In this paper we examine the ability of Artificial Neural Network methods (ANN) for predicting the stock market index. We first conduct an ANN analysis and then optimize the ANN model using Particle Swarm Optimization algorithm (PSO) to improve the prediction accuracy. In terms of data, we use NASDAQ index which is one of the most widely followed indices in the United States. Empirical results show that by determining the optimal set of biases and weights using PSO, we can augment the accuracy of the ANN model for this stock market data set.
A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.
Trade wars occur as a consequence of countries imposing tariffs and/or other trade barriers on one another. Such phenomenon is often a result of nationalism, unilateralism, and protectionism, and intensifies due to retaliation by different sides of the war. We use recent multinational trade data to measure the effects of single- and multi-sided tariffs on international trade. The methodology used in the paper is the structural Gravity model first introduced by Anderson and van Wincoop (2003). Theoretical implications and references of gravity equations are also presented throughout the article.
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