[2] Callegaro, G., Gagi, M., Scotti, S., & et al. (2017). Optimal investment in markets with over and under-reaction to information. Mathematical Finance Economy, 11, 299{322.
https://doi.org/10.1007/s11579-016-0182-8.
[3] Chiu, H., & Cont, R., (2023). A model-free approach to continuous-time nance. Mathematical Finance, 33(2), 257{273. https://doi.org/10.1111/ma .12370.
[8] He, H., & Pearson, N.D., (1991). Consumption and portfolio policies with incomplete markets and short-sale constraints: The in nite dimensional case. Journal of Economic Theory, 54, 259{304.
https://doi.org/10.1016/0022-0531(91)90123-L.
[11] Kang, M., Templeton, G., F., Kwak, D., & Um, S., (2024). Development of an AI framework using neural process continuous reinforcement learning to optimize highly volatile nancial portfolios. Knowledge-Based Systems, 300, 112017.
https://doi.org/10.1016/j.knosys.2024.112017.
[12] Korn, R., & MULLER, L., (2022). Optimal portfolio choice with crash risk and model ambiguity. International Journal of Theoretical and Applied Finance, 25(01), 22-50.
https://doi.org/10.1142/S0219024922500029.
[17] Uratani, T., (2014). A Portfolio Model for the Risk Management in Public Pension. Springer International Publishing, Cham, 183-186. http://dx.doi.org/10.1007/978-3-319-05014-0 41.
[18] Wu, B., & Li, L., (2023). Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market. Journal of Economic Dynamics and Control, 158, 104787.
https://dx.doi.org/10.2139/ssrn.4415531.
19] Xia, J., (2011). Risk aversion and portfolio selection in a continuoustime model. SIAM Journal on Control and Optimization, 49(5), 1916{1937.
https://doi.org/10.1137/10080871X.
[21] Zhang, C., Zhibin, L., & Kam, C.,Y., (2021). Optimal portfolio and consumption for a Markovian regime-switching jump-di usion process. The ANZIAM Journal, 63, 1{25.
https://doi.org/10.21914/anziamj.v63.14546.