Financial Time Series Forecasting Research Articles

English

In current years, a variety of time series models have been planned for financial markets forecasting. In each case, the exactness of time series forecasting models are primary to make decision and hence the research for humanizing the efficiency of forecasting models have been curried on. Lots of researchers have compared diverse time series models mutually in order to establish more proficient once in financial markets. In this paper, the performance of four interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressive integrated moving average (FARIMA), hybrid ANNs and fuzzy (FANN) and Improved FARIMA models are compared together. Empirical results of exchange rate forecasting indicate that the FANN model is more acceptable than other those models. Consequently, it can be a suitable alternative model for interval forecasting of financial time series.

Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting

Financial time series forecasting has been a challenge for time series analysts and researchers because it is noisy, nonstationary and chaotic. To overcome this limitation, this study uses empirical mode decomposition (EMD) and phase space reconstruction (PSR) to assist in the task of financial time series forecasting. In addition, we propose an approach that combines these two data preprocessing methods with extreme learning machine (ELM). The approach contains four steps as follows. (1) EMD is used to decompose the dynamics of the exchange rate time series into several components of intrinsic mode function (IMF) and one residual component. (2) The IMF and residual time series phase space is reconstructed to reveal its unseen dynamics according to the optimum time delay $$\tau $$ź and embedding dimension m. (3) The reconstructed time series datasets are divided into two datasets: training and testing, in which the training datasets are used to build ELM models. (4) A regression forecast model is set up for each IMF as well as the residual component by using ELM. The final prediction results are obtained by compositing the prediction values. To verify the effectiveness of the proposed approach, four exchange rates are chosen as the forecasting targets. Compared with some existing state-of-the-art models, the proposed approach yields superior results. Academically, we demonstrated the validity and superiority of the proposed approach that integrates EMD, PSR, and ELM. Corporations or individuals can apply the results of this study to acquire accurate exchange rate information and reduce exchange rate expenses.

Semi-parametric estimation and forecasting for exogenous log-GARCH models

Advanced computing and processing techniques have yielded abundant information for financial time series forecasting. It is, therefore, natural to ask for possible extensions of time series models to accommodate the wealth of information. In this article, we develop a new model for financial volatility estimation and forecasting by incorporating exogenous covariates in a semi-parametric log-GARCH model. With additional information, we gain an increased prediction power. We propose a quasi-maximum likelihood procedure via spline smoothing technique. Consistent estimators and asymptotic normality are obtained under mild regularity conditions. Simulation experiments provide strong evidence that corroborates the asymptotic theories. Additionally, an application to SPY index data demonstrates strong competitive advantage of our model comparing with GARCH(1,1) and log-GARCH(1,1) models.

Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks

Financial market dynamics forecasting has long been a focus of economic research. A stochastic time effective function neural network (STNN) with principal component analysis (PCA) developed for financial time series prediction is presented in the present work. In the training modeling, we first use the approach of PCA to extract the principal components from the input data, then integrate the STNN model to perform the financial price series prediction. By taking the proposed model compared with the traditional backpropagation neural network (BPNN), PCA-BPNN and STNN, the empirical analysis shows that the forecasting results of the proposed neural network display a better performance in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, HS300, S&P500 and DJIA in the established model, and the corresponding statistical comparisons of the above market indices are also exhibited.

Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

Deterministic Echo State Networks Based Stock Price Forecasting

Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500) demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

Financial time series prediction by a random data-time effective RBF neural network

An improved neural network of time series predicting is presented in this paper. We introduce a random data-time effective radial basis function neural network in determination of the output weights, the center vectors and the widths in the hidden layer of the network. In the training modeling, we consider that the historical data on the financial market is key to the investors' decision-making for their investing positions, and the impact of historical data depends closely on the time. We develop a random data-time effective function to describe this impact strength, and a weight is given to each of the historical data, where a drift function and a random Brownian volatility function are applied to express the behavior of the time strength. Further, this neural network is applied to the prediction of financial price series of crude oil, SSE, N225 and DAX. The empirical experiments show that the proposed neural network results in better performance in financial time series forecasting and is advantageous in increasing the forecasting precision.

