| Reference: | Alpaydin, Ethem, 2010, Introduction to Machine Learning (MIT Press).
Amihud, Yakov, and Haim Mendelson, 1986, Liquidity and Stock Returns, Financial
Analysts Journal 42, 43–48.
Ang, Andrew, Robert J. Hodrick, Yuhang Xing, and Xiaoyan Zhang, 2006, The Cross-
Section of Volatility and Expected Returns, The Journal of Finance 61, 259–299. Bali, Turan G., and Nusret Cakici, 2008, Idiosyncratic Volatility and the Cross Section
of Expected Returns, Journal of Financial and Quantitative Analysis 43, 29–58.
Bao, Yanlin, 2023, Replication of Gu, Kelly and Xiu (2020, RFS), Re- trieved May 25, 2024, from https://colab.research.google.com/drive/ 1fcWNL5CgD21kuFDRLvYpEV8l9f5m2n9m#scrollTo=463f2485.
Blitz, David, Hoogteijling Tobias, Harald Lohre, and Messow Philip, 2023, How Can Machine Learning Advance Quantitative Asset Management?, The Journal of Port- folio Management 50, 31–63.
Buchanan, Lauren J., 2011, The Success of Long-Short Equity Strategies versus Tradi- tional Equity Strategies Market Returns.
Bui, Dien Giau, De-Rong Kong, Chih-Yung Lin, and Tse-Chun Lin, 2023, Momentum in machine learning: Evidence from the Taiwan stock market, Pacific-Basin Finance Journal 82, 102178.
Chen, Andrew Y., and Tom Zimmermann, 2021, Open Source Cross-Sectional Asset Pricing, Critical Financial Review 11, 207–264.
Chen, Tianqi, and Carlos Guestrin, 2016, XGBoost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794, ACM.
Dopfel, Frederick E., and Sunder R. Ramkumar, 2005, The Efficiency Gains of Long- Short Credit Strategies, The Journal of Fixed Income 15, 5–15.
Drobetz, Wolfgang, Fabian Hollstein, Tizian Otto, and Marcel Prokopczuk, 2024, Esti- mating Stock Market Betas via Machine Learning, Journal of Financial and Quanti- tative Analysis 1–56.
Fama, Eugene F., and Kenneth R. French, 2015, A Five-Factor Asset Pricing Model, Journal of Financial Economics 116, 1–22.
Freyberger, Joachim, Andreas Neuhierl, and Michael Weber, 2020, Dissecting Charac- teristics Nonparametrically, The Review of Financial Studies 33, 2326–2377.
Grinold, Richard C., and Ronald N. Kahn, 2000, The Efficiency Gains of Long–Short Investing, Financial Analysts Journal 56, 40–53.
Gu, Shihao, Bryan Kelly, and Dacheng Xiu, 2020, Empirical Asset Pricing via Machine Learning, The Review of Financial Studies 33, 2223–2273.
Hameed, Allaudeen, and Yuanto Kusnadi, 2002, Momentum Strategies: Evidence from Pacific Basin Stock Markets, The Journal of Financial Research 25, 383–397.
Heaton, J.B., Nick Polson, and Jan Witte, 2016, Deep Learning for Finance: Deep Port- folios, Applied Stochastic Models in Business and Industry 33, 3–12.
Htun, H.H., M. Biehl, and N. Petkov, 2023, Survey of feature selection and extraction techniques for stock market prediction, Financial Innovation 9.
Huber, Peter J, 1964, Robust estimation of a location parameter, The Annals of Mathe- matical Statistics 35, 73–101.
Jacobs, Bruce I., and Kenneth N. Levy, 1993, Long/Short Equity Investing, The Journal of Portfolio Management 20, 52–63.
Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, The Journal of Finance 48, 65–91.
Kelly, Bryan T., Seth Pruitt, and Yinan Su, 2019, Characteristics are covariances: A unified model of risk and return, Journal of Financial Economics 134, 501–524.
Kingma, Diederik P., and Jimmy Ba, 2015, Adam: A Method for Stochastic Optimiza- tion, in Proceedings of the 3rd International Conference for Learning Representa- tions, San Diego.
Lee, Yongjae, John R. J. Thompson, Jang Ho Kim, Woo Chang Kim, and Francesco A. Fabozzi, 2023, An Overview of Machine Learning for Asset Management, The Jour- nal of Portfolio Management 49, 31–63.
Lewellen, Jonathan, 2015, The Cross-section of Expected Stock Returns, Critical Fi- nance Review 20, 1–44.
Miffre, Joëlle, and Adrian Fernandez-Perez, 2015, The Case for Long-Short Commodity Investing, The Journal of Alternative Investments 18, 92–104.
Moritz, Benjamin, and Tom Zimmermann, 2016, Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns, Working Paper, Lud- wig Maximilian University of Munich.
Pástor, Ľuboš, and Robert F. Stambaugh, 2003, Liquidity Risk and Expected Stock Re- turns, Journal of Political Economy 111, 642–685.
Rapach, David, Jack Strauss, and Guofu Zhou, 2012, How Can Machine Learning Ad- vance Quantitative Asset Management?, Journal of Finance 68, 1633–1662.
Sadhwani, Apaar, Kay Giesecke, and Justin Sirignano, 2021, Deep Learning for Mort- gage Risk, Journal of Financial Econometrics 19, 313–368.
Samuel, A. L., 1959, Some Studies in Machine Learning Using the Game of Checkers, IBM Journal of Research and Development 3, 210–229.
Spearman, Charles, 1904, The proof and measurement of association between two things, American Journal of Psychology 15, 72–101.
Tol, Ramon, and Christiaan Wanningen, 2009, On the Performance of Extended Alpha (130/30) versus Long-Only, The Journal of Portfolio Management 35, 51–60.
Tol, Ramon, and Christiaan Wanningen, 2011, 130/30: By How Much Will the Infor- mation Ratio, The Journal of Portfolio Management 37, 62–69.
Tsai, Pei-Fen, Cheng-Han Gao, and Shyan-Ming Yuan, 2023, Stock Selection Using Machine Learning Based on Financial Ratios, Mathematics 11.
Turing, Alan M., 1950, Computing Machinery and Intelligence, Mind 59, 433–60. Waid, Robert J, 2009, Long-Only: The Natural Benchmark Choice for 130/30, The
Journal of Portfolio Management 35, 48–50.
Wang, Yun-Chin, Jean Yu, and Shiow-Ying Wen, 2014, Does Fundamental and Tech- nical Analysis Reduce Investment Risk for Growth Stock? An Analysis of Taiwan Stock Market, International Business Research 7, 24–34.
White, 1988, Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns, in IEEE 1988 International Conference on Neural Networks, 451– 458 vol.2.
蔣佳穎, 2022, 以財務指標預測台股橫斷面期望報酬 ，未出版之博 (碩) 士論文， 國立政治大學，國際經營與貿易學系，台北市. |
