Papers

Asset Pricing

1. Ang, A., et al. (2013). "Asset pricing in the dark: The cross-section of OTC stocks." The Review of Financial Studies 26(12): 2985-3028.
2. Constantinides, G. M. and A. Ghosh (2017). "Asset pricing with countercyclical household consumption risk." The Journal of Finance 72(1): 415-460.
3. Fama, E. F. and K. R. French (2018). "Long-horizon returns." The Review of Asset Pricing Studies 8(2): 232-252.
4. Kroencke, T. A. (2017). "Asset pricing without garbage." The Journal of Finance 72(1): 47-98.
5. Moro Visconti, R., et al. (2018). "Big data-driven stochastic business planning and corporate valuation." Moro Visconti, R., Montesi, G., & Papiro, G.(2018). Big data-driven stochastic business planning and corporate valuation. Corporate Ownership & Control 15(3-1): 189-204.
6. Sabherwal, R., et al. (2019). "How does strategic alignment affect firm performance? The roles of information technology investment and environmental uncertainty." MIS Quarterly 43(2): 453-474.
7. Tuzel, S. and M. B. Zhang (2017). "Local risk, local factors, and asset prices." The Journal of Finance 72(1): 325-370.

Blockchain

1. Biais, B., et al. (2019). "The blockchain folk theorem." The Review of Financial Studies 32(5): 1662-1715.
2. Chiu, J. and T. V. Koeppl (2019). "Blockchain-based settlement for asset trading." The Review of Financial Studies 32(5): 1716-1753.
3. Cong, L. W. and Z. He (2019). "Blockchain disruption and smart contracts." The Review of Financial Studies 32(5): 1754-1797.
4. Easley, D., et al. (2019). "From mining to markets: The evolution of bitcoin transaction fees." Journal of Financial Economics 134(1): 91-109.
5. Foley, S., et al. (2019). "Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?" The Review of Financial Studies 32(5): 1798-1853.
6. Makarov, I. and A. Schoar (2020). "Trading and arbitrage in cryptocurrency markets." Journal of Financial Economics 135(2): 293-319.

Corporate Finance

1. Benitez, J., et al. (2018). "Impact of information technology infrastructure flexibility on mergers and acquisitions." MIS Quarterly 42(1).
2. Berente, N., et al. (2019). "Institutional logics and pluralistic responses to enterprise system implementation: a qualitative meta-analysis." MIS Quarterly 43(3): 873-902.
3. Choi, T. M., et al. (2018). "Big data analytics in operations management." Production and Operations Management 27(10): 1868-1883.
4. Fanning, K. and E. Drogt (2014). "Big data: New opportunities for M&A." Journal of Corporate Accounting & Finance 25(2): 27-34.
5. Fanning, K. and R. Grant (2013). "Big data: implications for financial managers." Journal of Corporate Accounting & Finance 24(5): 23-30.
6. Mills, J. (2014). "CEO facial width predicts firm financial policies." Available at SSRN 2503582.
7. Onay, C. and E. Ozturk (2018). "A review of credit scoring research in the age of Big Data." Journal of Financial Regulation and Compliance.
8. Yermack, D. (2017). "Corporate governance and blockchains." Review of Finance 21(1): 7-31.

Data Mining

1. Bijalwan, V., et al. (2014). "KNN based machine learning approach for text and document mining." International Journal of Database Theory and Application 7(1): 61-70.
2. Bijalwan, V., et al. (2014). "Machine learning approach for text and document mining." arXiv preprint arXiv:1406.1580.
3. Buehlmaier, M. M. and T. M. Whited (2018). "Are financial constraints priced? Evidence from textual analysis." The Review of Financial Studies 31(7): 2693-2728.
4. Dong, M., et al. (2019). "Textual Analysis of Banks' Pillar 3 Documents." Available at SSRN 3365005.
5. Gong, J., et al. (2017). "Examining the impact of keyword ambiguity on search advertising performance: A topic model approach." MIS Quarterly (Forthcoming).
6. Guo, L., et al. (2016). "Textual analysis and machine leaning: Crack unstructured data in finance and accounting." The Journal of Finance and Data Science 2(3): 153-170.
7. Mykova, R., et al. (2018). "Predicting abnormal stock return volatility using textual analysis of news–a meta-learning approach." Amfiteatru Economic 20(47): 185-201.
8. Seker, S. E., et al. (2013). "Time series analysis on stock market for text mining correlation of economy news." International Journal of Social Sciences and Humanity Studies 6(1): 69-91.
9. Yang, F., et al. (2018). "Textual analysis of corporate annual disclosures: a comparison between bankrupt and non-bankrupt companies." Journal of Emerging Technologies in Accounting 15(1): 45-55.

