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Asset Pricing
- 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.
- Constantinides, G. M. and A. Ghosh (2017). "Asset pricing with countercyclical household consumption risk." The Journal of Finance 72(1): 415-460.
- Fama, E. F. and K. R. French (2018). "Long-horizon returns." The Review of Asset Pricing Studies 8(2): 232-252.
- Kroencke, T. A. (2017). "Asset pricing without garbage." The Journal of Finance 72(1): 47-98.
- 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.
- 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.
- Tetlock, P. C., et al. (2008). "More than words: Quantifying language to measure firms' fundamentals." The Journal of Finance 63(3): 1437-1467.
- Tuzel, S. and M. B. Zhang (2017). "Local risk, local factors, and asset prices." The Journal of Finance 72(1): 325-370.
Blockchain
- Biais, B., et al. (2019). "The blockchain folk theorem." The Review of Financial Studies 32(5): 1662-1715.
- Chiu, J. and T. V. Koeppl (2019). "Blockchain-based settlement for asset trading." The Review of Financial Studies 32(5): 1716-1753.
- Cong, L. W. and Z. He (2019). "Blockchain disruption and smart contracts." The Review of Financial Studies 32(5): 1754-1797.
- Easley, D., et al. (2019). "From mining to markets: The evolution of bitcoin transaction fees." Journal of Financial Economics 134(1): 91-109.
- 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.
- Makarov, I. and A. Schoar (2020). "Trading and arbitrage in cryptocurrency markets." Journal of Financial Economics 135(2): 293-319.
Corporate Finance
- Benitez, J., et al. (2018). "Impact of information technology infrastructure flexibility on mergers and acquisitions." MIS Quarterly 42(1).
- Berente, N., et al. (2019). "Institutional logics and pluralistic responses to enterprise system implementation: a qualitative meta-analysis." MIS Quarterly 43(3): 873-902.
- Choi, T. M., et al. (2018). "Big data analytics in operations management." Production and Operations Management 27(10): 1868-1883.
- Fanning, K. and E. Drogt (2014). "Big data: New opportunities for M&A." Journal of Corporate Accounting & Finance 25(2): 27-34.
- Fanning, K. and R. Grant (2013). "Big data: implications for financial managers." Journal of Corporate Accounting & Finance 24(5): 23-30.
- Mills, J. (2014). "CEO facial width predicts firm financial policies." Available at SSRN 2503582.
- Onay, C. and E. Ozturk (2018). "A review of credit scoring research in the age of Big Data." Journal of Financial Regulation and Compliance.
- Yermack, D. (2017). "Corporate governance and blockchains." Review of Finance 21(1): 7-31.
Data Mining
- 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.
- Bijalwan, V., et al. (2014). "Machine learning approach for text and document mining." arXiv preprint arXiv:1406.1580.
- 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.
- Dong, M., et al. (2019). "Textual Analysis of Banks' Pillar 3 Documents." Available at SSRN 3365005.
- Gong, J., et al. (2017). "Examining the impact of keyword ambiguity on search advertising performance: A topic model approach." MIS Quarterly (Forthcoming).
- 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.
- 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.
- 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.
- 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
- Chen, M. A., et al. (2019). "How valuable is FinTech innovation?" The Review of Financial Studies 32(5): 2062-2106.
- D’Acunto, F., et al. (2019). "The promises and pitfalls of robo-advising." The Review of Financial Studies 32(5): 1983-2020.
- Schnabl, P., et al. (2018). "The Role of Technology in Mortgage Lending."
- Tang, H. (2019). "Peer-to-peer lenders versus banks: substitutes or complements?" The Review of Financial Studies 32(5): 1900-1938.
- Vallee, B. and Y. Zeng (2019). "Marketplace lending: a new banking paradigm?" The Review of Financial Studies 32(5): 1939-1982.
- Zhu, C. (2019). "Big data as a governance mechanism." The Review of Financial Studies 32(5): 2021-2061.
High Frequency Trading
- 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.
- 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.
- Bacry, E. and J.-F. Muzy (2014). "Hawkes model for price and trades high-frequency dynamics." Quantitative Finance 14(7): 1147-1166.
- Brogaard, J., et al. (2018). "High frequency trading and extreme price movements." Journal of Financial Economics 128(2): 253-265.
- Cespa, G. and X. Vives (2015). "The beauty contest and shortterm trading." The Journal of Finance 70(5): 2099-2154.
