online gambling singapore online gambling singapore online slot malaysia online slot malaysia mega888 malaysia slot gacor live casino malaysia online betting malaysia mega888 mega888 mega888 mega888 mega888 mega888 mega888 mega888 mega888 Paper

若讀者有推薦的網站可以在討論串留下相關訊息，我們會立即更新內容!!!

 

Big Data Analysis

  1. Moro Visconti, R., et al. (2018). "Big data-driven stochastic business planning and corporate valuation." Corporate Ownership & Control 15(3-1): 189-204.
  2. 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.
  3. Ahmed, O.,et al. (2018). " Big Data technologies: A survey " Journal of King Saud University - Computer and Information Sciences 30(4): 431-448.
  4. Singh, D., et al.(2015). " A survey on platforms for big data analytics. " Journal of big data, 2(1), 1-20.
  5. Fan, J., et al. (2014). " Challenges of big data analysis. " National science review, 1(2), 293-314.
  6. Muhtaroğlu, F. C. P., et al. (2013). " Business model canvas perspective on big data applications. " In 2013 IEEE International Conference on Big Data (pp. 32-37). IEEE.

 

Blockchain

  1. Natkamon Tovanich, et al. (2019). "Visualization of Blockchain Data: A Systematic Review." IEEE Transactions on Visualization and Computer Graphics. PP. 1-1. 10.1109/TVCG.2019.2963018.
  2. Daniel J. Moroz , et al. (2020). "Double-Spend Counterattacks: Threat of Retaliation in Proof-of-Work Systems."
  3. Kai-Min Chung , et al. (2020). "Game-Theoretically Fair Leader Election in O(log log n) Rounds under Majority Coalitions."
  4. Biais, B., et al. (2019). "The blockchain folk theorem." The Review of Financial Studies 32(5): 1662-1715.
  5. Chiu, J. and T. V. Koeppl (2019). "Blockchain-based settlement for asset trading." The Review of Financial Studies 32(5): 1716-1753.
  6. Cong, L. W. and Z. He (2019). "Blockchain disruption and smart contracts." The Review of Financial Studies 32(5): 1754-1797.
  7. Easley, D., et al. (2019). "From mining to markets: The evolution of bitcoin transaction fees." Journal of Financial Economics 134(1): 91-109.
  8. 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.
  9. Makarov, I. and A. Schoar (2020). "Trading and arbitrage in cryptocurrency markets." Journal of Financial Economics 135(2): 293-319.
  10. HamedGhoddusi,et al. (2019). " Machine learning in energy economics and finance: A review " Energy Economics 81: 709-727.
  11. Spiegeleer, J.,et al. (2018). " Machine learning for quantitative finance: fast derivative pricing, hedging and fitting " Taylor & Francis Online 18: 1635-1643.
  12. Farell, R., (2015). " An Analysis of the Cryptocurrency Industry " ScholarlyCommons
  13. Liu, Y.,et al. (2021). " Risks and Returns of Cryptocurrency " The Review of Financial Studies 34: 2689-2727.
  14. HaraldVranken (2017). " Sustainability of bitcoin and blockchains " Current Opinion in Environmental Sustainability 28: 1-9.
  15. Zheng Z.et al. (2018). " Blockchain challenges and opportunities: a survey " Int. J. Web and Grid Services, Volume 14, No 4

 

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.
  9. Liu, Y.,et al. (2021). " Risks and Returns of Cryptocurrency " The Review of Financial Studies 34: 2689-2727.

 

Cryptocurrency

  1. Lee, A. D., et al. (2020). "Bitcoin: Speculative asset or innovative technology?" Journal of International Financial Markets, Institutions and Money, 67(101209): 1042-4431.
  2. GRIFFIN, J. M., et al. (2020). "Is Bitcoin Really Untethered?" Journal of Finance, 75: 1913-1964.
  3. Dimpfl, T., et al. (2021). "Nothing but noise? Price discovery across cryptocurrency exchanges." Journal of Financial Markets, 54(100584).
  4. Liu, Y., et al. (2021). "Risks and Returns of Cryptocurrency." The Review of Financial Studies, 34(6): 2689-2727.
  5. Schilling L. and Uhlig H. (2019). " Some simple bitcoin economics " Journal of Monetary Economics 106: 16-26.
  6. Enoksena F.A., et al. (2020). " Understanding risk of bubbles in cryptocurrencies. " Journal of Economic Behavior and Organization, 176: 129-144.
  7. Geuder J., et al. (2019). " Cryptocurrencies as financial bubbles: The case of Bitcoin " Finance Research Letters, 31: 179-184.
  8. Liu, Y., et al. (2019). " Common risk factors in cryptocurrency " The Journal of Finance, 77(2): 1133-1177.
  9. . Enoksena F.A., et al. (2020). " Understanding risk of bubbles in cryptocurrencies " Journal of Economic Behavior and Organization, 176: 129-144.
  10. SOVBETOV Y., et al. (2018). " Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero " Journal of Economics and Financial Analysis, 2: 1-27.
  11. Koutmos D., et al. (2018). " Liquidity uncertainty and Bitcoin’s market microstructure " Economics Letters Volume, 172: 97-101.

