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This paper evaluates the relationships between weather variables like humidity, temperature, air pressure, wind speed and status (clear, cloud, rain, haze and snow) with stock returns and volatility in the Greater China region's stock market. The study focuses on four stock exchanges; Shanghai and Shenzhen stock exchanges from mainland China, Hong Kong stock exchange and Taiwan stock exchange from Hong Kong and Taiwan respectively. Using the robust approach the best peer econometric model for each market was chosen. The results of the study depict that weather variables have a significant effect on Greater China's capital markets. We are able to analyze that in some cases the variable affects returns as well as volatility • Prior studies have proved that weather has emotional effects. • Finance scholars have shown that emotions related to investor decisions. • I provide evidence that local weather affects stock returns. • I also explore which weather variable most liable for said effect. • I find that said effect is at least fall in every stock market of Greater China region. [ABSTRACT FROM AUTHOR]

Abstract: Conventional time series models have been applied to handle many forecasting problems, such as financial, economic and weather forecasting. In stock markets, correct stock predictions will bring a huge profit for stock investors. However, conventional time series models produce forecasts based on some strict statistical assumptions about data distributions, and, therefore, they are not very proper to forecast financial datasets. This paper proposes a new forecasting model using adaptive learning techniques to predict TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index) with multi-stock indexes (NASDAQ stock index and Dow Jones stock index). In verification, this paper employs seven year period of TAIEX stock index, from 1997 to 2003, as experimental datasets, and the root mean square error (RMSE) as evaluation criterion. The performance comparison results show that the proposed model outperforms the listing methods in forecasting Taiwan stock market. Besides, from statistical test results, it is showed that the volatility of Dow Jones and the NASDAQ affect TAIEX significantly. [Copyright &y& Elsevier]

Abstract: One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. This paper investigates stock market investment issues on Taiwan stock market using a two-stage data mining approach. The first stage Apriori algorithm is a methodology of association rules, which is implemented to mine knowledge and illustrate knowledge patterns and rules in order to propose stock category association and possible stock category investment collections. Then the K-means algorithm is a methodology of cluster analysis implemented to explore the stock cluster in order to mine stock category clusters for investment information. By doing so, this paper proposes several possible Taiwan stock market portfolio alternatives under different circumstances. [Copyright &y& Elsevier]

Abstract: Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index. To evaluate the forecasting performances, the proposed model is compared with four different models: Chen''s model, Yu''s model, Huarng''s model, and the ANFIS model. The results indicate that the proposed model is superior to the listing methods in terms of the root mean squared error (RMSE). [Copyright &y& Elsevier]

Abstract: In this paper, we employ the multivariate CUSUM (cumulative sum) test for covariance structure as well as the renormalized partial directed coherence (PDC) method to capture the structural causality change of real estate stock indices of five emerging Asian countries and regions (i.e., Thailand, Malaysia, South Korea, PR China, and Taiwan). Meanwhile, we develop a method to make the comparison of renormalized PDC more intuitive and a set of criteria to measure the result. One of our findings indicates that the regional influence of the Chinese real estate stock market on the causality structure of the five markets has arisen under the effect of the financial tsunami. [Copyright &y& Elsevier]

Abstract: Since 2000, the Industrial Development Bureau (IDB) of the Government in Taiwan has been implementing a policy of Electronic Manufacturing Promotion (e-MP) to assist firms to implement Business to Business Information System (B2BIS). In general, the actual performance of e-MP policy has just been based on the data given in a final report produced by the funded firm. Consequently there is no clear evidence as to the efficacy of the e-MP policy. Therefore, this paper mainly covers two parts (i) A Fuzzy analytic hierarchical process (Fuzzy AHP) was utilized to explore the weights of each policy item according to project leaders’ response of 17 funded cases to see whether the current e-MP policy really benefits firms and (ii) financial data of 30 cases was collected from the stock exchange in Taiwan to understand the actual performance and Fuzzy AHP was utilized to find the funded cases’ initial expectation to see whether the adoption of B2BIS by those cases funded by the Government actually met initial expectation. The results of this study indicate that the e-MP policy need to be adjust and costs decreased and work efficiencies rose are most important benefit in most of the funded cases after introduction of B2BIS and this meet their initial expectation. Finally, implications and suggestions are given for the e-MP policy to facilitate adjustments by the IDB. [Copyright &y& Elsevier]

