GMRVV m–SVR model for financial time series forecasting

This paper continues research within the framework of the scientific direction in econophysics at the Department of Information Systems and Mathematical Methods in Economics of the faculty of Economics of PSU. Modeling and prediction of financial time series is quite a perspective area of research, because it allows participants of financial processes to reduce risks and make effective decisions. For example, we could research financial processes with the help of fractal analysis. In the article there is studied and worked out in detail one of the methods of fractal analysis of financial time series – the box-counting method for assessment of the fractal dimension. This method is often used in studies conducted by domestic authors, but the authors do not delve into the characteristics and problems of using the box-counting method for analysis of time series, that means that the answers to the interested questions have not yet been given. The main problem is that, as a rule, the analyzed object in the tasks of applying the box-counting method to time series is a computer image of the plot of series. In the article there is proposed the procedure of adaptation of the box-counting method for assessment of the fractal dimension of time series, the procedure does not require the formation of a computer image of the plot. In the article there is considered following difficulties developed from this adaptation: 1) high sensitivity of the resulting estimation of the dimension to the input parameters of the method (the ratio of the sides of the covered by cells plane with the plot; the used range of lengths of the side of the cell; the number of partitions of the plane into cells); 2) the non-obviousness of choosing the optimal values ​​of these parameters. In the article there are analyzed approaches to the selection of these parameters that were proposed by other authors, and there are determined the most suitable approaches for the adapted box-counting method. Also there are developed unique methods for determining the ratio of the sides of the plane with the plot. In the paper there is written the computer program that implements the developed method, and this program is tested on the generated data. The study obtained the following results. The fact of sensitivity of the adapted box-counting method to input parameters is confirmed, that indicates the high importance of the correct choice of these parameters. According to the study, there is found out inability of the proposed methods of automatic determination the ratio of the sides of the plane in relation to artificial time series. There are obtained the most precise (in a statistical sense) estimates of fractal dimension, those found by means of the adapted box-counting method, with the fixed ratio of the sides 1:1. According to comparing the adapted box-counting method and R/S analysis, there are obtained the most precise estimates by the second method (R/S analysis). Finally in the paper there are formulated the possible directions for further research: 1) comparison of the accuracy of various methods for assessment of the fractal dimension on series of different lengths; 2) comparison of the methods of fractal analysis and p-adic analysis for modeling and prediction of financial time series; 3) determination of the conditions of applicability of various methods; 4) approbation of the developed methods for determining of the ratio of the sides of the plane with the plot on real economic data.

Always being aware of future economic and investment is important and significant for investors, companies, politicians and public people, in order to come to know the future economic perspective and accordingly how particular policies perform. There are different models in the field of prediction of financial time series that we can perform investment with lower risk by using them. Whatever prediction quantity is more accurate, the investment performs with lower risk. The proposed method of this paper is MA-SVR, which is a combination of memetic algorithm and support vector regression. We use memetic computation to estimate support vector regression method parameters and perform prediction with optimisation of SVR method parameters by using memetic algorithm. We evaluate the proposed MA-SVR method by use of prediction error with RMSE criterion, and compare it with other effective algorithms and we show that the proposed method causes improvement in the result from the view point of closeness of predicted quantities to reality.

Forecasting economic and financial time-series with non-linear models

Financial time series are chaotic that, in turn, leads their predictability to be complex and challenging. This paper presents a novel financial time series prediction hybrid that involves Chaos Theory, Convolutional neural network (CNN), and Polynomial Regression (PR). The financial time series is first checked in this hybrid for the presence of chaos. The chaos in the series of times is later modeled using Chaos Theory. The modeled time series is input to CNN to obtain initial predictions. The error series obtained from CNN predictions is fit by PR to get error predictions. The error predictions and initial predictions from CNN are added to obtain the final predictions of the hybrid model. The effectiveness of the proposed hybrid (Chaos+CNN+PR) is tested by using three types of Foreign exchange rates of financial time series (INR/USD, JPY/USD, SGD/USD), commodity prices (Gold, Crude Oil, Soya beans), and stock market indices (S&P 500, Nifty 50, Shanghai Composite). The proposed hybrid is superior to Auto-regressive integrated moving averages (ARIMA), Prophet, Classification and Regression Tree (CART), Random Forest (RF), CNN, Chaos+CART, Chaos+RF and Chaos+CNN in terms of MSE, MAPE, Dstat, and Theil’s U.

In the paper we show how L-FABS can be applied in a partial knowledge learning scenario or a full knowledge learning scenario to approximate financial time series. L-FABS combines agent-based simulation with machine learning to model the behavior of financial time series.We also discuss why Partial Knowledge and Full Knowledge learning scenario are relevant to the modeling of financial time series and how they can be used to assess the robustness of a modeling system for financial time series. In a Partial Knowledge learning setting usually only the initial conditions of the time series are provided, while in a Full Knowledge learning scenario any value of the financial time series is exploited as soon as it is available.An extensive experimental analysis of L-FABS is reported under a variety of financial time series and time frames.

Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step is the feature processing and time alignment. And then, the results of the model analysis of the maximum entropy and support vector regression are taken as the input of the artificial neural network. At last, obtaining the final predicted results by BP algorithm. The global optimality of SVR model and the flexible feature selection make the input model better, which is favorable to get better forecasting results in proposed hybrid financial time series model based on neural networks. Experimental results clearly show that the accuracy of prediction for China closed-end fund net value can achieve 98.25%.

