References
- Amenc, N. and Martellini, L., It's time for asset allocation. J. Financ. Transf., 2001, 3, 77–88.
- Artzner, P., Delbaen, F., Eber, J.M. and Heath, D., Coherent measures of risk. Math. Finance, 1999, 9(3), 203–228.
- Ban, G.Y.. and Rudin, C., The big data newsvendor: Practical insights from machine learning. Oper. Res., 2019, 67(1), 90–108.
- Bauer, M.D. and Mertens, T.M., Information in the yield curve about future recessions. FRBSF Econ. Lett., 2018, 20, 1–5.
- Bertsimas, D. and Kallus, N., From predictive to prescriptive analytics. Manag. Sci., 2020, 66(3), 1025–1044.
- Bogle, J.C., Stay the Course: The Story of Vanguard and the Index Revolution, 2018 (John Wiley & Sons: Hoboken, NJ).
- Campello, R.J., Moulavi, D. and Sander, J., Density-based clustering based on hierarchical density estimates. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 160–172, 2013.
- Chen, X., Owen, Z., Pixton, C. and Simchi-Levi, D., A statistical learning approach to personalization in revenue management. Manag. Sci., 2022, 68(3), 1923–1937.
- DeMiguel, V., Garlappi, L. and Uppal, R., Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Rev. Financ. Stud., 2009, 22(5), 1915–1953.
- Eckerli, F. and Osterrieder, J., Generative adversarial networks in finance: An overview, 2021. arXiv preprint arXiv: 2106.06364.
- Elton, E.J., Gruber, M.J. and de Souza, A., Are passive funds really superior investments? An investor perspective. Financ. Anal. J., 2019, 75(3), 7–19.
- Estrella, A. and Trubin, M., The yield curve as a leading indicator: Some practical issues. Curr. Issues Econ. Finance, 2006, 12(5).
- Evgenidis, A., Papadamou, S. and Siriopoulos, C., The yield spread's ability to forecast economic activity: What have we learned after 30 years of studies? J. Bus. Res., 2020, 106, 221–232.
- Fabozzi, F.J., Fabozzi, F.A., López de Prado, M. and Stoyanov, S.V., Asset Management: Tools and Issues, pp. 1–7, 2021 (World Scientific: Singapore).
- Fahling, E.J., Steurer, E. and Sauer, S., Active vs. passive funds—An empirical analysis of the German equity market. J. Financ. Risk Manag., 2019, 8(2), 73.
- Friedman, D., Isaac, R.M., James, D. and Sunder, S., Risky Curves: On the Empirical Failure of Expected Utility, 2014 (Routledge: New York, NY).
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., Generative adversarial networks, 2014. arXiv. Retrieved from https://arxiv.org/abs/1406.2661.
- Gutierrez, T., Pagnoncelli, B., Valladão, D. and Cifuentes, A., Can asset allocation limits determine portfolio risk–return profiles in DC pension schemes? Insur. Math. Econ., 2019, 86, 134–144. Retrieved from https://www.sciencedirect.com/science/article/pii/S0167668718301331.
- Hamilton, J.D., Rational-expectations econometric analysis of changes in regime: An investigation of the term structure of interest rates. J. Econ. Dyn. Control, 1988, 12(2–3), 385–423. Retrieved from https://www.sciencedirect.com/science/article/pii/0165188988900474.
- Hamilton, J.D., A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 1989, 57(2), 357–384.
- Hu, Y., Kallus, N. and Mao, X., Fast rates for contextual linear optimization. Manag. Sci., 2022, 68(6), 3975–4753, iv–v.
- Ibbotson, R.G., The importance of asset allocation. Financ. Anal. J., 2010, 66(2), 18–20. Retrieved from https://doi.org/10.2469/faj.v66.n2.4
- Kolm, P.N., Tütüncü, R. and Fabozzi, F.J., 60 Years of portfolio optimization: Practical challenges and current trends. Eur. J. Oper. Res., 2014, 234(2), 356–371. Retrieved from https://www.sciencedirect.com/science/article/pii/S0377221713008898 (60 years following Harry Markowitz's contribution to portfolio theory and operations research).