Abstract: This paper has three main contributions to the literature on investor herds. First, it extends investor herding studies to an emerging yet relatively sophisticated Taiwanese stock market at the sector level by using firm level data. Second, it employs different methodologies designed to test the existence of investor herds to better understand the sources of herd behavior. Third, it discusses the implications of different herding measures for investors exposed to systematic and unsystematic risks. We find that the linear model based on the cross-sectional standard deviation (CSSD) testing methodology yields no significant evidence of herding. However, the non-linear model proposed by and the state space based models of lead to consistent results indicating strong evidence of herd formation in all sectors. We also find that the herding effect is more prominent during periods of market losses. Our results suggest limited diversification opportunities for investors in this market, especially during periods of market losses when diversification is most needed. Further research is necessary to see whether similar findings hold for other emerging markets. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose and implement a data modeling system to measure the similarity of stock market behavior in different time periods. To implement this system, we also propose a data distribution approximation model, Polygon descriptor, and a shape difference measurement, called Deforming distance method. The Polygon descriptor can model a data distribution by characterizing the data dependency relationship among the data variables, and the Deforming distance is designed to be translation, scale, and rotation invariant. The Polygon descriptor and Deforming distance method are combined to measure the similarity between data sets based on the shape of data distribution. Stock price and EPS (earn per share) data during different time periods were selected to verify the modeling and measuring power of the proposed model and method. The Polygon descriptor is used to model the collected Stock price and EPS data for their dependency relationship. Then, difference between Polygon descriptors can be measured by using the Deforming distance measurement method for their Deforming distance values. Based on the Deforming difference values, the similarity of stock market behaviors in two time periods can be assessed, and the similar stock market behavior of different time periods can be identified. To demonstrate the capabilities of the proposed method and system, real-world data of Taiwan stock market during the period from 1986 to 2006 were collected and used. Experimental results show that the Polygon descriptor successfully captures the data dependencies between stock price and EPS, and the Deforming difference based similarity measurement estimates the changes of market behavior better than some commonly used methods. A web-based prototype system is available at http://www.csie.cntu.edu.tw/~pslai/TWStockSIdx/ for public trial, evaluation, comments and suggestions. [Copyright &y& Elsevier]

Abstract: This paper presents an integration prediction method including grey model (GM), Fourier series, and Markov state transition, known as Markov–Fourier grey model (MFGM), to predict the turning time of Taiwan weighted stock index (TAIEX) for increasing the forecasting accuracy. There are two parts of forecast. The first one is to build an optimal grey model from a series of data, the other uses the Fourier series to refine the residuals produced by the mentioned model. Finally, the Markov state scheme is used for predicting the possibility of location results to promote the intermediate results generated by the Fourier grey model (FGM). It is evident that the proposed approach gets the better result performance than that of the other methods. [Copyright &y& Elsevier]

Abstract: This paper examines lunisolar calendar anomalies in the Taiwan stock market, particularly the solar term effect. Using the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the significance of the solar term effect was tested. Statistical results showed that the lowest and the highest average stock returns are observed on Cold Dew and White Dew, respectively. Additionally, we found that all of the solar terms with negative average stock returns occurred during the period of Grain Fills to Winter Solstice. Although many investors believe that the solar term effect exists in the Taiwan stock market, the results of this study appear to show that the solar term effect is a mere superstition. The analytical results may prove useful for future theoretical and empirical work on the stock market in Taiwan and elsewhere. [Copyright &y& Elsevier]

Abstract: In our daily life, we often use some forecasting techniques to predict weather, temperature, stock, earthquake, economy, etc. Based on these forecasting results, we can prevent damages to occur or get benefits from the forecasting activities. In fact, an event in the real-world can be affected by many factors. The more the facts we consider, the higher the forecasting accuracy rate. Moreover the length of each interval in the universe of discourse also affects the forecasting results. In this paper, we present a new method to predict the temperature and the Taiwan Futures Exchange (TAIFEX), based on automatic clustering techniques and two-factors high-order fuzzy time series. The proposed method gets higher average forecasting accuracy rates than the existing methods. [Copyright &y& Elsevier]

Abstract: Stock market price index prediction is regarded as a challenging task of the financial time series prediction process. Support vector regression (SVR) has successfully solved prediction problems in many domains, including the stock market. This paper hybridizes SVR with the self-organizing feature map (SOFM) technique and a filter-based feature selection to reduce the cost of training time and to improve prediction accuracies. The hybrid system conducts the following processes: filter-based feature selection to choose important input attributes; SOFM algorithm to cluster the training samples; and SVR to predict the stock market price index. The proposed model was demonstrated using a real future dataset – Taiwan index futures (FITX) to predict the next day’s price index. The experiment results show that the proposed SOFM-SVR is an improvement over the traditional single SVR in average prediction accuracy and training time. [Copyright &y& Elsevier]