Which Pure Chaos Model Will Describe IDX Composite (Jakarta Composite Index (JCI) of Indonesia Stock Exchange (IDX)) Better?

2009 ◽  
Author(s):  
Minarnita Yanti Verawati Bakara
Keyword(s):  
Stock Exchange ◽  
Composite Index

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

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.

scholarly journals The Influence of Trading Volume, Market Trend, and Monetary Policy on Characteristics of the Chinese Stock Exchange: An Econophysics Perspective

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 985-998
Author(s):  
Meng Ran ◽  
Zhenpeng Tang ◽  
Weihong Chen
Keyword(s):  
Monetary Policy ◽  
Trading Volume ◽  
Stock Exchange ◽  
Composite Index ◽  
Policy Formulation ◽  
Time Intervals ◽  
Monetary Policies ◽  
Policy Cycles ◽  
Trading Volumes ◽  
Healthy Growth

Abstract The paper adopts the financial physics approach to investigate influence of trading volume, market trend, as well as monetary policy on characteristics of the Chinese Stock Exchange. Utilizing 1-minute high-frequency data at various time intervals, the study examines the probability distribution density, autocorrelation and multi-fractal of the Shanghai Composite Index. Our study finds that the scale of trading volume, stock market trends, and monetary policy cycles all exert significant influences on micro characteristics of Shanghai Composite Index. More specifically, under the conditions of large trading volumes, loose monetary policies, and downward stock trends, the market possesses better fitting on Levy’s distribution, the volatility self-correlation is stronger, and multifractal trait is more salient. We hope our study could provide better guidance for investment decisions, and form the basis for policy formulation aiming for a healthy growth of the financial market.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Realita Dinamika Pasar: Studi Intensif Distingsi Syariah Konvensional

2019 ◽  
Vol 3 (2) ◽  
pp. 190-202
Author(s):  
Yadi Nurhayadi ◽  
Daram Heriansyah ◽  
Eva Susanti ◽  
Siti Azizziah Azzahra
Keyword(s):  
Multivariate Analysis ◽  
Stock Exchange ◽  
Composite Index ◽  
Stock Index ◽  
Weak Correlation ◽  
Company Stock ◽  
Regression Correlation

The research confirm the differences between sharia company stock index and conventional company stock index as the issuer at The Indonesia Stock Exchange. This research is a continuation of a series of previous studies by Nurhayadi et al earlier on the comparison between the sharia market and the conventional market. The Data consist of Jakarta Stock Exchange (JSX) Composite Index (Indeks Harga Saham Gabungan (IHSG)), Jakarta Stock Exchange Liquid Index (LQ45), Jakarta Islamic Index (JII), Indonesia Sharia Stock Index (ISSI), ten companies of sharia issuer, and ten companies of conventional issuer. There are seven scenarios based on bivariate and multivariate analysis that conducted regression, correlation, and determination test to know whether conventional company influence on sharia company. The research scenarios cover five years data from January 2014 to December 2018. The result confirms that the fluctuation of conventional issuer's stocks is different from the fluctuation of sharia issuer's stocks. Conventional issuers have a weak correlation with sharia issuers. This condition implies that between the conventional market and the Islamic market there is no correlation.

scholarly journals ANALISIS PENGARUH INDEKS HARGA SAHAM GABUNGAN REGIONAL ASIA TERHADAP INDEKS HARGA SAHAM GABUNGAN INDONESIA

2018 ◽  
Vol 1 (2) ◽  
pp. 148
Author(s):  
Widodo Widodo
Keyword(s):  
South Korea ◽  
Significant Influence ◽  
Stock Exchange ◽  
Composite Index ◽  
Stock Index ◽  
Kuala Lumpur ◽  
Korea Strait ◽  
Independent Variables

ABSTRACTThe aims of this research is to analyze the influence of NIKKEI 225 Index (^N225), HANG SENG Index (^HSI), KOSPI Index (^KS11), Strait Times Index (^STI), and Kuala Lumpur Stock Exchange (^KLSE) simultaneously and partially in Jakarta Composite Index (^JKSE) during 2009 to 2017. Method of multiple linier regression with significant level 0,05 using STATA 10 program. The populations and samples was used this research is stock index on ASIA regional (NIKKEI 225 (Japan), HANG SENG Index (Hongkong), KOSPI (South Korea), Strait Times Index (Singapore), Kuala Lumpur Stock Exchange (Malaysia), and Jakarta Composite Index (Indonesia)) was conducted during January 2009 to May 2017. Results of this research simultaneously model for all independent variables are influence to dependent variable. However, parcially model ^N225, ^KS11 and ^KLSE variables positive and significant influence to ^JKSE variable. Whereas ^HSI and ^STI variable are not effect to ^JKSE variable during January 2009 to May 2017.Keywords: JKSE; N225; HSI; KS11; STI; KLSE.

scholarly journals Factors Affecting the Indonesia Stock Exchange: A Multi-Index Approach

2020 ◽  
Vol 11 (2) ◽  
pp. 196
Author(s):  
Didik Susilo ◽  
Sugeng Wahyudi ◽  
Irene Rini Demi Pangestuti
Keyword(s):  
Capital Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Market Conditions ◽  
Factors Affecting ◽  
Daily Data ◽  
Index Approach ◽  
The World ◽  
Exchange Condition ◽  
Regional Capital

This study examines the influence of world and regional capital market conditions on the Indonesian capital market (Indonesia Stock Exchange) condition. The DJIA (Dow Jones Industrial Average) index was used as a representative of the international capital market while the Hang Seng index and the Nikkei 225 index were used as a representative of regional capital market conditions. These two indices were chosen because the Japanese capital market was one of the most advanced capital markets in the world and the Hong Kong capital market, although not as big as Japan, still played an important role in the world. The data were obtained from Yahoo Finance during the period of 2014-2018. The dependent variable was the change in the JCI (Jakarta Composite Index), while the independent variables were changes in the index of DJIA, Nikkei 225 and Hang Seng index. Using daily data analyzed by the ARIMA method (1,1), it was found that there was a significant positive effect of DJIA with lag 1 and Hang Seng index on the JCI, but no significant effect was found from the Nikkei 225 index on the JCI.

scholarly journals Forecasting Volatility with Time-Varying Coefficient Regressions

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qifeng Zhu ◽  
Miman You ◽  
Shan Wu
Keyword(s):  
Constant Coefficient ◽  
Stock Exchange ◽  
Composite Index ◽  
Predictive Ability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Out Of Sample ◽  
Time Varying Coefficients

We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.

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.

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