Table of Content

25 February 2014, Volume 27 Issue 1

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A SELF-SIMILAR LOCAL NEURO-FUZZY MODEL FOR SHORT-TERM DEMAND FORECASTING

HASSANI Hossein , ABDOLLAHZADEH Majid , IRANMANESH Hossein ,MIRANIAN Arash

2014, 27(1):  3-20.  DOI: 10.1007/s11424-014-3299-y

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This paper proposes a self-similar local neuro-fuzzy (SSLNF) model with mutual information-based input selection algorithm for the short-term electricity demand forecasting. The proposed selfsimilar model is composed of a number of local models, each being a local linear neuro-fuzzy (LLNF) model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree  NLOLIMOT) learning algorithm which partitions the input space into axis-orthogonal sub-domains and then fits an LLNF model and its associated validity function on each sub-domain. Furthermore, the proposed approach allows different input spaces for rule premises (validity functions) and consequents (local models). This appealing property is employed to assign the candidate input variables (i.e., previous load and temperature) which influence short-term electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from short-term load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.

FORECASTING EXCHANGE RATES: AN OPTIMAL APPROACH

BENEKI Christina , YARMOHAMMADI Masoud

2014, 27(1):  21-28.  DOI: 10.1007/s11424-014-3304-5

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This paper looks at forecasting daily exchange rates for the United Kingdom, European Union, and China. Here, the authors evaluate the forecasting  erformance of neural networks (NN), vector singular spectrum analysis (VSSA), and recurrent singular spectrum analysis (RSSA) for forecasting exchange rates in these countries. The authors find statistically significant evidence based on the RMSE, that both VSSA and RSSA models outperform NN at  orecasting the highly unpredictable exchange rates for China. However, the authors find no evidence to suggest any difference between the forecasting accuracy of the three models for UK and EU exchange rates.

COMBINING SINGULAR SPECTRUM ANALYSIS AND PAR(p) STRUCTURES TO MODEL WIND SPEED TIME SERIES

ENEZES Mois′es Lima de , SOUZA Reinaldo Castro , PESSANHA Jos′e , Francisco Moreira

2014, 27(1):  29-46.  DOI: 10.1007/s11424-014-3301-8

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Singular spectrum analysis (SSA) is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals.  Leaving out the residual components and adding up the others, the time series can be smoothed. This procedure has been used to model Brazilian electricity consumption and flow series. The PAR(p), periodic autoregressive models, has been broadly used in modelling energy series in Brazil. This paper presents an approach of this decomposition method, by fitting the PAR(p), considering its multivariate version known as multivariate SSA (MSSA). The method was applied to a vector of two wind speed series recorded at two locations in the Brazilian Northeast region. The obtained results, when compared to the univariate decomposition of each series, were far superior, showing that the spatial correlation between the two series were considered by MSSA
decomposition stage.

EXCHANGE RATE FORECASTING WITH OPTIMUM SINGULAR SPECTRUM ANALYSIS

GHODSI Mansi , YARMOHAMMADI Masoud

2014, 27(1):  47-55.  DOI: 10.1007/s11424-014-3303-6

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Forecasting exchange rate is undoubtedly an attractive and challenging issue that has been of interest in different domains for many years. The singular spectrum analysis (SSA) technique has been used as a promising technique for time series forecasting including exchange rate series. The SSA technique is based upon two main choices: Window length, L, and the number of singular values, r. These values are very important for the reconstruction stage and forecasting purposes. Here the authors consider an optimum version of the SSA technique for forecasting exchange rates. The forecasting performances of the SSA technique for one-step-ahead forecast of six exchange rate series are used to find the best L and r.

