scholarly journals Multilayer Stock Forecasting Model Using Fuzzy Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hossein Javedani Sadaei ◽  
Muhammad Hisyam Lee
Keyword(s):  
Stock Market ◽  
Model Development ◽  
Single Layer ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Forecasting Model ◽  
Stock Market Forecasting ◽  
Huge Data ◽  
Dow Jones Industrial Average ◽  
Systematic Framework

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

scholarly journals Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Ya’nan Wang ◽  
Yingjie Lei ◽  
Xiaoshi Fan ◽  
Yi Wang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Intuitionistic Fuzzy Set ◽  
Time Series Forecasting ◽  
Approximate Reasoning ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Forecasting Model ◽  
New Model ◽  
Intuitionistic Fuzzy

Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

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.

An integrated stock market forecasting model using neural networks

2008 ◽  
Vol 1 (1) ◽  
pp. 30 ◽  
Author(s):  
Gary R. Weckman ◽  
Sriram Lakshminarayanan ◽  
Jon H. Marvel ◽  
Andy Snow
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Forecasting Model ◽  
Stock Market Forecasting

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.

A refined fuzzy time series model for stock market forecasting

2008 ◽  
Vol 387 (12) ◽  
pp. 2857-2862 ◽  
Author(s):  
Tahseen Ahmed Jilani ◽  
Syed Muhammad Aqil Burney
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Stock Market Forecasting

scholarly journals Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 3492-3499
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Stock Exchange ◽  
Event Extraction ◽  
Forecasting Model ◽  
Future Trends ◽  
Forecasting Accuracy ◽  
Stock Market Forecasting ◽  
Source Data ◽  
Multi Agent

Analyzing and forecasting the future trends in stock market is challenging due to the ever increasing size of stock data. Modern techniques extract the stock indicators from the web data to forecast the stock movements. However, most previous studies were based on single source of data for extracting these indicators. This might not be effective in obtaining all the possible diverse factors that influence the market movements. Multi-source data has been rarely applied for stock prediction and even those techniques have limitations in handling larger data. In an attempt to utilize multi-source data more effectively for extracting stock indicators and improve the forecasting accuracy of stock movements, this paper developed a stock market forecasting model using Tolerance based Multi-Agent Deep Reinforcement Learning (TMA-DRL) model. The TMA-DRL model effectively combines the quantitative stock data with the indicators i.e. the events extracted from news data and sentiments extracted from tweets. This forecasting model utilizes Random forests to extract the twitter opinions and Restricted Boltzmann Machine (RBM) for event extraction from news data. Combining these indicators, the TMA-DRL model leads to improved data learning and provides highly accurate prediction of future stock trends. Datasets for evaluation were collected from three sources namely Twitter, Market News and Stock exchange, for 12 months period. Evaluation results illustrate the effectiveness of the proposed TMA-DRL stock market forecasting model which makes predictions with high accuracy and less time complexity.

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