Taiwan Stock Exchange Capitalization Weighted Stock Index Research Articles

Multi-variable time series forecasting model based on high-order hesitant probabilistic linguistic fuzzy logical relationship

The stock market is uncertain, but its fluctuations have inherent laws. A suitable method to extract these rules from historical data is crucial for predicting future trends. However, since these rules are often disturbed by external noise, noise reduction while preserving critical internal information is necessary to improve the accuracy of fuzzy time series forecasting. In this paper, we propose a novel two-factor high-order fuzzy time series (FTS) forecasting model based on hesitant probabilistic fuzzy logical relationship (HPLR). To evaluate the performance of the model, we conduct empirical analysis using the closing price of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the main factor and the opening price as the secondary factor. The proposed model shows improved prediction performance and is intelligent and interpretable in model design. In addition, we forecasted the Hang Seng Index (HSI) to further illustrate the generalizability of the model.

Forecasting TAIEX and FITX with Affirmative and Doubtful Investor Sentiments

Abstract The existing literature have used media messages as a proxy variable for investor sentiment, but they mainly classify sentiment into positive or negative categories based on the words used in news articles, without much attention to the degree of affirmative or doubtful conveyed by the words used. Thus, in addition to classifying news content into positive or negative sentiment, this study also measures the degree of affirmative or doubtful expressed in the news articles in order to achieve more accurate predictive results. The study converts qualitative text to two quantitative scores (sentiment ratio and affirmative ratio) and investigates the predictive ability of these two variables on stock index returns and volatility in Taiwan’s case. The empirical findings indicate that only affirmative ratio exhibits a significant and negative impact on the one-day ahead returns of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Taiwan Stock Exchange Index Futures (FITX). Additionally, the volatility of returns in both future and spot markets is significantly influenced by both sentiment ratio and affirmative ratio. JEL Classifications: C22; G10; G40. Keywords: Investor sentiment; Text-mining; TAIEX; FITX; Affirmative ratio.

Forecasting of Taiwan's weighted stock Price index based on machine learning

AbstractThis study proposes a stack framework of light gradient boosting machine (LGBM) for Taiwan stock market index prediction. Stock market predictions have been regarded as a challenging task, as the market is affected by several factors such as political events, general economic conditions, institutional investors' choices, movement of the global market, psychology of investors. We construct a rich feature set to capture the impacts of global markets, institutional investors' choices, and the psychology of investors. A feature selection algorithm is proposed to choose important feature subset and enhance the training performance. To further improve the prediction accuracy, we employ stacking strategy to combine multiple classifiers together. A 10‐year period of the Taiwan stock exchange capitalization weighted stock index (TAIEX) is used to verify the performance of the proposed model. The experimental results suggest that our prediction model as well as the feature selection method can achieve good prediction performance.

Kapılı Tekrarlayan Hücreler Tabanlı Bulanık Zaman Serileri Tahminleme Modeli

Time series forecasting has lots of applications in various industries such as weather, business, retail and energy consumption forecasting. Accurate prediction in these applications is very important and also difficult task because of complexity and uncertainty of time series. Nowadays, using deep learning methods is a popular approach in time series forecasting and shows better performance than classical methods. However, in the literature, there are few studies which use deep learning methods in fuzzy time series (FTS) forecasting. In this study, we propose a novel FTS forecasting model which is based upon hybridization of Recurrent Neural Networks with FTS to deal with complexity and also uncertanity of these series. The proposed model utilizes Gated Recurrent Unit (GRU) to make prediction by using combination of membership values and also past value from original time series data as model input and produce real forecast value. Moreover, the proposed model can handle first order fuzzy relations as well as high order ones. In experiments, we have compared our model results with those of state-of-art methods by using two real world datasets; The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Nikkei Stock Average. The results indicate that our model outperforms or performs similar to other methods. The proposed model is also validated by using Covid-19 active case dataset and shows better performance than Long Short-term Memory (LSTM) networks.

