A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy

Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality.

Download Full-text

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

Entropy ◽

10.3390/e21050455 ◽

2019 ◽

Vol 21 (5) ◽

pp. 455 ◽

Cited By ~ 5

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.

Download Full-text

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

10.20944/preprints201707.0006.v1 ◽

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.

Download Full-text

Dividend Payout Ratio: Persoalan Free Cashflow, Debt To Equity Ratio, Kepemilikan Institutional, Investment Opportunity, Dan Kesejahteraan Karyawan

Liquidity ◽

10.32546/lq.v6i1.35 ◽

2017 ◽

Vol 6 (1) ◽

pp. 1-11

Author(s):

Nurlis Azhar ◽

Helmi Chaidir

Keyword(s):

Cash Flow ◽

Significant Influence ◽

Stock Exchange ◽

Institutional Ownership ◽

Free Cash Flow ◽

Manufacturing Companies ◽

Flow Ratio ◽

Prediction Ability ◽

Employee Welfare ◽

Equity Ratio

This study was conducted to examine the effect of Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) to Divident Payout Ratio (Parliament) partially on manufacturing companies listed on Indonesia Stock Exchange period 2011-2015. In addition, to test the feasibility of regression model, the influence of Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) to Divident Payout Ratio (DPR) simultaneously at manufacturing company listed on Bursa Indonesia Securities period 2011-2015. The population in this study are 146 manufacturing companies that have been and still listed in Indonesia Stock Exchange period 2011-2013. The sampling technique used was purposive sampling and obtained sample of 42 companies. Data analysis technique used is by using multiple linear regression test. The results showed that Free Cash Flow Ratio, no significant effect on Divident Payout Ratio (DPR). Debt Equity Ratio (DER) has a negative and significant influence on Divident Payout Ratio (DPR), Institutional Ownership has a significant positive effect on Divident Payout Ratio (DPR), Employee Welfare and Price Earning Ratio (PER) has a positive and significant influence on the Divident Payout Ratio ). Simultaneously Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) give effect to Divident Payout Ratio. The prediction ability of the five variables to the Divident Payout Ratio (DPR) is 21.3% as indicated by the adjusted R square of 0.271 while the remaining 79.7% is influenced by other factors not included in the research model.

Download Full-text

META-ANALYSIS ON AMERICAN PRESIDENTIAL ELECTION - 2016 AND AMERICAN STOCK MARKET

Studies of Applied Economics ◽

10.25115/eae.v38i1.2948 ◽

2020 ◽

Vol 38 (1) ◽

Author(s):

Farhan Ahmed ◽

Salman Bahoo ◽

Sohail Aslam ◽

Muhammad Asif Qureshi

Keyword(s):

Content Analysis ◽

Stock Market ◽

Event Study ◽

Presidential Election ◽

Republican Party ◽

Stock Exchange ◽

Meta Analysis ◽

Composite Index ◽

Stock Index ◽

Political Event

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.

Download Full-text

Chaotic Processes of Common Stock Index Returns: An Empirical Examination on Istanbul Stock Exchange (ISE) Market

SSRN Electronic Journal ◽

10.2139/ssrn.1617929 ◽

2010 ◽

Cited By ~ 1

Author(s):

GGkhan zer ◽

Cengiz Tansel Ertokatll

Keyword(s):

Stock Exchange ◽

Stock Index ◽

Common Stock ◽

Empirical Examination ◽

Istanbul Stock Exchange ◽

Stock Index Returns

Download Full-text

A Historical Data based Ensemble System for Efficient Stock Price Prediction

Recent Patents on Computer Science ◽

10.2174/2213275912666190730161807 ◽

2019 ◽

Vol 12 ◽

Author(s):

Vijay Kumar Dwivedi ◽

Manoj Madhava Gore

Keyword(s):

Stock Price ◽

Prediction Models ◽

Historical Data ◽

Stock Exchange ◽

Primary Objective ◽

Lasso Regression ◽

Stock Price Prediction ◽

Price Prediction ◽

Consistent Manner ◽

Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

Download Full-text