Similar financial models： Are they free lunches？

In recent years, the academic and practical circles have paid so much attention to similar financial models because they generated a miracle in GOME （Gome Electrical Appliances Holding Limited） and SUNING （Suning Appliance Co., Ltd.） respectively. But what＇s the influence of such a model on corporate profitability and risk？ Are similar financial models free lunches？ To seek for the answers to the above questions, in this paper, we take Gree （GREE Electric Appliances, Inc. of Zhuhai）, Midea （Guangdong Midea Electric Appliances Co. Ltd. Stores） and GOME for examples to carry out a comprehensive and in depth financial analysis. The conclusions of this paper are： The company with similar financial characteristics has higher profitability and risk level; Only the companies which meet special requirements need or can implement such a model and the identification and control of hidden risk can guarantee its success.

Intelligent Networks for Chaotic Fractional-Order Nonlinear Financial Model

The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system.The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the ANNs-LMB.This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution.Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model.The selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for certification.The results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and precise.The obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure.The stochastic procedures mainly depend on the combination of the artificial neural network(ANNs)along with the Levenberg-Marquardt Backpropagation(LMB)i.e.,ANNs-LMB technique.The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional orderα.The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1.The data proportion is applied as 73%,15%,and 12%for training,testing,and certification to solve the chaotic fractional system.The acquired results are verified through the comparison of the reference solution,which indicates the proposed technique is efficient and robust.The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error(MSE).To authenticate the exactness,and consistency of the technique,the obtained performances are plotted in the figures of correlation measures,error histograms,and regressions.From these figures,it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

A Study on Similar Financial Models in Manufacturing Sector

In this paper, we subdivide the sample of manufacturing companies listed in two stock exchanges in China from the year 2000 to 2009 into two categories： the firms which adopted the similar financial models and those which did not. Then we use t-test and nonparametric test to explore the differences between the two sub-samples in cash management, assets and liabilities structures, assets turnover ratios, profitability, liquidity and growth rates. The results show that the former has shorter Cash Conversion Cycle （CCC）, quicker assets turnover, higher Debt Ratio （DR）, higher growth rate and thus has better profitability. In addition, the regression analysis results further demonstrate that Return on Invested Capital （ROIC） of similar-financial-model firms are positively related to Net Profit Margin （NPM） （p 〈 0.01）, and negatively to CCC （p 〈 0.01）, Interest Expense Ratio （IER） （p 〈 0.01）, the Ratio of Interest-Bearing Debt to Total Liabilities （IBDR） （p 〈 0.01） and the ratio of Net Receivables to Sales Revenue （NRSR） （p 〈 0.01）. Moreover, negative relationship between ROIC and dummy variable GROUP implies that the former has better profitability than the latter.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

A Multifractal Detrended Fluctuation Analysis of the Ising Financial Markets Model with Small World Topology

We present a multifractal detrended fluctuation analysis （MFDFA） of the time series of return generated by our recently-proposed Ising financial market model with underlying small world topology. The result of the MFDFA shows that there exists obvious multifractal scaling behavior in produced time series. We compare the MFDFA results for original time series with those for shuffled series, and find that its multifractal nature is due to two factors： broadness of probability density function of the series and different correlations in small- and large-scale fluctuations. This may provide new insight to the problem of the origin of multifractality in financial time series.

Forecasting Financial Distress of Chinese High-tech Manufacturing Companies Based on a Hybrid Model of GA-SVM

Owing to the radical changing of Chinese economy, it is essential to build an effective financial distress prediction model. In this paper, we present a genetic algorithm （GA） approach for optimizing parameters of support vector machine （SVM）. We validate the proposed model on datasets of Chinese high-tech manufacturing industry. Experimental results reveal that the proposed GAo SVM model can compare to and even outperform other exiting classifiers. Compared to grid-search algorithm, the proposed GA-based takes less time to optimize SVM parameter without degrading the prediction accuracy of SVM.

