In this research, we proposed a new metaheuristic technique for stock portfolio multi-objective optimization employing the combination of Strength Pareto Evolutionary Algorithm (SPEA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Arbitrage Pricing Theory (APT). To generate the more precise model, ANFIS has implemented to envisage long-term movement values of the Tehran Stock Exchange (TSE)...

This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through divers...

Using the wavelet analysis for low-frequency time series extraction, we conduct out-of-sample predictions of the S&P500 price index future trend (up and down). The support vector machines (SVMs) with different kernels and parameters are used as the baseline forecasting model. The simulation results reveal that the SVMs with wavelet analysis approach outperform the SVMs with macroeconomic variab...

non-linear time series models have become fashionable tools to describe and forecast stock market returns in recent years. a significant amount of evidence supports a negative relationship between volume and future returns. this suggests that volume could act as a suitable threshold variable in lstar and tar models. in this research, we compared the forecasting ability of lsatr and tar models w...

In this paper, we evaluate the performance of a number of forecasting models of U.S. business fixed investment spending growth over the recent 1995:1-2004:2 out-of-sample period at multiple forecast horizons. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash-Flow models of investment spending, as well as empirical models developed more recently ...

Predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature. Intrinsic volatility in stock market across the globe makes the task of prediction challenging. Forecasting and diffusion modeling, although effective can’t be the panacea to the diverse range of problems encountered in prediction, short-term or otherwise. ...

The prediction of outliers plays an important role in stock arbitrage and risk avoiding. While most of researches focused on detecting outliers and removing them to forecast time series data, few focused on forecasting the occurrence of outliers. The main goal of this work is to forecast outlier occurrence in Chinese stock market. Firstly, we detect abnormal points of two market indexes and six...

Forecasting levels of stocks held by manufacturing industry is problematic. Stocks are the most volatile component of GDP. The data itself is subject to chronic revision. Yet, forecasting inventory changes in the supply chain is crucial for firms trying to manage output. The paper reports a successful approach to forecasting UK manufacturing stock behaviour sponsored by a leading European metal...

In this paper we propose a novel model for forecasting innovation success based on online virtual stock markets. In recent years, online virtual stock markets have been increasingly used as an economic and efficient information gathering tool for the online community. It has been used to forecast events ranging from presidential elections to sporting events and applied by major corporations suc...