- CollinDufresne, P. and V. Fos (2015). "Do prices reveal the presence of informed trading?" The Journal of Finance 70(4): 1555-1582.
- Conrad, J., et al. (2015). "High-frequency quoting, trading, and the efficiency of prices." Journal of Financial Economics 116(2): 271-291.
- Davis, R. L., et al. (2014). "Clustering of Trade Prices by HighFrequency and Non–HighFrequency Trading Firms." Financial Review 49(2): 421-433.
- Foucault, T., et al. (2016). "News trading and speed." The Journal of Finance 71(1): 335-382.
- Ho, K. Y., et al. (2015). High-frequency news flow and states of asset volatility. Handbook of High Frequency Trading, Elsevier: 359-383.
- Hoffmann, M., et al. (2014). "Optimization and statistical methods for high frequency finance." ESAIM: Proceedings and Surveys 45: 219-228.
- Kirilenko, A., et al. (2017). "‘Flash Crash’: The first market crash in the era of algorithms and automated trading." LSE Business Review.
- 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.
- Pan, W., et al. (2013). "Can High-Frequency Trading Drive the Stock Market Off a Cliff?" MIT Sloan Management Review 54(4): 16.
- 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
- Anand, A., et al. (2019). "Does institutional trading affect underwriting?" Journal of Corporate Finance 58: 101495.
- Chong, B. S. and Z. Liu (2020). "Issuer IPO underpricing and Directed Share Program (DSP)." Journal of Empirical Finance 56: 105-125.
- Gao, S., et al. (2020). "Differences of opinion, institutional bids, and IPO underpricing." Journal of Corporate Finance 60: 101540.
- 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.
- 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.
- 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.
- 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.
- 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
- Alanyali, M., et al. (2013). "Quantifying the relationship between financial news and the stock market." Scientific reports 3: 3578.
- Alfano, S. J., et al. (2015). "Is news sentiment more than just noise?".
- 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.
- 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.
- Guerard, J. B., et al. (2013). "Global stock selection modeling and efficient portfolio construction and management." The Journal of Investing 22(4): 121-128.
- 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.
- 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.
- 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.
- Kim, K. and S. Viswanathan (2018). "The'experts' in the crowd: The role of experienced investors in a crowdfunding market." Mis Quarterly.
- 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.
- Moat, H. S., et al. (2013). "Quantifying Wikipedia usage patterns before stock market moves." Scientific reports 3: 1801.
- 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.
- 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.
- 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.
- Preis, T., et al. (2013). "Quantifying trading behavior in financial markets using Google Trends." Scientific reports 3: 1684.
- 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.
- 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.
- 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).
- 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.
- 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
- 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.
- 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.
- Ay Karaku, B., et al. (2018). "Evaluating deep learning models for sentiment classification." Concurrency and Computation: Practice and Experience 30(21): e4783.
- 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.
- 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.
- 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.
- 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.
- 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.
- Fan, Y., et al. (2014). TTS synthesis with bidirectional LSTM based recurrent neural networks. Fifteenth Annual Conference of the International Speech Communication Association.
- Graves, A., et al. (2013). Hybrid speech recognition with deep bidirectional LSTM. 2013 IEEE workshop on automatic speech recognition and understanding, IEEE.
- Heaton, J., et al. (2017). "Deep learning for finance: deep portfolios." Applied Stochastic Models in Business and Industry 33(1): 3-12.
- Jia, H. (2016). "Investigation into the effectiveness of long short term memory networks for stock price prediction." arXiv preprint arXiv:1603.07893.
- 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.
- Mnih, V., et al. (2013). "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602.
- 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.
- 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.
- Yoshihara, A., et al. (2014). Predicting stock market trends by recurrent deep neural networks. Pacific rim international conference on artificial intelligence, Springer.
- 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.
Market Microstructure
- Andersen, T. G. and O. Bondarenko (2013). "Comments on'Testing VPIN on Big Data-Response to Reflecting on the VPIN Dispute'." Available at SSRN 2331106.
- Andersen, T. G. and O. Bondarenko (2015). "Assessing measures of order flow toxicity and early warning signals for market turbulence." Review of Finance 19(1): 1-54.
- Wu, K., et al. (2013). "A big data approach to analyzing market volatility." Algorithmic Finance 2(3-4): 241-267.
