scholarly journals A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 126 ◽  
Author(s):  
Vlasenko ◽  
Vlasenko ◽  
Vynokurova ◽  
Bodyanskiy ◽  
Peleshko
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Financial Time Series ◽  
Selection Process ◽  
Real Life ◽  
Fuzzy Model ◽  
Financial Data ◽  
Financial Time ◽  
Fuzzy Models ◽  
Neuro Fuzzy

Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

scholarly journals A case study of the residual-based cointegration procedure

2003 ◽  
Vol 7 (1) ◽  
pp. 29-48
Author(s):  
Riccardo Biondini ◽  
Yan-Xia Lin ◽  
Michael Mccrae
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Real Life ◽  
Arima Models ◽  
Financial Time ◽  
Long Run ◽  
Data Generating Process ◽  
Finance Theory ◽  
The Relationship

The study of long-run equilibrium processes is a significant component of economic and finance theory. The Johansen technique for identifying the existence of such long-run stationary equilibrium conditions among financial time series allows the identification of all potential linearly independent cointegrating vectors within a given system of eligible financial time series. The practical application of the technique may be restricted, however, by the pre-condition that the underlying data generating process fits a finite-order vector autoregression (VAR) model with white noise. This paper studies an alternative method for determining cointegrating relationships without such a pre-condition. The method is simple to implement through commonly available statistical packages. This ‘residual-based cointegration’ (RBC) technique uses the relationship between cointegration and univariate Box-Jenkins ARIMA models to identify cointegrating vectors through the rank of the covariance matrix of the residual processes which result from the fitting of univariate ARIMA models. The RBC approach for identifying multivariate cointegrating vectors is explained and then demonstrated through simulated examples. The RBC and Johansen techniques are then both implemented using several real-life financial time series.

Fractal Properties of Financial Time Series

2010 ◽  
Vol 439-440 ◽  
pp. 683-687 ◽  
Author(s):  
Hong Zhang ◽  
Ke Qiang Dong
Keyword(s):  
Time Series ◽  
Power Law ◽  
Financial Time Series ◽  
Financial Data ◽  
Financial Time ◽  
Singular Spectrum ◽  
Box Counting ◽  
Fractal Properties ◽  
Box Counting Dimension

In this paper, we analyze the stock of Nanjing Panda Electronics Co Ltd for the 44-year period, from May 2, 1996, to October 9, 2009, a total of 3200 trading days. Using the Box-counting dimension method, we find that the financial data have different power law exponents in the plot for the number of box and diameter of box, which indicates the multifractality exist in the time series. In order to investigate the latent properties in the data, the width and maximum of the singular spectrum are calculated. The results show the strong degree of multifractality in the time series.

The structure and dynamics of granular complex networks deriving from financial time series

2020 ◽  
Vol 31 (06) ◽  
pp. 2050087
Author(s):  
Li Tingting ◽  
Luo Chao ◽  
Shao Rui
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Financial Time Series ◽  
Markov Chain Model ◽  
Real Life ◽  
Chain Model ◽  
Sequential Data ◽  
Information Granule ◽  
Financial Time ◽  
Structure And Dynamics

High noise and strong volatility are the typical characteristics of financial time series. Combined with pseudo-randomness, nonsteady and self-similarity exhibiting in different time scales, it is a challenging issue for the pattern analysis of financial time series. Different from the existing works, in this paper, financial time series are converted into granular complex networks, based on which the structure and dynamics of network models are revealed. By using variable-length division, an extended polar fuzzy information granule (FIGs) method is used to construct granular complex networks from financial time series. Considering the temporal characteristics of sequential data, static networks and temporal networks are studied, respectively. As to the static network model, some features of topological structures of granular complex networks, such as distribution, clustering and betweenness centrality are discussed. Besides, by using the Markov chain model, the transfer processes among different granules are investigated, where the fluctuation pattern of data in the coming step can be evaluated from the transfer probability of two consecutive granules. Shanghai composite index and foreign exchange data as two examples in real life are applied to carry out the related discussion.

scholarly journals Using a Financial Training Criterion Rather than a Prediction Criterion

1997 ◽  
Vol 08 (04) ◽  
pp. 433-443 ◽  
Author(s):  
Yoshua Bengio
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Portfolio Selection ◽  
Financial Training ◽  
Financial Time Series ◽  
Traditional Approach ◽  
Financial Time ◽  
Squared Error ◽  
Noisy Time Series ◽  
Gains And Losses

The application of this work is to decision making with financial time series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time series that better results can be obtained when the model is directly trained in order to maximize the financial criterion of interest, here gains and losses (including those due to transactions) incurred during trading. Experiments were performed on portfolio selection with 35 Canadian stocks.

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

2013 ◽  
pp. 189-205
Author(s):  
S. Chakravarty ◽  
P. K. Dash ◽  
V. Ravikumar Pandi ◽  
B. K. Panigrahi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Financial Time Series ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Series Data ◽  
Functional Link ◽  
Financial Time ◽  
Neural Fuzzy

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.

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