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the wellknown Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.

CARTMAP: a neural network method for automated feature selection in financial time series forecasting

In the past two decades, there has been much interest in applying neural networks to financial time series forecasting. Yet, there has been relatively little attention paid to selecting the input features for training these networks. This paper presents a novel CARTMAP neural network based on Adaptive Resonance Theory that incorporates automatic, intuitive, transparent, and parsimonious feature selection with fast learning. On average, over three separate 4-year simulations spanning 2004–2009 of Dow Jones Industrial Average stocks, CARTMAP outperformed related and classical alternatives. The alternatives were an industry standard random walk, a regression model, a general purpose ARTMAP, and ARTMAP with stepwise feature selection. This paper also discusses why the novel feature selection scheme outperforms the alternatives and how it can represent a step toward more transparency in financial modeling.

A Trend‐Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting

This paper presents a novel trend‐based segmentation method (TBSM) and the support vector regression (SVR) for financial time series forecasting. The model is named as TBSM‐SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation (PLR), has been applied to locate a set of trading points within a financial time series data. However, owing to the dynamics in stock trading, PLR cannot reflect the trend changes within a specific time period. Therefore, a trend based segmentation method is developed in this research to overcome this issue. The model is tested using various stocks from America stock market with different trend tendencies. The experimental results show that the proposed model can generate more profits than other models. The model is very practical for real‐world application, and it can be implemented in a real‐time environment.

A novel text mining approach to financial time series forecasting

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. Most of the existing forecasting models for this problem do not take market sentiment into consideration. To overcome this limitation, motivated by the fact that market sentiment contains some useful forecasting information, this paper uses textual information to aid the financial time series forecasting and presents a novel text mining approach via combining ARIMA and SVR (Support Vector Regression) to forecasting. The approach contains three steps: representing textual data as feature vectors, using ARIMA to analyze the linear part and developing a SVR model based only on textual feature vector to model the nonlinear part. To verify the effectiveness of the proposed approach, quarterly ROEs (Return of Equity) of six security companies are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model that uses additional market sentiment provides a promising alternative to financial time series prediction.

A new hybrid for improvement of auto-regressive integrated moving average models applying particle swarm optimization

A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract from their popularities for financial time series forecasting: One limitation of an ARIMA model is that it requires a large amount of historical data to generate an accurate result. Both theoretical and empirical findings suggest that combining different time series models may be an effective method of improving the predictive performances of data especially when the models in the ensemble are quite different. The main purpose of present paper is to combine the ARIMA model with the particle swarm optimization (PSO) model in order to improve and generate more accurate forecasting results. Under small data information, combining the PSO and ARIMA models performs better performance results compared to an ARIMA model itself. The proposed model is robust and it may be used as an alternative forecasting tool in economics areas.

A support vector machine based MSM model for financial short-term volatility forecasting

Financial time series forecasting has become a challenge because of its long-memory, thick tails and volatility persistence. Multifractal process has recently been proposed as a new formalism for this problem. An iterative Markov-Switching Multifractal (MSM) model was introduced to the literature. It is able to capture many of the important stylized features of the financial time series, including long-memory in volatility, volatility clustering, and return outliers. The model delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. To enhance MSM’s short-term prediction accuracy, this paper proposes a support vector machine (SVM) based MSM approach which exploits MSM model to forecast volatility and SVM to model the innovations. To verify the effectiveness of the proposed approach, two stock indexes in the Chinese A-share market are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model provides a promising alternative to financial short-term volatility prediction.

