ADS Capstone Chronicles Revised

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785 – 794). https://doi.org/10.1145/2939672.2939785 Chen, Z., Li, C., & Wu, Y. (2018). The impact of social media sentiment on cryptocurrency prices. Journal of Economic Behavior & Organization, 146, 334-342. Fama, E. F., & French, K. R. (2015). A five factor asset pricing model. Journal of Financial Economics, 116 (1), 1 – 22. https://doi.org/10.1016/j.jfineco.2014.10.010 Fang, Y., & Wang, L. (2021). Imputation methods for time series data: A comparative study. Journal of Data Science and Analytics, 15(3), 235-250. https://doi:10.1007/s41060-021 00252-5 Granger, C. W. J., & Newbold, P. (2014). Forecasting economic time series. Academic Press.

impact Bitcoin value? A test of the silent majority hypothesis. Journal of Management Information Systems, 35 (1), 19 – 52. https://doi.org/10.1080/07421222.2018.1440774 McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of Bitcoin using Machine Learning. 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE. https://doi.org/10.1109/pdp2018.2018.00060 Mittal, A., & Goel, A. (2012). Stock prediction using Twitter sentiment analysis. Stanford University. Murphy, J. J. (2018). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications . New York Institute of Finance. Nakamura, S., & Eguchi, S. (2017). Predicting stock market trends by recurrent deep neural networks. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Reiff, N. (2023). Why bitcoin’s value is so volatile . Investopedia. https://www.investopedia.com/articles/investing /052014/why-bitcoins-value-so-volatile.asp Sattarov, O., Muminov, A., Lee, C. W., Kang, H. K., Oh, R., Ahn, J., Oh, H. J., & Jeon, H. S. (2020). Recommending cryptocurrency trading points with deep reinforcement learning approach. Applied Sciences, 10 (4), 1506. https://doi.org/10.3390/app10041506

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice . OTexts.

Kim, S., & Kim, D. (2019). Cryptocurrency price prediction based on social media sentiment analysis. Journal of Finance and Data Science, 5 (1), 25 – 33. Kristoufek, L. (2013). Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet

era. Scientific Reports, 3 (1), 3415. https://doi.org/10.1038/srep03415

Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004). Applied linear regression models . McGraw-Hill/Irwin.

Mai, F., Shan, Z., Bai, Q., Wang, X. S., & Chiang, R. H. (2018). How does social media

91