ESTIMATING MULTI-COUNTRY PROSPERTY INDEX: A TWO-DIMENSIONAL SINGULAR SPECTRUM ANALYSIS APPROACH

ZHANG Jiawei , HASSANI Hossein, XIE Haibin, ZHANG Xun

2014, 27(1):  56-74.  DOI: 10.1007/s11424-014-3314-3

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With the development of the global economy, interaction among different economic entities from one region is intensifying, which makes it significant to consider such interaction when constructing composite index for each country from one region. Recent advances in signal extraction and time series analysis have made such consideration feasible and practical. Singular spectrum analysis (SSA) is a well-developed technique for time series analysis and proven to be a powerful tool for signal extraction. The present study aims to introduce the usage of the SSA technique for multi-country business cycle analysis. The multivariate SSA (MSSA) is employed to construct a model-based composite index and the two dimensional SSA (2D-SSA) is employed to establish the multi-country composite index. Empirical results performed on Chinese economy demonstrate the accuracy and stability of MSSA-based
composite index, and the 2D-SSA based composite indices for Asian countries confirm the efficiency of the technique in capturing the interaction among different countries.

CLUSTER-BASED REGULARIZED SLICED INVERSE REGRESSION FOR FORECASTING MACROECONOMIC VARIABLES

YU Yue , CHEN Zhihong , YANG Jie

2014, 27(1):  75-91. 

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This paper concerns the dimension reduction in regression for large data set. The authors introduce a new method based on the sliced inverse regression approach, called cluster-based regularized sliced inverse regression. The proposed method not only keeps the merit of considering both response and predictors’ information, but also enhances the capability of handling highly correlated variables. It is justified under certain linearity conditions. An empirical application on a macroeconomic data set shows that the proposed method has outperformed the dynamic factor model and other shrinkage methods.

INFORMATION IDENTIFICATION IN DIFFERENT NETWORKS WITH HETEROGENEOUS INFORMATION SOURCES

FENG Xu , ZHANG Wei , ZHANG Yongjie , XIONG Xiong

2014, 27(1):  92-116.  DOI: 10.1007/s11424-014-3297-0

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Traditional cheap-talk game model with homogeneous information sources provided a conclusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors incorporate different information diffusion networks and heterogeneous information sources into an agent-based artificial stock market. The obtained results are different with traditional results that identification ability of uninformed agents has been highly improved with diffusion networks and heterogeneous information sources. Additionally, the authors find uninformed agents can improve identification ability only if there exists a sufficient number of heterogeneous information sources in stock market.

FORECASTING TIME SERIES WITH GENETIC PROGRAMMING BASED ON LEAST SQUAREv METHOD

YANG Fengmei , LI Meng , HUANG Anqiang , LI Jian

2014, 27(1):  117-129.  DOI: 10.1007/s11424-014-3295-2

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Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptions unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method: Genetic programming based on least square method (GP-LSM). Inheriting the advantages of genetic algorithm (GA), without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method (LSM). In order to verify the validity of this method, the authors compare this method with seasonal auto regression integrated moving average (SARIMA and back propagation artificial neural networks (BP-ANN). The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.

DOES INVESTOR SENTIMENT PREDICT STOCK RETURNS? THE EVIDENCE FROM CHINESE STOCK MARKET

BU Hui , PI Li

2014, 27(1):  130-143.  DOI: 10.1007/s11424-013-3291-y

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This paper examines the proxy variables of investor sentiment in Chinese stock market carefully, and tries to construct an investor sentiment index indirectly. We use cross correlation analysis to examine lead-lag relationship between the proxy variables and HS300 index. The results show that net added accounts (NAA), SSE share turnover (TURN), and closed-end fund discount (CEFD) are leading variables to stock market. The average first day return of IPOs (RIPO) and relative degree of active trading in equity market (RDAT) are contemporary variables, while number of IPOs (NIPO) is a lagging variable of stock market. Using the sentiment proxy variables with most possible leading order, and forward selection stepwise regression method, the empirical results on monthly stock returns reveal that three leading proxy variables can be used to form a sentiment index. And the out of sample tests prove that this sentiment index has good predictive power of Chinese stock market, and it is robust.

IS TECHNICAL ANALYSIS INFORMATIVE IN UK STOCK MARKET? EVIDENCE FROM DECOMPOSITION-BASED VECTOR AUTOREGRESSIVE (DVAR) MODEL

XIE Haibin , BIAN Jiangze , WANG Mingxi , QIAO Han

2014, 27(1):  144-156.  DOI: 10.1007/s11424-014-3280-9

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The paper proposes a new approach — The decomposition-based vector autoregressive (DVAR) model to scrutinize the predictability of the UK stock market. Empirical studies performed on the monthly British FTSE100 index over 1984–2012 confirm that the DVAR model does provide informative forecasts for both in-sample and out-of-sample forecasts. Trading strategies based on the DVAR forecasts can significantly beat the simple buy-and-hold, which demonstrates the valuable information provided by technical analysis in the UK stock market.

