scholarly journals A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1474 ◽  
Author(s):  
Ming-Chi Tsai ◽  
Ching-Hsue Cheng ◽  
Meei-Ing Tsai
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Sequential Data ◽  
Proposed Model ◽  
Market Indexes ◽  
Fitting Model

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

scholarly journals Pengujian Metode Fuzzy Time Series Chen dan Hsu Untuk Meramalkan Nilai Indeks Bursa Saham Syariah Di Jakarta Islamic Index (JII)

2017 ◽  
Vol 7 (2) ◽  
pp. 108-124
Author(s):  
Rizka Zulfikar ◽  
Prihatini Ade`Mayvita
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Series Method ◽  
Forecasting Error ◽  
Time Series Method ◽  
The Difference ◽  
Market Indexes

This research is an  empirical  study to tested  the accuracy  of Chen  and  Hsu’s  Fuzzy Time Series Method used to forecast  sharia  market  stock index in Jakarta Islamic  Index. The data  used in this research are  secondary  data  consists of daily stock market indexes during  23 November 2016 to 14 July 2017.  Chen dan Hsu’s Fuzzied Series Method used in this research has the smallest MSE (Mean Square Error)  and AFER (Average Forecasting Error  Rate) value rather  than others method such as Song and Chrissom (1993). Song and Chrissom (1994), Chen (1996), Hwang, Chen and Lee (1998), Huarng  (2001)  and  Chen (2002). To tested  the accuracy  of the Chen’s  dan  Hsu’s Fuzzied Series. Method researcher has to do 5 (five) steps such as (1) Determine lag between historical  data, interval and The Universe Data  (U), (2) Distributing  Data  into The Unniverse,  (3) Define The Fuzzy Set, (4) Determine The Fuzzy Logical Relationship (FLR), and (5) Analyse the Difference between data. There are 3 (three) rules in Chen’s dan Hsu’s Fuzzied Series Method based on the Difference and FLR.  The result of this research is Chen dan Hsu’s Fuzzied Series Method has MSE = 1.88 and AFER =0.006% and  it can  be used to make forecasting  on value and trend  sharia  stock market  in Jakarta  Islamic index.

HEURISTIC BIVARIATE FORECASTING MODEL OF MULTI-ATTRIBUTE FUZZY TIME SERIES BASED ON FUZZY CLUSTERING

2012 ◽  
Vol 11 (01) ◽  
pp. 167-195 ◽  
Author(s):  
GUOFANG NAN ◽  
SHUAIYIN ZHOU ◽  
JISONG KOU ◽  
MINQIANG LI
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Bivariate Model ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Universe ◽  
Universe Of Discourse

Fuzzy time series has been applied to forecast various domain problems because of its capability to deal with vagueness and incompleteness inherent in data. However, most existing fuzzy time series models cannot cope with multi-attribute time series and remain too subjective in the partition of the universe of discourse. Moreover, these models do not consider the trend factor and the corresponding external time series, which are highly relevant to target series. In the current paper, a heuristic bivariate model is proposed to improve forecasting accuracy, and the proposed model applies fuzzy c-means clustering algorithm to process multi-attribute fuzzy time series and to partition the universe of discourse. Meanwhile, the trend predictors are extracted in the training phase and utilized to select the order of fuzzy relations in the testing phase. Finally, the proper full use of the external series to assist forecasting is discussed. The performance of the proposed model is tested using actual time series including the enrollments at the University of Alabama, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a sensor dataset. The experimental results show that the proposed model can be utilized for multi-attribute time series and significantly improves the average MAER to 1.19% when compared with other forecasting models.

scholarly journals Pengujian Metode Fuzzy Time Series Chen dan Hsu Untuk Meramalkan Nilai Indeks Bursa Saham Syariah Di Jakarta Islamic Index (JII)

2018 ◽  
Vol 7 (2) ◽  
pp. 108-124
Author(s):  
Rizka Zulfikar ◽  
Prihatini Ade'Mayvita
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Series Method ◽  
Forecasting Error ◽  
Time Series Method ◽  
The Difference ◽  
Market Indexes

This research is an  empirical  study to tested  the accuracy  of Chen  and  Hsu’s  Fuzzy Time Series Method used to forecast  sharia  market  stock index in Jakarta Islamic  Index. The data  used in this research are  secondary  data  consists of daily stock market indexes during  23 November 2016 to 14 July 2017.  Chen dan Hsu’s Fuzzied Series Method used in this research has the smallest MSE (Mean Square Error)  and AFER (Average Forecasting Error  Rate) value rather  than others method such as Song and Chrissom (1993). Song and Chrissom (1994), Chen (1996), Hwang, Chen and Lee (1998),   Huarng  (2001)  and  Chen (2002).   To tested  the accuracy  of the Chen’s  dan  Hsu’s Fuzzied Series   Method researcher has to do 5 (five) steps such as (1) Determine lag between historical  data, interval and The Universe Data  (U), (2) Distributing  Data  into The Unniverse,  (3) Define The Fuzzy Set, (4) Determine The Fuzzy Logical Relationship (FLR), and (5) Analyse the Difference between data. There are 3 (three) rules in Chen’s dan Hsu’s Fuzzied Series Method based on the Difference and FLR.  The result of this research is Chen dan Hsu’s Fuzzied Series Method has MSE = 1.88 and AFER =0.006% and  it can  be used to make forecasting  on value and trend  sharia  stock market  in Jakarta  Islamic index.

