Effect of Distributional Assumption on GARCH Model into Shenzhen Stock Market： a Forecasting Evaluation

This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.

ST-Trader:A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Production Forecast of Citrus in China and Production and Marketing Situation of Citrus in Chongqing in 2016 Production Season

According to the statistics of the Ministry of Agriculture,the planting area of citrus would increase steadily,and the yield would decline slightly,2. 556 7 million ha and 36. 168 million t,respectively. Compared with 2015,the planting area would increase by 1. 97% and the yield would increase by 1. 17%. According to the production scheduling of Chongqing Agricultural Commission,the citrus production in Chongqing in 2016 would continue to maintain a steady and rapid growth,the estimated area and yield were 0. 206 7 million ha and 2. 8 million t,increasing by 4. 27% and 4. 48% compared with 2015 respectively. By the end of November 2016,most of mature citrus products in Chongqing would show different degree of rise in purchasing price,while the purchasing price of red orange and some processed raw material fruits would show different amplitude of decline. On the whole,the production and marketing situation of Chongqing citrus would become better.

Analysis of Chinese Power Market in 2007 and Its Forecast

Power supply and demand inJanuary-September, 2007Since 2007, the national economy developed continu-ously, showing a situation of rapid growth, more optimizedstructure, increased efficiency and improvement of people'slivelihood. In the first three quarters, GDP achieved 16.6043trillion Yuan, and its year-on-year growth rate was 11.5%;

2004 Neodymia Market and 2005 Forecast

In recent 10 years, global NdFeB magnetic materials industry develops at the increasing speed over 20% every year, which strongly stimulates the fast production improvement of neodymia and neodymium metal. Thereinto, production of Chinese NdFeB enhances the most rapidly. In 2004, output of Chinese sintered NdFeB reached 25,000 tons, up 82.5% over previous year. 1. 2004 Chinese Neodymia Production (1) Production of Southern Ore According to statistics, total 30,000 tons of

A MULTISCALE MODELING APPROACH INCORPORATING ARIMA AND ANNS FOR FINANCIAL MARKET VOLATILITY FORECASTING

The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.

Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information.However,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity.This paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price indices.We developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN architecture.The outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm.The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and TAIEX.In comparative testing,the proposed model proved its ability to provide closing price predictions with superior accuracy.Further,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.

MODELING AND FORECASTING OF STOCK MARKETS UNDER A SYSTEM ADAPTATION FRAMEWORK

This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal dynamic model and an adaptive filter. The output-error model is adopted as the internal model whereas the adaptive filter is a time-varying state space model with instrumental variables. Its input-output behavior, and internal as well as external forces are then identified. Special attention has also been paid to the recent financial crisis by examining the movement of Dow Jones Industrial Average （DJIA） as an example to illustrate the advantage of the proposed framework. Supported by time-varying causality tests, five influential factors from economic and sentiment aspects are introduced as the input of this framework. Testing results show that the proposed framework has a much better prediction performance than the existing methods, especially in complicated economic situations. An application of this framework is also presented with focuses on forecasting the turning periods of the market trend. Realizing that a market trend is about to change when the external force begins to exhibit clear patterns in its frequency responses, the authors develop a set of rules to recognize this kind of clear patterns. These rules work well for stock indexes from US, China and Singapore.

The Role of Japanese Candlestick in DVAR Model

The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and empirical studies. The main finding of this paper is that both lower and upper shadows in Japanese Candlestick Granger contribute to the DVAR model explanation power, and thus, providing useful information for improving the DVAR forecasts. This finding makes sense as it means that the infor- mation contained in the lower and upper shadows should be used when modeling the stock returns with DVAR. Empirical studies performed on China SSEC stock index demonstrate that DVAR model with upper and lower shadows as exogenous variables does have informative and valuable out-of-sample forecasts.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards optima.The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS.The antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.Tuning of the consequent part parameters are accomplished using extreme learning machine.The optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices.The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm.Analysis of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

Auto Market Forecast, 1996

Ⅰ. Estimated Macro-economic Environment for 1996 1996 is the first year of the 9th Five-Year Plan, the state will further deepen its system reforms and inject new vitality toward economic development. Economic growth will maintain balanced, inflation will be more

Forecast of China Auto Market '98

In 1997, China’s auto market enjoyed a rise again after a fall during 1994～1996. The State Information Center predicts, in 1998, Chi-na’s auto production and sales will increase steadily, the demand for automobiles will be be-tween 1.65～1.75 million units and the produc-tion will be probably about 1.70 million units, both will hit an all-time high.