. Xiu. 2021. Thousands of alpha tests. Review of Financial Studies 34:3456-96.

ML model, Factor zoo:  Leippold, M., Wang, Q., &Zhou, W.(2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82.

ML model, Factor zoo:  Kozak, S., Nagel, S., & Santosh, S. (2020). Shrinking the cross-section. Journal of Financial Economics, 135(2), 271-292.

BERT, Demand-system asset pricing:  Gabaix, X., Koijen, R. S., Richmond, R., & Yogo, ML (2023). Asset embeddings. Available at SSRN 4507511.

BERT, text analysis in acct: Huang, A. H., Wang, H., &Y. (2023). FinBEKT: A large language model for extracting information from financial text. Contemporary Accounting Research,  40( 2) ,  806- 841. 

PCA, Factor zoo:  Giglio,  S. ,  Xiu,  D. ,  &  Zhang,  D. (2023) .  Prediction when factors are weak.  Multiple University of Chicago, Becker Friedman Institute for Economics Working Paper, (2023-47).

Lasso, Factor zoo: Feng,  G. ,  Giglio,  S. ,  &  Xiu,  D. (2020) .  Taming the factor zoo:  A test of new factors. The Journal of Finance, 75(3), 1327-1370.

Mutiple ML model, factor zoo:  Gu,S.,Kelly, B.,& Xiu, D.(2021). Autoencoder asset pricing models. Journal of Econometrics, 222(1), 429-450.

Bayes ML, factor zoo: Jensen, T. I., Kelly, B., & Pedersen, L. H. (2023). Is there a replication crisis in finance?. The Journal of Finance, 78(5), 2465-2518.

Bayes ML,  return predictioin:  Smith,  S. C,  & Timmermann,  A.  ( 2021) .  Break risk.  The Review of Financial Studies, 34(4), 2045-2100.

Bayes ML, return predictioin:  Gao, Ming and Zhang, Cong, Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach (October 28,2023). Available at SSRN: https://ssrn.com/abstract=4545015 or http://dx.doi.org/10.2139/ssrn.4545015

LDA & Lasso, Macro : Bybee, L., Kelly, B. T., Manela, A., & Xiu, D. (2021). Business news and business cycles (No. w29344). National Bureau of Economic Research.

LLMs, return prediction: Jiang, J., Kelly, B. T., & Xiu, D. (2022). Expected returns and large anguage models. Available at SSRN.

Satellite imagery, governance: Zhu, C (2019). Big data as a governance mechanism. The Review of Financial Studlies, 32(5),2021-2061.

Video, behavioral finance:  Hu, Allen, and Song Ma. Persuading imestors: A video-bawed stuady. No,w29048 National Bureau of Ecomomic Research, 2021.

CNN, expected return:  Jiang, J., Kelly, B, ke Xiu, D. (2023). (Re-) Imag (in) ing Price Trends. The Journal of Finance, 78(6), 3195.3249

Voice, risk:  Yang, Y, Qin, Y, Fan, Y, & Zhang, Z. (2003), Unbocking the Power of Voisce for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approsch. Mis Quarterly; 47(1).

Log data from SEC:  Cao, S. S., Du, K, Yang, B, &  Zbang, A. L.(2021). Copycat skills and disclosure costs: Evidence from peer companies' digital footprints. Jounal of Accounting Research, 59(4), 1261-1302

BERT, textual analysis in Chinese:  Lee, C. M. , & Zhong, Q. ( 2022) . Shall we talk ?  The role of interactive investor platforms in corporate communication. Journal of Accounting and Economics, 74( 2- 3) , 101524. 

Stats, return prediction:  Ke, Zheng and Kelly, Bryan T. and Xiu, Dacheng, Predicting Returns with Text Data (September 30,2020). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2019-69, Yale ICF Working Paper No, 2019-10, Chicago Booth Research Paper No. 20-37, Available at SSRN: https://ssrn.com/abstract=3389884 or http://dx.doi.org/10.2139/ssrn.3389884

LDA ,  fraud:  Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What are you saying? Using topic to detect financial misreporting. Journal of Accounting Research,58(1),237-291.

Tone,Disclosure:  Cheng, L., Roulstone, D. T., & K Van Buskirk, A. (2021). Are investors influenced by the order of information in earnings press releases?. The Accounting Review, 96(2),413-433.

Proxy from word cnt, Social Media:  Bartov, E., Faurel, L., & Mohanram, P. S. (2018). Can Twitter help predict firm-level earnings and stock returns?. The Accounting Review, 93(3), 25-57.