The Influence of Hedge, Arbitrage, and After-Hours Trading on the Holding Returns of TAIEX Futures

This study points out a new explanation of the non-trading effect of financial derivatives from the perspective of hedging demand. We examine the influence of hedging demand on the non-trading effect of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) Futures. By dividing the sample period into trading period and non-trading period and testing the difference between the risk premiums in these two intervals, we find that there is a non-trading effect in TAIEX Futures, which means that the holding returns of TAIEX Futures in the non-trading period are higher than those in the trading period. By estimating a dummy-regression model, the evidence shows that when the VIX (Taiwan Index Option Volatility Index) indicator is relatively high, the non-trading effect will be more significant, indicating that the non-trading effect may come from investors’ hedging needs. In addition, it is found that when the futures index is higher than the spot index, the non-trading effect becomes less obvious. The possible reason is that when there is a positive spread in index futures, investors will expect a bull market, thus reducing the hedging demand of index futures. In the end, we find that the liquidity in the after-hours trading session is poor, resulting in high hedging costs, and forcing investors to hedge during the regular trading period. Therefore, the after-hours trading of TAIEX Futures fails to reduce the non-trading effect.

Impacts of relatively rational and irrational investor sentiment on realized volatility

AbstractWe adopt intraday data in this study to facilitate an exploration of the influences of relatively rational and irrational investor sentiment on volatility within the Taiwan stock markets. Following the decomposition of daily trading volume within the Taiwan Stock Exchange Capitalization Weighted Stock Index (TWSE) into two subsets, comprising the trading volume of institutional investors and individual investors, we go on to investigate the influence of each subset on realized volatility. We reveal that the relatively rational sentiment of institutional investors plays a stabilizing role in future volatility, whereas the relatively irrational sentiment of individual investors tends to exacerbate such volatility. Therefore, we suggest that our modified model, which takes into account the relatively rational and irrational sentiment of investors, is capable of more accurately predicting volatility than the benchmark model.

Optimal determination of hidden Markov model parameters for fuzzy time series forecasting

This paper presents Fuzzy Time Series (FTS) forecasting technique using Hidden Markov Model (HMM) optimized by Particle Swarm Optimization (PSO) and Genetic algorithm (GA). One of the major limitations of HMM is the lack of efficient method for HMM parameter estimation. Traditional methods like Baum Welch Algorithm (BWA) have been used to address the associated problem with HMM model. The BWA in itself does not truly capture the fuzziness in natural data resulting in the HMM algorithm into local minima. To address this challenge, this paper formulates the HMM parameter estimation problem into an optimization problem optimized by GA and PSO algorithms. To solve the problem of insufficiency in data allied with the HMM model, we adopted a method called smoothing to reduce the occurrence of zero in the observation events. Monte Carlo simulation is used at the end of the forecast to maintain stability and efficiency of the developed approach. The performance of developed model is evaluated using data of daily average temperature and cloud density of Taipei Taiwan, the Internet traffic data of Ahmadu Bello University (ABU) and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). Experimental results showed that the proposed forecasting models has a good forecasting accuracy when compared with existing models.

Multivariate intuitionistic fuzzy inference system for stock market prediction: The cases of Istanbul and Taiwan

Many of decision-making and policy planning processes involve a time-series prediction problem and so this area has extensive literature including a great variety of time-series prediction tools and inferences systems. An important part of these is based on fuzzy sets. However, it is known that fuzzy sets may fail to satisfy or characterize the uncertainty of the data in a comprehensive manner because they cannot depict the neutrality degree of time-series. Another important and decisive deficiency of current inference systems is to based on the univariate structure. However, the time series dealt with in a prediction problem generally interact with other time series. Considering these issues, creating an inference system based on intuitionistic fuzzy sets and multivariate relationships for a time series prediction problem is a requirement even an obligation. With these regards, this study presents a multivariate intuitionistic fuzzy time-series definition and its prediction models and introduces a multivariate intuitionistic fuzzy inference system (M-IFIS). The basic novelty of the article can be expressed as the definition of a multivariate intuitionistic fuzzy time series, as well as the creation of a relevant analysis mechanism, first-time in the literature. Sigma-pi neural network is used as an inference tool in M-IFIS and membership and non-membership values and lagged crisp observations of multivariable time-series are used as inputs of it. In order to reveal the performance of the proposed system, Istanbul Stock Exchange (IEX) and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) are analysed and the results are evaluated as comprehensive and comparative. All findings reveal the superiority M-IFIS in predictive accuracy.

