Non-equidistant “Basic Form”-focused Grey Verhulst Models (NBFGVMs) for ill-structured socio-economic forecasting problems

Multiple uncertainties complicate socio-economic forecasting problems, especially when relying on ill-conditioned limited data. Such problems are best addressed by grey prediction models such as Grey Verhulst Model (GVM). This paper resolves the incompatibility between GVM’s estimation and prediction by taking its basic form equation as the basis of both. The resultant “Basic Form”-focused GVM (BFGVM) is also further developed to create Direct Non-equidistant BFGVM (DNBFGVM) and, in turn, DNBFGVM with Recursive simulation (DNBFGVMR). Experimental analyses comprise 19 socio-economic time series with an emphasis on Iranian population, a low-frequency non-equidistant time series with remarkable strategic importance. Promisingly, the proposed DNBFGVM and DNBFGVMR provide accurate in-sample and out-of-sample socio-economic forecasts, show highly significant improvements over the best traditional GVM, and offer cost-effective intelligent support of decision-making. Final results suggest future trends of studied socio-economic time series. Specifically, they reveal Iranian population to grow even slower than anticipated, demanding an urgent consideration of policy-makers

Dynamic portfolio insurance strategy: a robust machine learning approach

In this paper, we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy. For determining the amount for investing in the risky asset, a critical parameter is a constant risk multiplier that is calculated in our proposed model using RGP to reflect market dynamics. Our model includes four main steps: (1) Selecting the best stocks for constructing a portfolio using a density-based clustering strategy. (2) Enhancing the robustness of our proposed model with an application of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for forecasting the future prices of the selected stocks. The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and our model’s robustness. (3) Implementing the RGP model for calculating the risk multiplier. Risk variables are used to generate equation trees for calculating the risk multiplier. (4) Determining the optimal portfolio weights of the assets using the well-known Markowitz portfolio optimization model. Experimental results show that our proposed strategy outperforms our previous model

Portfolio selection with robust estimators considering behavioral biases in a causal network

In this study, we develop a behavioral portfolio selection model that incorporates robust estimators for model inputs in order to reduce the need to change the portfolio over consecutive periods. It also includes Conditional Value at Risk as a sub-additive risk measure, which is preferable in behavioral portfolio selection. Finally, we model a varying risk attitude in a causal network in which investor behavioral biases and latest realized return are related to using a causation algorithm. We also provide a case study in Tehran Stock Exchange, where the results disclose that albeit our model is not mean-variance efficient, it selects portfolios that are robust, well diversified, and have less utility loss compared to a well-known behavioral portfolio model

Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility

Abstract In recent years, empirical literature used widely GARCH models to characterize crude oil price volatility. Because this augmenting attention, six univariate GARCH models and two methods of estimation the parameters for forecasting oil price volatility are examined in this paper. Based on obtained results, the best method for forecasting crude oil price volatility of Brent market is determined. All the examined models in this paper belong to the univariate time series family. The four years out-of-sample volatility forecasts of the GARCH models are evaluated using the superior predictive ability test with more loss functions. The results show that GARCH (1,1) can outperform all of the other models for the crude oil price of Brent market across different loss functions. Four different measures are used to evaluate the forecasting accuracy of the models. Also two methods of estimation the parameters of GARCH models are compared for forecasting oil price volatility. The results suggest that in our study, maximum likelihood estimation (MLE) gives better results for estimation than generalized method of moments (GMM)