Fintech

1. Chen, M. A., et al. (2019). "How valuable is FinTech innovation?" The Review of Financial Studies 32(5): 2062-2106.
2. D’Acunto, F., et al. (2019). "The promises and pitfalls of robo-advising." The Review of Financial Studies 32(5): 1983-2020.
3. Schnabl, P., et al. (2018). "The Role of Technology in Mortgage Lending."
4. Tang, H. (2019). "Peer-to-peer lenders versus banks: substitutes or complements?" The Review of Financial Studies 32(5): 1900-1938.
5. Vallee, B. and Y. Zeng (2019). "Marketplace lending: a new banking paradigm?" The Review of Financial Studies 32(5): 1939-1982.
6. Zhu, C. (2019). "Big data as a governance mechanism." The Review of Financial Studies 32(5): 2021-2061.

High Frequency Trading

1. Agarwalla, S. K., et al. (2015). "Impact of the introduction of call auction on price discovery: Evidence from the Indian stock market using high-frequency data." International Review of Financial Analysis 39: 167-178.
2. Allen, D. E., et al. (2015). "Machine news and volatility: the Dow Jones industrial average and the TRNA real-time high-frequency sentiment series." The handbook of high frequency trading: 327-344.
3. Bacry, E. and J.-F. Muzy (2014). "Hawkes model for price and trades high-frequency dynamics." Quantitative Finance 14(7): 1147-1166.
4. Brogaard, J., et al. (2018). "High frequency trading and extreme price movements." Journal of Financial Economics 128(2): 253-265.
5. Cespa, G. and X. Vives (2015). "The beauty contest and shortterm trading." The Journal of Finance 70(5): 2099-2154.
6. CollinDufresne, P. and V. Fos (2015). "Do prices reveal the presence of informed trading?" The Journal of Finance 70(4): 1555-1582.
7. Conrad, J., et al. (2015). "High-frequency quoting, trading, and the efficiency of prices." Journal of Financial Economics 116(2): 271-291.
8. Davis, R. L., et al. (2014). "Clustering of Trade Prices by HighFrequency and Non–HighFrequency Trading Firms." Financial Review 49(2): 421-433.
9. Foucault, T., et al. (2016). "News trading and speed." The Journal of Finance 71(1): 335-382.
10. Ho, K. Y., et al. (2015). High-frequency news flow and states of asset volatility. Handbook of High Frequency Trading, Elsevier: 359-383.
11. Hoffmann, M., et al. (2014). "Optimization and statistical methods for high frequency finance." ESAIM: Proceedings and Surveys 45: 219-228.
12. Kirilenko, A., et al. (2017). "‘Flash Crash’: The first market crash in the era of algorithms and automated trading." LSE Business Review.
13. Myers, B. and A. Gerig (2015). Simulating the synchronizing behavior of high-frequency trading in multiple markets. Financial Econometrics and Empirical Market Microstructure, Springer: 207-213.
14. Pan, W., et al. (2013). "Can High-Frequency Trading Drive the Stock Market Off a Cliff?" MIT Sloan Management Review 54(4): 16.
15. Solakoglu, M. N. and N. Demir (2015). "News Releases and Stock Market Volatility: Intraday Evidence from Borsa Istanbul." Handbook of High Frequency Trading: 385.

Initial Public Offering

1. Anand, A., et al. (2019). "Does institutional trading affect underwriting?" Journal of Corporate Finance 58: 101495.
2. Chong, B. S. and Z. Liu (2020). "Issuer IPO underpricing and Directed Share Program (DSP)." Journal of Empirical Finance 56: 105-125.
3. Gao, S., et al. (2020). "Differences of opinion, institutional bids, and IPO underpricing." Journal of Corporate Finance 60: 101540.
4. Hoque, H. and S. Mu (2019). "Partial private sector oversight in China's A-share IPO market: An empirical study of the sponsorship system." Journal of Corporate Finance 56: 15-37.
5. Megginson, W. L., et al. (2019). "Financial distress risk in initial public offerings: how much do venture capitalists matter?" Journal of Corporate Finance 59: 10-30.
6. Michala, D. (2019). "Are private equity backed initial public offerings any different? Timing, information asymmetry and post-IPO survival." Journal of Corporate Finance 59: 31-47.
7. Ozmel, U., et al. (2019). "Outside Insiders: Does Access to Information Prior to an IPO Generate a Trading Advantage After the IPO?" Journal of Financial and Quantitative Analysis 54(1): 303-334.
8. Yan, Y., et al. (2019). "Uncertainty and IPO initial returns: evidence from the tone analysis of China’s IPO prospectuses." Pacific-Basin Finance Journal 57: 101075.