Dendritic neuron model neural network trained by modified particle swarm optimization for time‐series forecasting

AbstractDifferent types of artificial neural networks (NNs), such as nonprobabilistic and computation‐based time‐series forecasting tools, are widely and successfully used in the time‐series literature. Whereas some of them use an additive aggregation function, others use a multiplicative aggregation function in the structure of their neuron models. In particular, recently proposed sigma‐pi NNs and dendritic NNs have additional and multiplicative neuron models. This study aims to take advantage of the dendritic neuron model neural network (DNM‐NN) in forecasting and hence uses the DNM‐NN trained by a modified particle swarm optimization as the main contribution of the study optimization in time‐series forecasting to improve the forecasting accuracy. To evaluate the forecasting performance of the DNM‐NN, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) was analyzed, and the obtained results were discussed together with the results produced by other time‐series forecasting models, including traditional, fuzzy‐based, and computational‐based models.

A new hybrid fuzzy time series model with an application to predict PM10 concentration

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.

DYNAMIC VOLATILITY SPILLOVERS ACROSS THE EXTENDED GREATER CHINA REGION STOCK MARKETS

The study investigates volatility transmissions of major financial market indices found in the Extended Greater China Region (EGCR): the Shanghai Stock Exchange Index (SSEI), the Hang Seng Stock Exchange Index (HSEI), the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and the Singapore Stock Exchange Index (STI). This paper utilizes three Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models to determine variance persistence in the EGCR. The MGARCH approach applies the Baba–Engle–Kraft–Kroner (BEKK) model and the dynamic conditional correlation (DCC) and constant conditional correlation (CCC) estimations. This research found that the SSEI consistently has the highest volatility among the stock markets in the EGCR, and this can be explained by the poor acknowledgment of minority shareholders’ rights or having a weak so-called “common-law” regime. The paper also found that volatilities in the EGCR are determined strongly by their own lagged values more than the product of lagged cross-products of shocks. This means that financial market movements in the EGCR are still strongly influenced by internal factors more than the external influences. The paper contributes to the literature by understanding the potential changes in volatility relationships in low- and high-volatility regimes of the EGCR, which can be used to improve risk management practices and asset allocation techniques in the region.

A Two-Factor Fuzzy-Fluctuation Time Series Forecasting Model for Stock Markets Based on a Probabilistic Linguistic Preference Relationship and Similarity Measure

An increasing number of scholars have tried to incorporate external factors affecting the disturbance of a time series into their forecasting models. However, these studies only verify the linkage relationship of two or more time series by empirical tests without providing any theoretical explanation. This makes it difficult to choose a linkage time series without using many tests. In this paper, a novel two-factor fuzzy-fluctuation time series (FFTS) forecasting model is proposed based on the probabilistic linguistic preference relationship (PLPR) and similarity measure. It not only proposes the idea of combining external factors with internal potential trends but also explains the linkage mechanism of time series fluctuations from the perspective of behavioral preference. Specifically, the probabilistic linguistic preference logical relationship (PLPLR) is employed to express the fluctuation behavior rule and preference attribute from the history testing dataset. The Euclidean distance or Hamming distance between the “current state” and the left side of training PLPLRs is introduced as a similarity comparison method for the identification of appropriate rules. The proposed model is tested using a traditional time series (e.g., the enrollment of the University of Alabama) to compare its performance with existing models. The model is also employed to forecast realistic stock markets, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Index (HSI). The performance comparison illustrates the effectiveness and universality of the model.

Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

A Multi-attribute Fuzzy Fluctuation Time Series Model Based on Neutrosophic Soft Sets and Information Entropy

Most existing forecasting models abstract logical rules that are based on historical discrete states in time series, and inconsistencies between these discrete states are rarely described quantitatively. In this paper, a multi-attribute fuzzy fluctuation time series-forecasting model based on neutrosophic soft sets (NSSs) and information entropy is proposed, which describes the complex changes of the logical rule training stage from the two characteristics of the state and volatility. The innovation and advantages of the model are mainly as follows: (1) The NSSs which have multi-attribute mapping and multi-dimensional expression functions can depict the complex state of multiple attributes in a specific period of time and thus characterise the state of the stock market clearly. (2) Using information entropy to quantify the degree of inconsistency of stock market fluctuations at a certain time which reflect the characteristics of volatility in the stock market effectively. (3) The similarity measure is used to find the optimal rule from the dimensions of state and volatility. To verify the validity of this model, this paper takes the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Experiments show that the model has a stable prediction performance for different data sets. Meanwhile, the prediction error is compared with other methods, which proves that the model has better prediction accuracy and versatility.

A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation.

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm

In this paper, we propose a new clustering-based fuzzy time series (FTS) forecasting method based on linear combinations of independent variables, the subtractive clustering algorithm and the artificial bee colony (ABC) algorithm. The subtractive clustering algorithm is used to automatically generate clusters of historical training data and get the cluster center of each cluster. The ABC algorithm is applied to obtain the optimal neighborhood radiuses of the subtractive clustering algorithm to get the optimal cluster center of each cluster of the historical training data. Based on the obtained cluster center of each cluster, the weighted contribution of each cluster with respect to each historical training datum is calculated. Finally, the proposed method constructs the linear combinations of independent variables of the historical training data using this weighted contribution to forecast the historical testing data. The proposed method gets higher forecasting accuracy rates than the existing methods for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the enrollments of the University of Alabama and the daily percentage of CO2.

Neutrosophic Soft Sets Forecasting Model for Multi-Attribute Time Series

Traditional time series forecasting models mainly assume a clear and definite functional relationship between the historical values and the current/future values of a dataset. In this paper, we extended the current model by generating multi-attribute forecasting rules based on the consideration of combining multiple related variables. In this model, neutrosophic soft sets (NSSs) are employed to represent historical statues of several closely related attributes in stock market, such as volumes, stock market index, and daily amplitudes. Specifically speaking, the status of up, equal, and down in historical stock index can be represented by truth, indeterminacy, and false, respectively, by neutrosophic sets (NSs) and NSSs can build mappings of different related attributes to NSs. The advantages of the proposed model are: 1) using NSSs to enclose different historical characteristics in time series to preserve inherent complexity of a dataset with mapping adequate features and 2) with the existing researches of NSSs, it is efficient with using Euclidean distance to find the optimal rules, and thus, the model can avoid incomplete of rules due to the limited sample dataset. To evaluate the performance of the model, we explored the closing prices of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major parameter that we forecast and the stock amplitudes and volumes as the other factors to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast some other influential indexes as well.

A Forecasting Model Based on Multi-Valued Neutrosophic Sets and Two-Factor, Third-Order Fuzzy Fluctuation Logical Relationships

Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well.

Analysis of the clientele effect and the information content of short‐term index option returns in Taiwan

We compare and contrast the clientele effect, information content and the buy‐and‐ hold returns of options with weekly and monthly expiration periods (Weeklys and Monthlys) traded on the Taiwan Stock Exchange Capitalization‐weighted Stock Index (TAIEX). No significant clientele effect is discernible in either market. Furthermore, Weeklys has the wider bid‐ask spread and lower depth clearly implies greater information asymmetry than Monthlys. Unlike Weeklys, Monthlys are found to play a leading informational role in TAIEX returns. We further observe that both types of options have significantly negative returns.

A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS) according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs) for a two-factor first-order autoregressive (AR(1)) model and forecasting the training data with the AR(1) model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m)) model. Lastly, we forecasted test data with the ARMA(1,m) model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) from 2001 to 2015 and the international gold price from 2000 to 2010.