Multi-Factor Fuzzy Time Series Model Based on Stock Volatility for Forecasting Taiwan Stock Index

2011 ◽  
Vol 211-212 ◽  
pp. 1119-1123 ◽  
Author(s):  
Ching Hsue Cheng ◽  
Jing Wei Liu ◽  
Tzu Hsuan Lin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Series Data ◽  
Stock Volatility ◽  
Taiwan Stock Exchange ◽  
Proposed Model

Fuzzy time series have in recent years drawn many scholars' attention due to their ability can handle the time series data with incomplete, imprecise and ambiguous pattern. However, most traditional time series models employed only single variable (stock index) in forecasting, yet ignored some factors that would also affect the stock volatility. Therefore, this paper proposes a novel forecasting model using multi-factor fuzzy time series model to forecast TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index). Multi-factor fuzzy time series model is composed of three main components: stock index, trading volume and interactions between two stock markets. In order to evaluate the performance of the proposed model, the transaction records of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index) and NASDAQ(National Association of Securities Dealers Automated Quotations) from 2000/01/04 to 2003/12/31 are used as experimental dataset and the root mean square error (RMSE) as evaluation criterion. The results show that the proposed model outperforms the listing models in accuracy for forecasting Taiwan stock market.

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

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

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.

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

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

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 954
Author(s):  
Aiwu Zhao ◽  
Junhong Gao ◽  
Hongjun Guan
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Exchange ◽  
Forecasting Model ◽  
Historical Time ◽  
Logical Relationship ◽  
Probabilistic Linguistic Term Set ◽  
Time Series Dataset ◽  
Proposed Model ◽  
Linguistic Term

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.

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

scholarly journals Forecasting Based on High-Order Fuzzy-Fluctuation Trends and Particle Swarm Optimization Machine Learning

2017 ◽  
Author(s):  
Jingyuan Jia ◽  
Aiwu Zhao ◽  
Shuang Guan
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Stock Exchange ◽  
Particle Swarm ◽  
High Order ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Swarm Optimization ◽  
Training Time ◽  
Comparison Results

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each data with the data of its previous day in historical training time series to generate a new fluctuation trend time series(FTTS). Then, fuzzify the FTTS into fuzzy-fluctuation time series(FFTS) according to the up, equal or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of future is nonlinear, Particle Swarm Optimization (PSO) algorithm is employed to estimate the required parameters. Finally, use the acquired parameters to forecast the future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or such kinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

scholarly journals Time series prediction: A combination of Long Short-Term Memory and structural time series models

2020 ◽  
Vol 4 (1) ◽  
pp. 500-515
Author(s):  
Quoc Luu ◽  
Son Nguyen ◽  
Uyen Pham
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Time Series Prediction ◽  
Structural Models ◽  
Short Term ◽  
Proposed Model ◽  
Auto Regressive ◽  
Long Short Term Memory

Stock market is an important capital mobilization channel for economy. However, the market has potential loss due to fluctuations of stock prices to reflect uncertain events such as political news, supply and demand of daily trading volume. There are many approaches to reduce risk such as portfolio construction and optimization, hedging strategies. Hence, it is critical to leverage time series prediction techniques to achieve higher performance in stock market. Recently, Vietnam stock markets have gained more and more attention as their performance and capitalization improvement. In this work, we use market data from Vietnam’s two stock market to develop an incorporated model that combines Sequence to Sequence with Long-Short Term Memory model of deep learning and structural models time series. We choose 21 most traded stocks with over 500 trading days from VN-Index of Ho Chi Minh Stock Exchange and HNX-Index of Hanoi Stock Exchange (Vietnam) to perform the proposed model and compare their performance with pure structural models and Sequence to Sequence. For back testing, we use our model to decide long or short position to trade VN30F1M (VN30 Index Futures contract settle within one month) that are traded on HNX exchange. Results suggest that the Sequence to Sequence with LSTM model of deep learning and structural models time series achieve higher performance with lower prediction errors in terms of mean absolute error than existing models for stock price prediction and positive profit for derivative trading. This work significantly contribute to literature of time series prediction as our approach can relax heavy assumptions of existing methodologies such as Auto-regressive–moving-average model, Generalized Auto-regressive Conditional Heteroskedasticity. In practical, investors from Vietnam stock market can use the proposed model to develop trading strategies.

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