- Krokhmal, P., Uryasev, S. and Palmquist, J., Portfolio optimization with conditional value-at-risk objective and constraints. J. Risk, 2001, 4(2), 43–68.
- Kumar, R.R., Stauvermann, P.J. and Vu, H.T.T., The relationship between yield curve and economic activity: An analysis of G7 countries. J. Risk Financ. Manag., 2021, 14(2), 62.
- Lommers, K., Harzli, O.E. and Kim, J., Confronting machine learning with financial research. J. Financ. Data Sci., 2021, 3(3), 67–96.
- Lu, J. and Yi, S., Autoencoding conditional GAN for portfolio allocation diversification, 2022. arXiv preprint arXiv:2207.05701.
- Mariani, G., Zhu, Y., Li, J., Scheidegger, F., Istrate, R., Bekas, C. and Malossi, A.C.I., Pagan: Portfolio analysis with generative adversarial networks, 2019. arXiv. Retrieved from https://arxiv.org/abs/1909.10578.
- Markowitz, H., Portfolio selection. J. Finance, 1952, 7(1), 77–91. Retrieved 2022-10-20, from http://www.jstor.org/stable/2975974.
- Massey, F.J., The Kolmogorov–Smirnov test for goodness of fit. J. Am. Stat. Assoc., 1951, 46(253), 68–78. Retrieved 2022-11-25, from http://www.jstor.org/stable/2280095.
- Pagnoncelli, B.K., Ramírez, D., Rahimian, H. and Cifuentes, A., A synthetic data-plus-features driven approach for portfolio optimization. Comput. Econ., 2022. Retrieved from https://doi.org/10.1007/s10614-022-10274-2
- Pflug, G.C., Some remarks on the value-at-risk and the conditional value-at-risk. In Probabilistic Constrained Optimization, pp. 272–281, 2000 (Springer).
- Pun, C.S., Wang, L. and Wong, H.Y., Financial thought experiment: A GAN-based approach to vast robust portfolio selection. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), 2020.
- Rockafellar, R.T. and Uryasev, S., Optimization of conditional value-at-risk. J. Risk, 2000, 2(3), 21–41.
- Rockafellar, R.T. and Uryasev, S., Conditional value-at-risk for general loss distributions. J. Bank. Finance, 2002, 26(7), 1443–1471.
- Schaller, H. and Norden, S.V., Regime switching in stock market returns. Appl. Financ. Econ., 1997, 7(2), 177–191. https://doi.org/10.1080/096031097333745
- See, C.T.. and Sim, M., Robust approximation to multiperiod inventory management. Oper. Res., 2010, 58(3), 583–594.
- Sharpe, W.F., The arithmetic of active management. Financ. Anal. J., 1991, 47(1), 7–9.
- Takahashi, S., Chen, Y. and Tanaka-Ishii, K., Modeling financial time-series with generative adversarial networks. Phys. A, 2019, 527, 121261.
- Thune, K., How and why John Bogle started vanguard, 2022. Retrieved from www.thebalancemoney.com/how-and-why-john-bogle-started-vanguard-2466413.
- Tu, J. and Zhou, G., Data-generating process uncertainty: What difference does it make in portfolio decisions? J. Financ. Econ., 2004, 72(2), 385–421. Retrieved from https://www.sciencedirect.com/science/article/pii/S0304405X03002472.
- Walden, M.L., Active versus passive investment management of state pension plans: Implications for personal finance. J. Financ. Couns. Plan., 2015, 26(2), 160–171.
- Xu, L., Skoularidou, M., Cuesta-Infante, A. and Veeramachaneni, K., Modeling tabular data using Conditional GAN, 2019. CoRR abs/1907.00503. Retrieved from http://arxiv.org/abs/1907.00503.