Investment decision model via an improved BP neural network

Abstract

In macro investment, an investment decision model is established by using an improved back propagation (BP) artificial neural network (ANN). In this paper, the relations between elements of investment and output of products are determined, and then the optimal distribution of investment is determined by adjusting the distributions rationally. This model can reflect the highly nonlinear mapping relations among each element of investment by using nonlinear utility functions to improve the architecture of artificial neural network, which can be widely applied in investment problems.

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

“…The well-known statistical models proposed include autoregressive, moving average, autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) models. The widely used machine-learning approaches include the neural network (NN) based models (Quah and Srinivasan 1999;Rabiner 1989;Roman and Jameel 1996), support vector machines (De Gooijer and Hyndman 2006;He et al 2008;Shen et al 2010;Tkacz 2001), fuzzy systems (Kandel 1991), linear regression, Kalman filtering (Ma and Teng 2004), and hidden Markov models (Rabiner 1989). All of these approaches were used for learning the forecasting models.…”

An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset

“…The widely used artificial intelligence approaches include neural network (NN) [2][3][4], support vector machines (SVM) [5][6][7][8], fuzzy systems [9], linear regression, Kalman filtering [10], and hidden Markov models (HMM) [3]. All of these approaches are used for updating the model parameters.…”