Tone & sentiment, Disclosure:  Cho, H., & Muslu, V. (2021). How do firms change investments based on MD&A disclosures of peer firms?. The Accounting Review, 96(2), 177-204

Complexity:

1. Fog, readability of disclosure : Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and economics, 45(2-3), 221-247.

2. Alternative explanation for fog, readability of disclosure: Guay, W, Samuels, D., Taylor, D. (2016). Guiding through the fog: Financial statement complexity and voluntary disclosure. Journal of Accounting and Economics, 62(2-3), 234-269

3. Conference call, readability of disclosure: Bushee, B. J., Gow, I. D.,  D. J. (2018). Linguistic complexity in firm disclosures: Obfuscation or information?. Journal of Accounting Research, 56(1), 85-121.

Word-bag & Conference call, Political risk :  Hassan, T. A., Hollander, S., Van Lent, L., Tahoun, A.(2019). Firm-level political risk: Measurement and effects. The Quarterly Journal of Economics, 134(4), 2135-2202.

GPT & BERT : behavioral finance Bybee, I..(2023) The Ghost in the Machine: Generating Beliefs with Large Language Models

textual analysis, ESG:  Engle, R. F., Giglio, S., Kelly, B., Lee, H. " Stroebel, J. (2020). Hedging climate change news. The Review of Financial Studies, 33(3), 1184-1216.

textual analysis, ESG:  Giglio, S., Kuchler, T., Stroebel, J., & & Zeng, X.(2023). Biodiversity risk (No. w31137). National Bureau of Economic Research

word-cnt, ESG: Sautner, Z., Van Lent, L., Vilkov, G., & Zhang, R. (2023). Firm-level climate change exposure. The Journal of Finance, 78(3), 1449-1498

word-cnt, ESG:  Sautner, Z., Van Lent, L., Vilkov, G., & Zhang, R. (2023). Pricing climate change exposure. Management Science, 69(12), 7540-7561

Key-word Mining and extension: King, G., Lam, P., & Roberts, M. E. (2017). Computer-assisted keyword and document set discovery from unstructured text. American Journal of Political Science, 61(4), 971-988.

Karolyi, G. A,  & Van Nieuwerburgh, S. (2020). New methods for the cross-section of returns. The Review of Financial Studies, 33(5), 1879-1890.

Bybee, L., Kelly, B., &Su, Y. (2023). Narrative asset pricing: Interpretable systematic risk factors from news text. The Review of Financial Studies, 36(12), 4759-4787.

Liu, M. (2022). Assessing human information processing in lending decisions: A machine learning approach. Journal of Accounting Research, 60(2), 607-651.

Giglio,S., Xiu, D., & Zhang, D. (2021). Test assets and weak factors (No. w29002). National Bureau of Economic Research.

Chen, L., Pelger, M.,  & Zhu, J. (2024). Deep learming in asset pricing. Management Science, 70(2),714-750.

Hommel, N., Landier, A., & k Thesmar, D. (2023). Corporate valuation: An empirical comparison of discounting methods (No. w30898). National Bureau of Economic Research.

Van Binsbergen, Jules H., Xiao Han, and Alejandro Lopez-Lira. "Man versus machine learning; The term structure of earnings expectations and conditional biases," The Review of financial studies 36, no. 6 (2023): 2361-2396. 

Cao, S, Jiang W., Yang, B., & Zhang, A. L. (2023). How to talk when a machine is listening: Corporate disclosure in the age of Al. The Review of Financial Studies, 36(9), 3603-3642

Cao, S., Jiang, W., Wang, J. L., &  ang, B. (2021). From man vs. machine to man+ machine: The art and Al of stock analyses. Columbia Business School Research Paper.

Anand, A., Samadi, M., Sokobin, J., & K. (2021). Institutional order handling and broker-affiliated trading venues. The Review of Financial Studies, 34(7), 3364-3402.

Benamar, H., T. Foucault, and C. Vega. 2021. Demand for information, uncertainty, and the response of US Treasury securities to news. Review of Financial Studies 34:3403-55

Easley, D, M. Lopez de Prado, M. OYHara, and Z. Zhang. 2021. Microstructure in the machine age. Review of Financial Studies 34:3316-63

Xgboost:   Chen, T. , & Guestrin, C. ( 2016, August) . Xgboost:  A scalable tree- boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

Lightgbm:  Ke,G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W..... &Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.

CatBoost: Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V, & & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.

LSTM: Graves, A., \& Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37-45.

GRU:  Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & k Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Transformer: Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.(2017). Attention is all you need. Advances in neural information processing systems, 30.

GATs: Veličković, P, Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.