- Wu, K., et al. (2013). "Testing VPIN on Big Data–Response to'Reflecting on the VPIN Dispute'." Available at SSRN 2318259.
Market Prediction
- Andreou, E., et al. (2013). "Should macroeconomic forecasters use daily financial data and how?" Journal of Business & Economic Statistics 31(2): 240-251.
- Araujo, R. d. A., et al. (2015). "A hybrid model for high-frequency stock market forecasting." Expert Systems with Applications 42(8): 4081-4096.
- Camba-Mendez, G., et al. (2014). "An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies."
- Oliveira, N. M. d. R. (2013). Mining microblogging data to model and forecast stock market behavior.
- Pillemer, J., et al. (2014). "The face says it all: CEOs, gender, and predicting corporate performance." The Leadership Quarterly 25(5): 855-864.
- Ranco, G., et al. (2016). "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics." PLoS one 11(1).
Network Analysis
- Cetorelli, N. and S. Peristiani (2013). "Prestigious stock exchanges: A network analysis of international financial centers." Journal of Banking & Finance 37(5): 1543-1551.
- Dimitrios, K. and O. Vasileios (2015). "A network analysis of the Greek stock market." Procedia Economics and Finance 33: 340-349.
- 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.
- Heiberger, R. H. (2014). "Stock network stability in times of crisis." Physica A: Statistical Mechanics and its Applications 393: 376-381.
- Massara, G. P., et al. (2016). "Network filtering for big data: Triangulated maximally filtered graph." Journal of complex Networks 5(2): 161-178.
- Ozsoylev, H. N., et al. (2014). "Investor networks in the stock market." The Review of Financial Studies 27(5): 1323-1366.
- Peralta, G. and A. Zareei (2016). "A network approach to portfolio selection." Journal of empirical finance 38: 157-180.
- Wang, F., et al. (2017). "Analyzing entrepreneurial social networks with big data." Annals of the American Association of Geographers 107(1): 130-150.
Risk Management
- Brogaard, J., et al. (2017). "Stock liquidity and default risk." Journal of Financial Economics 124(3): 486-502.
- Butaru, F., et al. (2016). "Risk and risk management in the credit card industry." Journal of Banking & Finance 72: 218-239.
- Cerchiello, P. and P. Giudici (2014). "Financial big data analysis for the estimation of systemic risks." Universita di Pavia Department of Economics and Management Working Paper 86.
- Chen, J., et al. (2015). "Big data based fraud risk management at Alibaba." The Journal of Finance and Data Science 1(1): 1-10.
- Choi, T.-M., et al. (2016). "Recent development in big data analytics for business operations and risk management." IEEE transactions on cybernetics 47(1): 81-92.
- Habib, A. and M. M. Hasan (2017). "Managerial ability, investment efficiency and stock price crash risk." Research in International Business and Finance 42: 262-274.
- Kristoufek, L. (2013). "Can Google Trends search queries contribute to risk diversification?" Scientific reports 3: 2713.
- Li, S. and X. Zhan (2019). "Product market threats and stock crash risk." Management Science 65(9): 4011-4031.
- Niesen, T., et al. (2016). Towards an integrative big data analysis framework for data-driven risk management in industry 4.0. 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE.
- Swedloff, R. (2014). "Risk classification's big data (r) evolution." Conn. Ins. LJ 21: 339.
- Zhou, Q. and J. Luo (2015). "The risk management using limit theory of statistics on extremes on the big data era." Journal of Computational and Theoretical Nanoscience 12(12): 6237-6243.
Textual Analysis
- Bholat, D., et al. (2015). "Text mining for central banks." Available at SSRN 2624811.
- 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.
- Bijalwan, V., et al. (2014). "Machine learning approach for text and document mining." arXiv preprint arXiv:1406.1580.
- 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.
- Dong, M., et al. (2019). "Textual Analysis of Banks' Pillar 3 Documents." Available at SSRN 3365005.
- Gong, J., et al. (2017). "Examining the impact of keyword ambiguity on search advertising performance: A topic model approach." MIS Quarterly (Forthcoming).
- 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.
- Kim, Y., et al. (2014). "Text opinion mining to analyze news for stock market prediction." Int. J. Advance. Soft Comput. Appl 6(1): 2074-8523.
- 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.
- Nassirtoussi, A. K., et al. (2014). "Text mining for market prediction: A systematic review." Expert Systems with Applications 41(16): 7653-7670.