Investor Behavior

1. Alanyali, M., et al. (2013). "Quantifying the relationship between financial news and the stock market." Scientific reports 3: 3578.
2. Alfano, S. J., et al. (2015). "Is news sentiment more than just noise?".
3. Chen, H., et al. (2014). "Wisdom of crowds: The value of stock opinions transmitted through social media." The Review of Financial Studies 27(5): 1367-1403.
4. Curme, C., et al. (2014). "Quantifying the semantics of search behavior before stock market moves." Proceedings of the National Academy of Sciences 111(32): 11600-11605.
5. Guerard, J. B., et al. (2013). "Global stock selection modeling and efficient portfolio construction and management." The Journal of Investing 22(4): 121-128.
6. Guerard, J. B., et al. (2013). "Efficient global portfolios: Big data and investment universes." IBM Journal of Research and Development 57(5): 11: 11-11: 11.
7. Ho, K.-Y., et al. (2013). "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches." The North American Journal of Economics and Finance 26: 436-456.
8. Kapetanios, G., et al. (2019). "Jumps in option prices and their determinants: Real-time evidence from the E-mini S&P 500 option market." Available at SSRN 2368385.
9. Kim, K. and S. Viswanathan (2018). "The'experts' in the crowd: The role of experienced investors in a crowdfunding market." Mis Quarterly.
10. Lee, Y. J., et al. (2014). Analysis on stock market volatility with collective human behaviors in online message board. 2014 IEEE International Conference on Computer and Information Technology, IEEE.
11. Moat, H. S., et al. (2013). "Quantifying Wikipedia usage patterns before stock market moves." Scientific reports 3: 1801.
12. Moat, H. S., et al. (2014). Anticipating stock market movements with Google and Wikipedia. Nonlinear phenomena in complex systems: From nano to macro scale, Springer: 47-59.
13. Moat, H. S., et al. (2014). "Using big data to predict collective behavior in the real world 1." Behavioral and Brain Sciences 37(1): 92-93.
14. Okada, K. and T. Yamasaki (2014). "Investor Sentiment in News and the Calendar Anomaly--New Evidence from a Large Textual Data." Available at SSRN 2394008.
15. Preis, T., et al. (2013). "Quantifying trading behavior in financial markets using Google Trends." Scientific reports 3: 1684.
16. Preis, T., et al. (2010). "Complex dynamics of our economic life on different scales: insights from search engine query data." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368(1933): 5707-5719.
17. Shi, Y. and K.-Y. Ho (2020). "News sentiment and states of stock return volatility: Evidence from long memory and discrete choice models." Finance Research Letters: 101446.
18. Sinha, N. R. (2014). Using Big Data in Finance: Example of Sentiment-Extraction from News Articles, Board of Governors of the Federal Reserve System (US).
19. Vakeel, K. and S. Dey (2014). Impact of News Articles on Stock Prices: An Analysis using Machine Learning. Proceedings of the 6th IBM Collaborative Academia Research Exchange Conference (I-CARE) on I-CARE 2014.
20. Yang, X., et al. (2020). "How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash." Pacific-Basin Finance Journal 59: 101245.

Machine learning

1. Akita, R., et al. (2016). Deep learning for stock prediction using numerical and textual information. 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE.
2. Alaa, A. M. and M. Van Der Schaar (2018). "A hidden absorbing semi-markov model for informatively censored temporal data: Learning and inference." The Journal of Machine Learning Research 19(1): 108-169.
3. Ay Karaku, B., et al. (2018). "Evaluating deep learning models for sentiment classification." Concurrency and Computation: Practice and Experience 30(21): e4783.
4. Chen, C., et al. (2018). "A two-stage penalized least squares method for constructing large systems of structural equations." The Journal of Machine Learning Research 19(1): 40-73.
5. Chong, E., et al. (2017). "Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies." Expert Systems with Applications 83: 187-205.
6. Das, A. and D. Kempe (2018). "Approximate submodularity and its applications: subset selection, sparse approximation and dictionary selection." The Journal of Machine Learning Research 19(1): 74-107.
7. Dash, R. and P. K. Dash (2016). "A hybrid stock trading framework integrating technical analysis with machine learning techniques." The Journal of Finance and Data Science 2(1): 42-57.
8. El Karoui, N. and E. Purdom (2018). "Can we trust the bootstrap in high-dimensions? the case of linear models." The Journal of Machine Learning Research 19(1): 170-235.
9. Fan, Y., et al. (2014). TTS synthesis with bidirectional LSTM based recurrent neural networks. Fifteenth Annual Conference of the International Speech Communication Association.
10. Graves, A., et al. (2013). Hybrid speech recognition with deep bidirectional LSTM. 2013 IEEE workshop on automatic speech recognition and understanding, IEEE.
11. Heaton, J., et al. (2017). "Deep learning for finance: deep portfolios." Applied Stochastic Models in Business and Industry 33(1): 3-12.
12. Jia, H. (2016). "Investigation into the effectiveness of long short term memory networks for stock price prediction." arXiv preprint arXiv:1603.07893.
13. Lukyanenko, R., et al. (2019). "Expecting the unexpected: Effects of data collection design choices on the quality of crowdsourced user-generated content." MIS Quarterly 43(2): 623-648.
14. Mnih, V., et al. (2013). "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602.
15. Patel, J., et al. (2015). "Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques." Expert Systems with Applications 42(1): 259-268.
16. Rather, A. M., et al. (2015). "Recurrent neural network and a hybrid model for prediction of stock returns." Expert Systems with Applications 42(6): 3234-3241.
17. Yoshihara, A., et al. (2014). Predicting stock market trends by recurrent deep neural networks. Pacific rim international conference on artificial intelligence, Springer.
18. Zhu, Y., et al. (2016). "Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models." Sustainability 8(5): 433.