Modelling of Corporate Exposure at Default(EAD)in Emerging Financial Markets:The Case for Counters Listed on the Zimbabwe Stock Exchange(ZSE)in the Dollarization Era

This study explores the Exposure at Default(EAD)emanating from credit events undertaken by listed banking corporations trading on emerging markets,such as Zimbabwe’s Stock Exchange(ZSE)or market in the dollarization era,namely period 2010-2012.The dollarization of the Zimbabwean economy in 2009 coincided with the recovery of the global financial economy from the worst worldwide economic recession ever experienced in this world.The study used audited and published data drawn from financial statements of two banking corporations for the period 2010 to 2012 that were accessible on ZSE website.These data were presented and analyzed using Eviews7.The study revealed that there were a lot of non-performing loans drawn from EADs of banking corporations trading on the ZSE in the period under review.The study further noted that credit exposures issued by commercial banks in the period 2010-2012 were also exposed to risk from the nature of the borrowers,banks’internal and external market variables.The variables that impacted on banks’credit exposures include political,social,industrial,unemployment,technological challenges,state of financial markets,their capitalization and liquidity statuses.We therefore conclude that banks in emerging markets need to efficiently and effectively manage their credit portfolios in their desire to grow towards sustainable development.The study also concludes that banks in emerging markets that are into lending activities should adopt and implement financial econometric(EAD)models that are easy to apply,practical,pragmatic,and adjusted for market friction.The study recommends that listed banking corporations in emerging markets need to adhere to the requirements of the Basel Ⅱ and Ⅲ Capital Accords if they are to make meaningful business out of their credit exposure operations.It also recommends that banks should come up with capitalization and investment strategies that suit their economic conditions if they are to grow and develop sizeable market shares and wealth from their lending businesses.Finally the study recommends that banks in emerging economies should adapt to international business standards,strategies,ethics,and corporate governance parameters if they are to grow towards greater similarity with those in developed nations in their service delivery to the stakeholders and contribution to nation building and sustainable development.

Some Remarks About Financial Market Modelling Using a Minority Game Approach

The methods adopted by static physics corroborating the existence of electromagnetic forces are applicable to the theory of financial markets. Perceived from a classically physical angle, the financial market is defined as a system composed of several individual entries cooperating upon electromagnetic principles. The approach concerned gives rise to a certain model of financial market, otherwise known as a minority game. In the case of minority game, the allocation of securities and funds is conditioned exclusively upon the fluctuation of prices, where a higher tendency to purchase goods and stocks results in the scale being more profitable and vice versa. Thus players from a minority group gain a prevailing position.

Empirical Research on the City Financial Service Satisfaction in Emerging Economies Financial Centers

A secondary indexes system is constructed by using the SERVQUAL model of the service satisfaction measurement and combining the basic principles of financial service as well as its ＂tolerance zones＂ . All kinds of financial service satisfaction indexes are analyzed and tested empirically in Shanghai and other major emerging financial centers, and it is concluded that emerging financial centers are in a disadvantage position of the financial service level as a whole in comparison with the mature international financial centers, while Shanghai is demanded to make an improvement in the credibility of financial services and the professional knowledge reserve of service personnel in comparison with other emerging financial centers.

Structural Stability of the Financial Market Model: Continuity of Superhedging Price and Model Approximation

The present paper continues the topic of our recent paper in the same journal,aiming to show the role of structural stability in financial modeling.In the context of financial market modeling,structural stability means that a specific“no-arbitrage”property is unaffected by small(with respect to the Pompeiu–Hausdorff metric)perturbations of the model’s dynamics.We formulate,based on our economic interpretation,a new requirement concerning“no arbitrage”properties,which we call the“uncertainty principle”.This principle in the case of no-trading constraints is equivalent to structural stability.We demonstrate that structural stability is essential for a correct model approximation(which is used in our numerical method for superhedging price computation).We also show that structural stability is important for the continuity of superhedging prices and discuss the sufficient conditions for this continuity.

The Odd Log-Logistic Weibull-G Family of Distributions with Regression and Financial Risk Models

A new generalization of the Weibull-G family is proposed with two extra shape parameters.The mathematical properties are derived in great detail.Using the Weibull and normal distributions as baseline distributions,two models are introduced.The first model is a location-scale regression model based on a new extension of the Weibull distribution.The second model is a new two-step financial risk model to forecast the daily value at risk.The flexibility and applicability of the proposed models are investigated by means of five real data sets on the lifetime and financial returns.Empirical findings of the study show that proposed models work well and produce better results than other well-known models for financial risk modeling and censored lifetime data analysis.

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.

On interest-rate risk management of postal savings bureau

This article analyzes interest-rate risks faced by the postal savings bureau （PSB） based on the complete balance sheet. It presents the extended gap model and the extended duration gap model to measure the interest-rate risk, and discusses the inner balance-sheet strategies and the off-balance-sheet strategies to manage the interest-rate risks.