- 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.
- 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.
Asset Pricing
- 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.
- Constantinides, G. M. and A. Ghosh (2017). "Asset pricing with countercyclical household consumption risk." The Journal of Finance 72(1): 415-460.
- Fama, E. F. and K. R. French (2018). "Long-horizon returns." The Review of Asset Pricing Studies 8(2): 232-252.
- Kroencke, T. A. (2017). "Asset pricing without garbage." The Journal of Finance 72(1): 47-98.
- 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.
- 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.
- Tuzel, S. and M. B. Zhang (2017). "Local risk, local factors, and asset prices." The Journal of Finance 72(1): 325-370.
Blockchain
- Biais, B., et al. (2019). "The blockchain folk theorem." The Review of Financial Studies 32(5): 1662-1715.
- Chiu, J. and T. V. Koeppl (2019). "Blockchain-based settlement for asset trading." The Review of Financial Studies 32(5): 1716-1753.
- Cong, L. W. and Z. He (2019). "Blockchain disruption and smart contracts." The Review of Financial Studies 32(5): 1754-1797.
- Easley, D., et al. (2019). "From mining to markets: The evolution of bitcoin transaction fees." Journal of Financial Economics 134(1): 91-109.
- 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.
- Makarov, I. and A. Schoar (2020). "Trading and arbitrage in cryptocurrency markets." Journal of Financial Economics 135(2): 293-319.
Corporate Finance
- Benitez, J., et al. (2018). "Impact of information technology infrastructure flexibility on mergers and acquisitions." MIS Quarterly 42(1).
- Berente, N., et al. (2019). "Institutional logics and pluralistic responses to enterprise system implementation: a qualitative meta-analysis." MIS Quarterly 43(3): 873-902.
- Choi, T. M., et al. (2018). "Big data analytics in operations management." Production and Operations Management 27(10): 1868-1883.
- Fanning, K. and E. Drogt (2014). "Big data: New opportunities for M&A." Journal of Corporate Accounting & Finance 25(2): 27-34.
- Fanning, K. and R. Grant (2013). "Big data: implications for financial managers." Journal of Corporate Accounting & Finance 24(5): 23-30.
- Mills, J. (2014). "CEO facial width predicts firm financial policies." Available at SSRN 2503582.
- Onay, C. and E. Ozturk (2018). "A review of credit scoring research in the age of Big Data." Journal of Financial Regulation and Compliance.
- Yermack, D. (2017). "Corporate governance and blockchains." Review of Finance 21(1): 7-31.
Data Mining
- 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.
- Bijalwan, V., et al. (2014). "Machine learning approach for text and document mining." arXiv preprint arXiv:1406.1580.
- 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.
- Dong, M., et al. (2019). "Textual Analysis of Banks' Pillar 3 Documents." Available at SSRN 3365005.
- Gong, J., et al. (2017). "Examining the impact of keyword ambiguity on search advertising performance: A topic model approach." MIS Quarterly (Forthcoming).
- 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.
- 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.
- 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.
- 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
- Chen, M. A., et al. (2019). "How valuable is FinTech innovation?" The Review of Financial Studies 32(5): 2062-2106.
- D’Acunto, F., et al. (2019). "The promises and pitfalls of robo-advising." The Review of Financial Studies 32(5): 1983-2020.
- Schnabl, P., et al. (2018). "The Role of Technology in Mortgage Lending."
- Tang, H. (2019). "Peer-to-peer lenders versus banks: substitutes or complements?" The Review of Financial Studies 32(5): 1900-1938.
- Vallee, B. and Y. Zeng (2019). "Marketplace lending: a new banking paradigm?" The Review of Financial Studies 32(5): 1939-1982.
- Zhu, C. (2019). "Big data as a governance mechanism." The Review of Financial Studies 32(5): 2021-2061.
High Frequency Trading
- 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.
- 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.
- Bacry, E. and J.-F. Muzy (2014). "Hawkes model for price and trades high-frequency dynamics." Quantitative Finance 14(7): 1147-1166.
- Brogaard, J., et al. (2018). "High frequency trading and extreme price movements." Journal of Financial Economics 128(2): 253-265.
- Cespa, G. and X. Vives (2015). "The beauty contest and shortterm trading." The Journal of Finance 70(5): 2099-2154.
- CollinDufresne, P. and V. Fos (2015). "Do prices reveal the presence of informed trading?" The Journal of Finance 70(4): 1555-1582.
