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

Tsai, Mi Ching; Cheng, Ching‐Hsue; Tsai, Meei-Ing

doi:10.3390/sym11121474

Cited by 15 publications

(5 citation statements)

References 52 publications

(84 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [28] implemented the stock index, trading volume, and the daily difference in two stock market indices to build a multifactor fuzzy time-series fitting model. e evaluation of this proposed model was for the NASDAQ Stock Market (NASDAQ), the Taiwan Stock Exchange Index (TAIEX), and the Hang Seng Index (HSI).…”

Section: Integrations Between International Financial Marketsmentioning

confidence: 99%

The Co‐Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

AL-Najjar

2022

Complexity

View full text Add to dashboard Cite

In the past two decades, especially after the financial crisis of 2007–09, the literature for examining the availability of integration between the stock exchanges in developed and developing markets has grown. The importance of this topic stems from the significant implications of the linkage between exchange markets on various decisions taken by interested parties, such as policymakers and investors, in the decisions for portfolio diversification. This study examines the relationship between a developing stock exchange index, Amman Stock Exchange Index (ASEI), and the number of international indices, including S&P 500, NASDAQ, Nikkei, DAX, CAC, and HSI for 2008-2019. To validate the availability of the linkage between the indices, the author includes various tests of a correlation coefficient, stepwise regression analysis, and artificial neural network (ANN). Despite the results indicating that the ANN is more efficient than linear regression in investigating the availability of the relationship between ASEI and international indices, stepwise regression and neural network support this relationship. Furthermore, ANN results revealed that the S&P 500 index and year have the most substantial relationship with ASEI. Our research is theoretically and practically important; policymakers and investors can benefit from our findings. Future studies may explore the effect of different international stock market indices on ASEI or other developing markets. Further studies can use macroeconomic factors to build prediction models for stock market indices.

show abstract

Section: Integrations Between International Financial Marketsmentioning

confidence: 99%

The Co‐Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

AL-Najjar

2022

Complexity

View full text Add to dashboard Cite

show abstract

“…Several researchers have focused on using different input variables (i.e., day, month, year, trading volume, etc.) to study the power of estimation on stock market index [19][20][21][22][23][24][25]. Heston and Sinha [29] used neural network to investigate their effect on predicting stock index returns.…”

Section: Discussionmentioning

confidence: 99%

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

Assous

Al-Rousan

AL-Najjar

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices.

show abstract

“…Mansour et al [15] formulated a multiobjective financial portfolio selection approach involving fuzzy parameters, where the distribution options are given by fuzzy numbers from the information provided by the decision environment. Tsai et al [16], in contrast to traditional methods, use more variables included in the fuzzy model to predict and better reflect the issue of stock volatility.…”

Section: Review Of the Scientific Literaturementioning

confidence: 99%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

show abstract

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

Cited by 15 publications

References 52 publications

The Co‐Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

The Co‐Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Contact Info

Product

Resources

About