Financial time series exhibit a mixture of linear and nonlinear components as indicated by the corresponding lagplots. As we will explain in this paper, certain financial time series can be approximated by increasing functions of a fixed number of time lags or antecedents. This work presents a suitable model for dealing with financial prediction problems, called increasing hybrid morphological-linear perceptron (IHMP). A pseudo-gradient steepest descent method is presented to design the IHMP (learning process), using the back-propagation algorithm and a systematic approach to overcome the problem of nondifferentiability of morphological operations. The learning process includes an automatic phase correction step that is geared at eliminating the time phase distortions that typically occur in financial time series prediction (“random walk dilemma”). Furthermore, we compare the proposed IHMP with other neural and statistical models using three complex nonlinear problems of financial time series prediction.

The log‐returns of most financial assets exhibit bursts of outlying observations that are characteristic of heavy tails. As such, models formulated for financial time series must capture heavy‐tailedness. Regular variation is a natural and often used concept to describe and model heavy‐tailed phenomena. This article first provides some evidence for heavy tails in log‐returns and then describes the basics of regular variation. It turns out that the two most commonly used models for financial time series, the GARCH and heavy‐tailed stochastic volatility (SV) processes, have finite‐dimensional distributions that are regularly varying. Regular variation is also a powerful tool for deriving large sample properties of important statistics of financial time series, from maxima to sample autocorrelations of functions of the process. These techniques show that although GARCH and SV processes share many common features, there can be a substantial difference in the large sample properties of the maxima and sample autocorrelations.

Apparent Multifractality in Financial Time Series

Financial time series forecasting using rough sets with time-weighted rule voting

The main aim of this paper is presentation and empirical analysis of the new approach which combines volatility models with Peaks over Threshold method. The standard volatility models better estimate medium size values of financial times series. The new approach is based on possibility, that extremes are estimated using POT method, and medium values are estimated using standard volatility models. The contribution of this paper is analysis of the value-atrisk and the expected shortfall for this new approach. Financial risk model evaluation of this risk measures is one of the key part of this paper. Conditional tail estimates are obtained by adjusting the unconditional extreme value theory procedure with the returns filtered by standard volatility models (McNeil, Frey, 2000). Additionally, risk measures estimates obtained from volatility models with application of EVT versus those computed, using standard volatility models for financial time series are compared.

In this paper, we present a parametric modelling of financial time series using long memory k-factor Gegenbauer processes suggested by Gray et al. (1989) and known in the statistical literature as generalized long memory processes. Using forecasting performances of this kind of processes and recent results on estimation theory we show how on an application to the monthly time series of exchange rates for the French franc against the US dollar, we can improve the efficiency of the long-term forecasting process.Key wordsFinancial seriesforecastinggeneralized long memory processes

The recent work in the author's research group on using independent component analysis (ICA) for the analysis and prediction of financial time series is reviewed. ICA belongs to the group of linear transform methods, with the goal to make a transform from the observed signals into a signal space in which the signals are statistically independent. Sometimes independence can be attained, especially in blind source separation in which the observed signals are assumed to be linear mixtures of independent source components. Then the goal of ICA is to invert the unknown mixing operation. Even when independence is not possible, as is often the case in financial time series, the ICA transformation produces useful component signals whose dependence is reduced, and that are nonGaussian with a density allowing sparse coding. The ICA transformation is also related to the temporal structure of the found signals as measured by Kolmogorov complexity or its approximations. The signals are structured and hence may be easier to interpret and predict. After discussing the ICA criterion within the context of linear signal transforms, the FastICA algorithm is reviewed. It is a computationally efficient method for finding a subset or all of the component signals. Then, two financial applications are covered: decomposition of parallel financial time series of weekly sales into basic factors, and prediction of mixture time series by linear combinations of predictions on the ICA components.

Associating stock prices with web financial information time series based on support vector regression

1: Architectures.- Scale Independence in the Visual System.- Dynamic Neuronal Information Processing of Vowel Sounds in Auditory Cortex.- Convolutional Spiking Neural Network for Robust Object Detection with Population Code using Structured Pulse Packets.- Networks Constructed of Neuroid Elements Capable of Temporal Summation of Signals.- Predictive Synchrony Organized by Spike-Based Hebbian Learning with Time-Representing Synfire Activities.- Improving Chow-Liu Tree Performance by Mining Association Rules.- A Reconstructed Missing Data-Finite Impulse Response Selective Ensemble (RMD-FSE) Network.- Higher Order Multidirectional Associative Memory with Decreasing Energy Function.- Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees with Fat Decision Hyperplanes.- 2: Learning Algorithms.- On Some External Characteristics of Brain-like Learning and Some Logical Flaws of Connectionism.- Superlinear Learning Algorithm Design.- Extension of Binary Neural Networks for Multi-class Output and Finite Automata.- A Memory-Based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks.- Structural Optimization of Neural Networks by Genetic Algorithm with Degeneration (GAd).- Adaptive Training for Combining Classifier Ensembles.- Combination Strategies for Finding Optimal Neural Network Architecture and Weights.- 3: Applications.- Biologically Inspired Recognition System for Car Detection from Real-Time Video Streams.- Financial Time Series Prediction Using Non-Fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression.- A Method for Applying Neural Networks to Control of Nonlinear Systesm.- Robot Manipulator Control via Recurrent Neural Networks.- Gesture Recognition Based on SOM Using Multiple Sensors.- Enhanced Phrase-Based Document Clustering Using Self-Organizing Map (SOM) Architectures.- Discovering Gene Regulatory Networks from Gene Expression Data with the Use of Evolving Connectionist Systems.- Experimental Analysis of Knowledge Based Multiagent Credit Assignment.- Implementation of Visual Tracking System Using Artificial Retina Chip and Shape Memory Alloy Actuator.