A robust automatic phase-adjustment method for financial forecasting

In this work we present the robust automatic phase-adjustment (RAA) method to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid model composed of a qubit multilayer perceptron (QuMLP) with a quantum-inspired evolutionary algorithm (QIEA), which is able to evolve the complete QuMLP architecture and parameters, as well as searches for the best time lags to optimally describe financial phenomena. In the attempt to improve the QuMLP parameters supplied by QIEA, each individual of the QIEA population is further trained by the complex back-propagation (CBP) algorithm. Also, for each forecasting model generated, we use a phase fix procedure to adjust time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed method through six real world financial time series, and the obtained results are discussed and compared to results found with the best methods recently presented in the literature.

Trend forecasting of financial time series using PIPs detection and continuous HMM

Many machine learning methods in Artificial intelligence literature, such as Neural Networks, Genetic Algorithms, SVM, and Case-Based Reasoning, have been applied to forecast the financial market with high irregularity and uncertatinty. Among them, SVM is the most representative method that models and forecasts financial time series. However, SVM cannot reflect on the dynamic characteristic of financial time series effectively due to its superior generalization performance. And we cannot guarantee that the parameters of SVM have the optimum values, since they are locally searched owing to the limited time bound. These vulnerabilities are the main factors that degenerates the forecasting performance of SVM. On the other hand, continuous HMM can effectively capture the irregular and dynamic movement of financial time series with a non-stationary property, since it models a financial time series stochastically, rather than deterministically. Therefore, this paper suggests a new method that constructs the trend forecasting model of financial time series. It firstly detects PIPs indicating the significant turnabout of trend in each financial time series. And then the detected PIPs are used to construtct its trend forecasting model based on continuous HMM. In the experiment with various financial time series datasets we demonstrate its superiority.

Grey relational grade in local support vector regression for financial time series prediction

Support vector regression (SVR) has often been applied in the prediction of financial time series with many characteristics. On account of much time consumption of global SVR, local machines are carried out to accelerate the computation. In this paper, we introduce local grey SVR (LG-SVR) integrated grey relational grade with local SVR for financial time series forecasting. Pattern search method and leave-one-out errors are adopted for model selection. Experimental results of three real financial time series prediction demonstrate that LG-SVR can speed up computing speed and improve prediction accuracy.

An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting

This paper proposes a hybrid model, evolutionary functional link neural fuzzy model (EFLNF), to forecast financial time series where the parameters are optimized by two most efficient evolutionary algorithms: (a) genetic algorithm (GA) and (b) particle swarm optimization (PSO). When the periodicity is just one day, PSO produces a better result than that of GA. But the gap in the performance between them increases as periodicity increases. The convergence speed is also better in case of PSO for one week and one month a head prediction. To testify the superiority of the EFLNF, a number of comparative studies have been made. First, functional link artificial neural network (FLANN) and functional link neural fuzzy (FLNF) were combined with back propagation (BP) learning algorithm. The result shows that FLNF performs better than FLANN. Again, FLNF is compared with EFLNF where the latter outperforms the former irrespective of the periodicity or the learning algorithms with which it has been combined. All models are used to predict the most chaotic financial time series data; BSE Sensex and S&P CNX Nifty stock indices one day, one week and one month in advance.

A Hybrid Particle Swarm Optimization and Support Vector Regression Model for Financial Time Series Forecasting

In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting.

Periodicity, Non-stationarity, and Forecasting of Economic and Financial Time Series: Editors' Introduction

Periodicity, Non-stationarity, and Forecasting of Economic and Financial Time Series: Editors' Introduction

Use of Artificial Neural Networks in Forecasting of Financial Time Series of High Frequencies with Stock Exchange Quotations as an Example

A field of science such as artificial neural networks (ANN) arouses more and more interest all over the world, not only among academics and researchers. Since its beginning in the 1940‟ties it has experienced its ups and downs. From the huge fascination in the early stage to nearly falling into oblivion after Minsky‟s book [10]. Then in the 80‟ties interest in ANN experienced a rapid growth which surprised everyone and resulted in a dramatic rise and demand for information about ANN. This interest is also a result of a very dynamic development of computer science in the recent years as well as increasing need for iterative computational models, to which most definitely we can include ANN.