- Conrad, J., et al. (2015). "High-frequency quoting, trading, and the efficiency of prices." Journal of Financial Economics 116(2): 271-291.
- Davis, R. L., et al. (2014). "Clustering of Trade Prices by HighFrequency and Non–HighFrequency Trading Firms." Financial Review 49(2): 421-433.
- Foucault, T., et al. (2016). "News trading and speed." The Journal of Finance 71(1): 335-382.
- Ho, K. Y., et al. (2015). High-frequency news flow and states of asset volatility. Handbook of High Frequency Trading, Elsevier: 359-383.
- Hoffmann, M., et al. (2014). "Optimization and statistical methods for high frequency finance." ESAIM: Proceedings and Surveys 45: 219-228.
- Kirilenko, A., et al. (2017). "‘Flash Crash’: The first market crash in the era of algorithms and automated trading." LSE Business Review.
- 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.
- Pan, W., et al. (2013). "Can High-Frequency Trading Drive the Stock Market Off a Cliff?" MIT Sloan Management Review 54(4): 16.
- 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
- Anand, A., et al. (2019). "Does institutional trading affect underwriting?" Journal of Corporate Finance 58: 101495.
- Chong, B. S. and Z. Liu (2020). "Issuer IPO underpricing and Directed Share Program (DSP)." Journal of Empirical Finance 56: 105-125.
- Gao, S., et al. (2020). "Differences of opinion, institutional bids, and IPO underpricing." Journal of Corporate Finance 60: 101540.
- 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.
- 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.
- 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.
- 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.
- 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
- Alanyali, M., et al. (2013). "Quantifying the relationship between financial news and the stock market." Scientific reports 3: 3578.
- Alfano, S. J., et al. (2015). "Is news sentiment more than just noise?".
- 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.
- 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.
- Guerard, J. B., et al. (2013). "Global stock selection modeling and efficient portfolio construction and management." The Journal of Investing 22(4): 121-128.
- 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.
- 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.
- 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.
- Kim, K. and S. Viswanathan (2018). "The'experts' in the crowd: The role of experienced investors in a crowdfunding market." Mis Quarterly.
- 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.
- Moat, H. S., et al. (2013). "Quantifying Wikipedia usage patterns before stock market moves." Scientific reports 3: 1801.
- 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.
- 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.
- 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.
- Preis, T., et al. (2013). "Quantifying trading behavior in financial markets using Google Trends." Scientific reports 3: 1684.
- 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.
- 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.
- 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).
- 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.
- 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
- 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.
- 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.
- Ay Karaku, B., et al. (2018). "Evaluating deep learning models for sentiment classification." Concurrency and Computation: Practice and Experience 30(21): e4783.
- 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.
- 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.
- 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.
- 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.
- 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.
- Fan, Y., et al. (2014). TTS synthesis with bidirectional LSTM based recurrent neural networks. Fifteenth Annual Conference of the International Speech Communication Association.
- Graves, A., et al. (2013). Hybrid speech recognition with deep bidirectional LSTM. 2013 IEEE workshop on automatic speech recognition and understanding, IEEE.
- Heaton, J., et al. (2017). "Deep learning for finance: deep portfolios." Applied Stochastic Models in Business and Industry 33(1): 3-12.
- Jia, H. (2016). "Investigation into the effectiveness of long short term memory networks for stock price prediction." arXiv preprint arXiv:1603.07893.
- 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.
- Mnih, V., et al. (2013). "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602.
- 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.
- 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.
- Yoshihara, A., et al. (2014). Predicting stock market trends by recurrent deep neural networks. Pacific rim international conference on artificial intelligence, Springer.
- 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.
Market Microstructure
- Andersen, T. G. and O. Bondarenko (2013). "Comments on'Testing VPIN on Big Data-Response to Reflecting on the VPIN Dispute'." Available at SSRN 2331106.
- Andersen, T. G. and O. Bondarenko (2015). "Assessing measures of order flow toxicity and early warning signals for market turbulence." Review of Finance 19(1): 1-54.
- Wu, K., et al. (2013). "A big data approach to analyzing market volatility." Algorithmic Finance 2(3-4): 241-267.
- Wu, K., et al. (2013). "Testing VPIN on Big Data–Response to'Reflecting on the VPIN Dispute'." Available at SSRN 2318259.
