Practice of a TwoStage Model Using Support Vector Regression and BlackLitterman for ETF Portfolio Selection Roboadvisor is a hot topic in the field of financial technology (FinTech) in recent years. This study proposes a programmatic ETF portfolio configuration that combines SVR and BlackLittermanu0027s twostage model. The results of the study showed that the MSE of the first stage of the model was 2.7970 with good performance. According to the prediction result of the first stage, the parameter setting of the BlackLitterman is performed, and then the model is constructed to further adjust the configuration. The final results show that under the same risk value, the SVMBL twostage model proposed by this study has a higher return rate than historical return and implied return. Therefore, it can be provided as a reference for the development of an ETF investment strategy.

A constrained multiperiod robust portfolio model with behavioral factors and an interval semiabsolute deviation Abstract   In this paper, a multiperiod portfolio selection model is presented considering the behavioral factors of investors. The behavioral factors are presented in the form of prospect theory with three main parameters as reference dependence, risk aversion, and diminishing sensitivity; these parameters define the behavioral features of investors that can influence their investing. The future return of assets is regarded according to the interval based on uncertainty. Besides, a criterion of interval semiabsolute deviation is presented to control the risk of investing. The influence of uncertainty exists in the optimal parameters; the final result is controlled by a robust optimization approach. In order to make this model applicable and adapting it to the conditions of the real world, we considered the cardinality constraints, entropy and the possibility of riskfree borrowing and lending with different rates. First, the model is solved by GAMS in small size to check the feasibility, then, due to the NPhard feature of the model, we used three metaheuristic algorithms, which are more appropriate for solving large problems, to solve the proposed model. To survey the modelxe2x80x99s performance, we used real data from a case study. The results show that the model reflects the behavioral factors of investors in their portfolio and it also maintains the robustness level in accordance with the degree of conservatism. Besides, the sensitivity analysis is performed to show the modelxe2x80x99s efficiency. Finally, we compared the results of different metaheuristic algorithms and benchmark modelxe2x80x99s performance.