 

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.
  7. Keke, G.,et al. (2018). "A survey on FinTech " Journal of Network and Computer Applications 103: 262-273.
  8. Thomas Philippon. (2016). " The Fintech Opportunity " NBER Working Paper No. 22476.
  9. Itay Goldstein,et al. (2019). " To FinTech and Beyond " The Review of Financial Studies, Volume 32, Issue 5, May 2019, Pages 1647–1661

 

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.

 

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. Mishra, S., & Rzeszotarski, J. M. (2021). "Designing Interactive Transfer Learning Tools for ML Non-Experts." The 2021 CHI Conference on Human Factors in Computing Systems, 364, 1-15.
  2. Yang, Q. (2013). "Big data, lifelong machine learning and transfer learning." The sixth ACM international conference on Web search and data mining, 505-506.
  3. 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.
  4. 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.
  5. Ay Karaku, B., et al. (2018). "Evaluating deep learning models for sentiment classification." Concurrency and Computation: Practice and Experience 30(21): e4783.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. Fan, Y., et al. (2014). TTS synthesis with bidirectional LSTM based recurrent neural networks. Fifteenth Annual Conference of the International Speech Communication Association.
  12. Graves, A., et al. (2013). Hybrid speech recognition with deep bidirectional LSTM. 2013 IEEE workshop on automatic speech recognition and understanding, IEEE.
  13. Heaton, J., et al. (2017). "Deep learning for finance: deep portfolios." Applied Stochastic Models in Business and Industry 33(1): 3-12.
  14. Jia, H. (2016). "Investigation into the effectiveness of long short term memory networks for stock price prediction." arXiv preprint arXiv:1603.07893.
  15. 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.
  16. Mnih, V., et al. (2013). "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602.
  17. 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.
  18. 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.
  19. Yoshihara, A., et al. (2014). Predicting stock market trends by recurrent deep neural networks. Pacific rim international conference on artificial intelligence, Springer.
  20. 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

  1. 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.
  2. 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.
  3. Wu, K., et al. (2013). "A big data approach to analyzing market volatility." Algorithmic Finance 2(3-4): 241-267.
  4. Wu, K., et al. (2013). "Testing VPIN on Big Data–Response to'Reflecting on the VPIN Dispute'." Available at SSRN 2318259.
  5. O’hara, M. (2015). " High frequency market microstructure. " Journal of financial economics, 116(2), 257-270.
  6. Madhavan, A. (2000). " Market microstructure: A survey. " Journal of financial markets, 3(3), 205-258.
  7. Dimpfl, T. (2017). " Bitcoin market microstructure " Available at SSRN 2949807.
  8. Choi, K. J., et al.(2022). " Bitcoin microstructure and the Kimchi premium. " Available at SSRN 3189051.

 

Market Prediction

  1. Andreou, E., et al. (2013). "Should macroeconomic forecasters use daily financial data and how?" Journal of Business & Economic Statistics 31(2): 240-251.
  2. Araujo, R. d. A., et al. (2015). "A hybrid model for high-frequency stock market forecasting." Expert Systems with Applications 42(8): 4081-4096.
  3. 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."
  4. Oliveira, N. M. d. R. (2013). Mining microblogging data to model and forecast stock market behavior.
  5. Pillemer, J., et al. (2014). "The face says it all: CEOs, gender, and predicting corporate performance." The Leadership Quarterly 25(5): 855-864.
  6. 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

  1. Cetorelli, N. and S. Peristiani (2013). "Prestigious stock exchanges: A network analysis of international financial centers." Journal of Banking & Finance 37(5): 1543-1551.
  2. Dimitrios, K. and O. Vasileios (2015). "A network analysis of the Greek stock market." Procedia Economics and Finance 33: 340-349.
  3. 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.
  4. Heiberger, R. H. (2014). "Stock network stability in times of crisis." Physica A: Statistical Mechanics and its Applications 393: 376-381.
  5. Massara, G. P., et al. (2016). "Network filtering for big data: Triangulated maximally filtered graph." Journal of complex Networks 5(2): 161-178.
  6. Ozsoylev, H. N., et al. (2014). "Investor networks in the stock market." The Review of Financial Studies 27(5): 1323-1366.
  7. Peralta, G. and A. Zareei (2016). "A network approach to portfolio selection." Journal of empirical finance 38: 157-180.
  8. 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

  1. 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.
  2. Chen, J., et al. (2015). "Big data based fraud risk management at Alibaba." The Journal of Finance and Data Science 1(1): 1-10.
  3. 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.
  4. 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.
  5. Swedloff, R. (2014). "Risk classification's big data (r) evolution." Conn. Ins. LJ 21: 339.
  6. 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

  1. Bholat, D., et al. (2015). "Text mining for central banks." Available at SSRN 2624811.
  2. 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.
  3. Bijalwan, V., et al. (2014). "Machine learning approach for text and document mining." arXiv preprint arXiv:1406.1580.
  4. 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.
  5. Dong, M., et al. (2019). "Textual Analysis of Banks' Pillar 3 Documents." Available at SSRN 3365005.
  6. Gong, J., et al. (2017). "Examining the impact of keyword ambiguity on search advertising performance: A topic model approach." MIS Quarterly (Forthcoming).
  7. 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.
  8. 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.
  9. 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.
  10. Nassirtoussi, A. K., et al. (2014). "Text mining for market prediction: A systematic review." Expert Systems with Applications 41(16): 7653-7670.
  11. 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.
  12. 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.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Web Services

YOU MAY BE INTERESTED

Scraper API

Popular Tags