Market Prediction
- Andreou, E., et al. (2013). "Should macroeconomic forecasters use daily financial data and how?" Journal of Business & Economic Statistics 31(2): 240-251.
- Araujo, R. d. A., et al. (2015). "A hybrid model for high-frequency stock market forecasting." Expert Systems with Applications 42(8): 4081-4096.
- Camba-Mendez, G., et al. (2014). "An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies."
- Oliveira, N. M. d. R. (2013). Mining microblogging data to model and forecast stock market behavior.
- Pillemer, J., et al. (2014). "The face says it all: CEOs, gender, and predicting corporate performance." The Leadership Quarterly 25(5): 855-864.
- Ranco, G., et al. (2016). "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics." PLoS one 11(1).
Network Analysis
- Cetorelli, N. and S. Peristiani (2013). "Prestigious stock exchanges: A network analysis of international financial centers." Journal of Banking & Finance 37(5): 1543-1551.
- Dimitrios, K. and O. Vasileios (2015). "A network analysis of the Greek stock market." Procedia Economics and Finance 33: 340-349.
- 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.
- Heiberger, R. H. (2014). "Stock network stability in times of crisis." Physica A: Statistical Mechanics and its Applications 393: 376-381.
- Massara, G. P., et al. (2016). "Network filtering for big data: Triangulated maximally filtered graph." Journal of complex Networks 5(2): 161-178.
- Ozsoylev, H. N., et al. (2014). "Investor networks in the stock market." The Review of Financial Studies 27(5): 1323-1366.
- Peralta, G. and A. Zareei (2016). "A network approach to portfolio selection." Journal of empirical finance 38: 157-180.
- Wang, F., et al. (2017). "Analyzing entrepreneurial social networks with big data." Annals of the American Association of Geographers 107(1): 130-150.
Risk Management
- Brogaard, J., et al. (2017). "Stock liquidity and default risk." Journal of Financial Economics 124(3): 486-502.
- Butaru, F., et al. (2016). "Risk and risk management in the credit card industry." Journal of Banking & Finance 72: 218-239.
- Cerchiello, P. and P. Giudici (2014). "Financial big data analysis for the estimation of systemic risks." Universita di Pavia Department of Economics and Management Working Paper 86.
- Chen, J., et al. (2015). "Big data based fraud risk management at Alibaba." The Journal of Finance and Data Science 1(1): 1-10.
- Choi, T.-M., et al. (2016). "Recent development in big data analytics for business operations and risk management." IEEE transactions on cybernetics 47(1): 81-92.
- Habib, A. and M. M. Hasan (2017). "Managerial ability, investment efficiency and stock price crash risk." Research in International Business and Finance 42: 262-274.
- Kristoufek, L. (2013). "Can Google Trends search queries contribute to risk diversification?" Scientific reports 3: 2713.
- Li, S. and X. Zhan (2019). "Product market threats and stock crash risk." Management Science 65(9): 4011-4031.
- Niesen, T., et al. (2016). Towards an integrative big data analysis framework for data-driven risk management in industry 4.0. 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE.
- Swedloff, R. (2014). "Risk classification's big data (r) evolution." Conn. Ins. LJ 21: 339.
- Zhou, Q. and J. Luo (2015). "The risk management using limit theory of statistics on extremes on the big data era." Journal of Computational and Theoretical Nanoscience 12(12): 6237-6243.
Textual Analysis
- Bholat, D., et al. (2015). "Text mining for central banks." Available at SSRN 2624811.
- 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.
- Bijalwan, V., et al. (2014). "Machine learning approach for text and document mining." arXiv preprint arXiv:1406.1580.
- 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.
- Dong, M., et al. (2019). "Textual Analysis of Banks' Pillar 3 Documents." Available at SSRN 3365005.
- Dougal, C., et al. (2012). "Journalists and the stock market." The Review of Financial Studies 25(3): 639-679.
- Gong, J., et al. (2017). "Examining the impact of keyword ambiguity on search advertising performance: A topic model approach." MIS Quarterly (Forthcoming).
- 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.
- Kim, Y., et al. (2014). "Text opinion mining to analyze news for stock market prediction." Int. J. Advance. Soft Comput. Appl 6(1): 2074-8523.
- 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.
- Nassirtoussi, A. K., et al. (2014). "Text mining for market prediction: A systematic review." Expert Systems with Applications 41(16): 7653-7670.
- 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.
- 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.
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