References
- Alexander, G. J. and Baptista, A. M. (2008). Active portfolio management with benchmarking: Adding a value-at-risk constraint. Journal of Economic Dynamics and Control, 32(3):779–820.
- Anagnostopoulos, K. P. and Mamanis, G. (2011). The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms. Expert Systems with Applications, 38(11):14208–14217.
- Apt, K. (2003). Principles of constraint programming. Cambridge university press.
- Belotti, P. (2009). Couenne: a users manual.
- Ben-Tal, A. and Nemirovski, A. (2007). Selected topics in robust convex optimization. Mathematical Programming, 112(1):125–158.
- Bertsimas, D., King, A., Mazumder, R., et al. (2016). Best subset selection via a modern optimization lens. The annals of statistics, 44(2):813–852.
- Bertsimas, D. and Shioda, R. (2009). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications, 43(1):1–22.
- Bertsimas, D. and Sim, M. (2004). The price of robustness. Operations Research, 52(1):35–53.
- Bienstock, D. (1996). Computational study of a family of mixed-integer quadratic programming problems. Mathematical programming, 74(2):121–140.
- Bonami, P., Biegler, L. T., Conn, A. R., Cornujols, G., Grossmann, I. E., Laird, C. D., Lee, J., Lodi, A., Margot, F., Sawaya, N., and Wchter, A. (2008). An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optimization, 5(2):186–204.
- Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010, pages 177–186. Springer.
- Cesarone, F., Scozzari, A., and Tardella, F. (2009). Efficient algorithms for mean-variance portfolio optimization with hard real-world constraints. Giornale dellIstituto Italiano degli Attuari, 72:37–56.
- Cesarone, F., Scozzari, A., and Tardella, F. (2012). A new method for mean-variance portfolio optimization with cardinality constraints. Annals of Operations Research, 205(1):213–234.
- Chang, T.-J., Meade, N., Beasley, J. E., and Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, 27(13):1271–1302.
- Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., and Vlachogiannakis, N. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications, 112:353–371.
- Chiam, S., Tan, K., and Al Mamum, A. (2008). Evolutionary multi-objective portfolio optimization in practical context. International Journal of Automation and Computing, 5(1):67–80.
- Chong, E., Han, C., and Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83:187–205.
- Cremers, K. M. and Petajisto, A. (2009). How active is your fund manager? A new measure that predicts performance. The Review of Financial Studies, 22(9):3329–3365.
- Cremers, M., Ferreira, M. A., Matos, P., and Starks, L. (2016). Indexing and active fund management: International evidence. Journal of Financial Economics, 120(3):539–560.
- Cui, X., Zheng, X., Zhu, S., and Sun, X. (2013). Convex relaxations and miqcqp reformulations for a class of cardinality-constrained portfolio selection problems. Journal of Global Optimization, 56(4):1409–1423.
- Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear analysis: Real world applications, 10(4):2396–2406.
- Deng, G., Dulaney, T., McCann, C., and Wang, O. (2013). Robust portfolio optimization with value-at-risk-adjusted sharpe ratios. Journal of Asset Management, 14(5):293–305.
- Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A., and Focardi, S. M. (2007). Robust portfolio optimization and management. John Wiley & Sons.
- Fama, E. F. and French, K. R. (2010). Luck versus skill in the cross-section of mutual fund returns. The journal of finance, 65(5):1915–1947.
- Fischer, T. and Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2):654–669.
- Freitas, F. D., De Souza, A. F., and de Almeida, A. R. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10-12):2155–2170.
- Goetzmann, W., Ingersoll, J., Spiegel, M., and Welch, I. (2007). Portfolio performance manipulation and manipulation-proof performance measures. The Review of Financial Studies, 20(5):1503–1546.
- Goldfarb, D. and Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of operations research, 28(1):1–38.
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680.
- Gülpınar, N., Katata, K., and Pachamanova, D. A. (2011). Robust portfolio allocation under discrete asset choice constraints. Journal of Asset Management, 12(1):67–83.
- Heaton, J., Polson, N., and Witte, J. (2016). Deep learning in finance. arXiv preprint arXiv:1602.06561.
- Hopfield, J. J. and Tank, D. W. (1985). neural computation of decisions in optimization problems. Biological cybernetics, 52(3):141–152.
- Huang, J., Sialm, C., and Zhang, H. (2011). Risk shifting and mutual fund performance. The Review of Financial Studies, 24(8):2575–2616.
- Jiang, Z., Xu, D., and Liang, J. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. preprint arXiv:1706.10059., pages 1–31.
- Jobst, N. J., Horniman, M. D., Lucas, C. A., and Mitra, G. (2001). Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints. Quantitative Finance, 1(5):489–501.
- Jorion, P. (2003). Portfolio Optimization with Tracking-Error Constraints. Financial Analysts Journal, 59(5):70–82.
- Kim, J. H., Kim, W. C., and Fabozzi, F. J. (2014). Recent developments in robust portfolios with a worst-case approach. Journal of Optimization Theory and Applications, 161(1):103–121.
- Larrañaga, P. and Lozano, J. A. (2001). Estimation of distribution algorithms: A new tool for evolutionary computation, volume 2. Springer Science & Business Media.
- LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436.
- Maringer, D. and Kellerer, H. (2003). Optimization of cardinality constrained portfolios with a hybrid local search algorithm. OR Spectrum, 25(4):481–495.
- Niaki, S. T. A. and Hoseinzade, S. (2013). Forecasting s&p 500 index using artificial neural networks and design of experiments. Journal of Industrial Engineering International, 9(1):1.
- Oztekin, A., Kizilaslan, R., Freund, S., and Iseri, A. (2016). A data analytic approach to forecasting daily stock returns in an emerging market. European Journal of Operational Research, 253(3):697–710.
- Petajisto, A. (2013). Active share and mutual fund performance. Financial Analysts Journal, 69(4):73–93.
- Rossi, F., Van Beek, P., and Walsh, T. (2006). Handbook of constraint programming. Elsevier.
- Ruiz-Torrubiano, R. and Suárez, A. (2010). Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints. IEEE Computational Intelligence Magazine, 5(2):92–107.
- Sahinidis, N. V. (1996). Baron: A general purpose global optimization software package. Journal of global optimization, 8(2):201–205.
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61:85–117.
- Smith, K. A. (1999). Neural networks for combinatorial optimization: a review of more than a decade of research. INFORMS Journal on Computing, 11(1):15–34.
- Soleimani, H., Golmakani, H. R., and Salimi, M. H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, 36(3):5058–5063.
- Stucchi, P. (2015). A unified approach to portfolio selection in a tracking error framework with additional constraints on risk. The Quarterly Review of Economics and Finance, 56:165–174.
- Vielma, J. P., Ahmed, S., and Nemhauser, G. L. (2008). A lifted linear programming branch-and-bound algorithm for mixed-integer conic quadratic programs. INFORMS Journal on Computing, 20(3):438–450.
- Wilson, G. and Pawley, G. (1988). On the stability of the travelling salesman problem algorithm of hopfield and tank. Biological Cybernetics, 58(1):63–70.
- Woodside-Oriakhi, M., Lucas, C., and Beasley, J. E. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213(3):538–550.
- Zhu, H., Wang, Y., Wang, K., and Chen, Y. (2011). Particle swarm optimization (pso) for the constrained portfolio optimization problem. Expert Systems with Applications, 38(8):10161–10169.