A ROUGH SET APPROACH TO FEATURE SELECTION BASED ON SCATTER SEARCH METAHEURISTIC

WANG Jue , ZHANG Qi , ABDEL-RAHMAN Hedar , ABDEL-MONEM MIbrahim

2014, 27(1):  157-168.  DOI: 10.1007/s11424-014-3298-z

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Rough set theory is an effective method to feature selection, which has recently fascinated many researchers. The essence of rough set approach to feature selection is to find a subset of the original features. It is, however, an NP-hard problem finding a minimal subset of the features, and it is necessary to investigate effective and efficient heuristic algorithms. This paper presents a novel rough set approach to feature selection based on scatter search metaheuristic. The proposed method, called scatter search rough set attribute reduction (SSAR), is illustrated by 13 well known datasets from UCI machine learning repository. The proposed heuristic strategy is compared with typical attribute reduction methods including genetic algorithm, ant colony, simulated annealing, and Tabu search. Computational results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features and show promising and competitive performance on the considered datasets.

ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY

SCHMIDBAUER Harald, R¨OSCH Angi , SEZER Tolga,TUNALIO ?GLU Vehbi Sinan

2014, 27(1):  169-180.  DOI: 10.1007/s11424-014-3302-7

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Trading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predicament either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of timeseries bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.

A TRANSFER FORECASTING MODEL FOR CONTAINER THROUGHPUT GUIDED BY DISCRETE PSO

XIAO Jin , XIAO Yi , FU Julei , LAI Kin Keung

2014, 27(1):  181-192.  DOI: 10.1007/s11424-014-3296-1

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Accurate forecast of future container throughput of a port is very important for its construction,upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm (TF-DPSO). It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two important ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.

AMERICAN OPTION PRICING UNDER GARCH DIFFUSION MODEL: AN EMPIRICAL STUDY

WU Xinyu , YANG Wenyu , MA Chaoqun , ZHAO Xiujuan

2014, 27(1):  193-207.  DOI: 10.1007/s11424-014-3279-2

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The GARCH diffusion model has received much attention in recent years, as it describes financial time series better when compared to many other models. In this paper, the authors study the empirical performance of American option pricing model when the underlying asset follows the GARCH diffusion. The parameters of the GARCH diffusion model are estimated by the efficient importance sampling-based maximum likelihood (EIS-ML) method. Then the least-squares Monte Carlo (LSMC) method is introduced to price American options. Empirical pricing results on American put options in Hong Kong stock market shows that the GARCH diffusion model outperforms the classical constant volatility (CV) model significantly.

A QUANTITATIVE MODEL FOR INTRADAY STOCK PRICE CHANGES BASED ON ORDER FLOWS

LI Meng ,HUI Xiaofeng , ENDO Misao , KISHIMOTO Kazuo

2014, 27(1):  208-224.  DOI: 10.1007/s11424-014-3300-9

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This paper proposes a double Markov model of the double continuous auction for describing intra-day price changes. The model splits intra-day price changes as the repetition of one tick price moves and assumes order arrivals are independent Poisson random processes. The dynamic process of price formation is described by a birth-death process of the double M/M/1 server queue corresponding to the best bid/ask. The initial depths of the best bid and ask are defined as different constants depending on the last price change. Thus, the price changes in the model follow a first-order Markov process. As the initial depth of the best bid/ask is originally larger than that of the opposite side when the last price is down/up, the model may explain the negative  autocorrelations of the price of the best bid/ask. The estimated parameters are based on the real tick-by-tick data of the Nikkei 225 futures listed in Osaka Stock Exchanges. The authors find the model accurately predicts the returns of Osaka Stock Exchange average.

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

XIAO Yi , XIAO Jin , LIU John , WANG Shouyang

2014, 27(1):  225-236.  DOI: 10.1007/s11424-014-3305-4

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The financial market volatility forecasting is regarded as a challenging task because of irregularity, 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 predicted 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.