AN EMPIRICAL STUDY ON THE STATISTICAL PROPERTIES OF ROMANIAN EMERGING STOCK MARKET RASDAQ

2004 ◽  
Vol 07 (06) ◽  
pp. 723-739 ◽  
Author(s):  
MIRCEA GLIGOR
Keyword(s):  
Stock Market ◽  
Long Range ◽  
Composite Index ◽  
Nonlinear Function ◽  
Stock Index ◽  
Absolute Value ◽  
New Information ◽  
Range Correlation ◽  
Long Time ◽  
Index Changes

An empirical analysis of the Romanian emerging stock market RASDAQ based on the statistical study of the composite index RASDAQ-C reveals the leptokurtic profile of the probability density function (p.d.f.) of the stock index changes, the power law asymptotic behaviour of the p.d.f., the breakdown of scaling at long time scales, the absence of linear correlation in the stock index changes but existence of long-range correlation in nonlinear function such as the absolute value or the square of index changes (implicitly the long-range correlation in the index volatility). These results, consistent with the similar referring to the more liquid markets, suggest the presence of several universal features, in addition to several particularities related to the quickness of assimilation of the new information and its impact over the investors.

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 VOLATILITAS INDEKS KOMPOSIT PASAR MODAL ASEAN-3

2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Anhar Fauzan Priyono
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Exchange Rate Regime ◽  
Composite Index ◽  
Stock Index ◽  
Movement Behavior ◽  
Stock Market Index ◽  
Monthly Basis ◽  
Volatility Model ◽  
Aggregate Investment

Rapid integration between domestic and world economy in the last decade has been a major issue. For Indonesia, the situation has been accelerated by the adoption of floating exchange rate regime in 1997, also with the development of Indonesia stock exchange. One notable financial variable that often exposed to external shocks is stock market index. This research will analyzed the behavior of 3 major stock market indices in ASEAN, those are Jakarta Composite Index (JCI), Kuala Lumpur stock index (KLSE), and Singapore stock index (STI). The employment of volatility model is chosen to figured the behavior of those 3 indices, and to analyze the aggregate investment in each stock market. Observation will be based upon monthly basis, from 2010 until 2015.The findings in this research are (i) similarity in the movement behavior of ASEAN-3 stock market indices, (ii) Indonesia stock market shows the highest aggregate investment return relative to Malaysia and Singapore, (iii) Singapore stock market shows the lowest aggregate investment risk relative to Indonesia and Malaysia, as the representation of more developed stock market.

scholarly journals Analisa Korelasi Return Indeks – Indeks Saham terhadap Indeks Harga Saham Gabungan pada Bursa Efek Indonesia

2015 ◽  
Vol 6 (2) ◽  
pp. 330 ◽  
Author(s):  
Mulyono Mulyono
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Composite Index ◽  
Stock Index ◽  
Stock Trading ◽  
Close Relationship ◽  
Coefficient Corrélation ◽  
Stock Price Index

Stock market generally has the stock price index that measures the performance of stock trading, the Indonesia Stock Exchange has a stock price index that is widely known as Jakarta Composite Index (IHSG). During its development, the Indonesia Stock Exchange has many alternative indexes that measure the performance of stock trading. Research that is to be conducted on the correlation between return of the stock index listed in Indonesia Stock Exchange and return of Jakarta Composite Index. Return stock index listed on the Indonesia Stock Exchange, namely, LQ45 Index, Jakarta Islamic Index (JII), KOMPAS100 Index, BISNIS-27 Index, PEFINDO25 Index and SRI-KEHATI Index, has a close relationship with the return Jakarta Composite,Index which is a reflection of the movement of all existing stock in the market. Return of stocks index that have the highest coefficient correlation is KOMPAS100 In dex, which have return index coefficient correlation is 0.949, thus KOMPAS100 Index that consisting of 100 stocks, based on the results of the study can be used as an alternative investment to get a return that is at least equal or close to the yield given by Jakarta Composite Index(IHSG) that consists of 445 stocks

RANDOM WALKS IN RANDOM ENVIRONMENT WITH LONG-RANGE CORRELATED DRIFT FORCE

2002 ◽  
Vol 16 (24) ◽  
pp. 3561-3566
Author(s):  
ANDREI A. FEDORENKO ◽  
STEFFEN TRIMPER
Keyword(s):  
Fixed Point ◽  
Long Range ◽  
Time Behavior ◽  
Long Time Behavior ◽  
Random Drift ◽  
Short Range Correlation ◽  
Range Correlation ◽  
Long Time ◽  
Drift Force ◽  
Drift Forces

We consider the random walk in a d-dimensional environment with positionally random drift forces obeying power law correlations ~ |x|-a for large distances x. This model is studied using a renormalization group expansion in ε = 2 - d, δ = 2-a. We find a new long-range fixed point in addition to the short range correlation and the pure fixed points found previously. The new fixed point is stable for δ > 2ε, δ > 0 and it leads to a subdiffusive long-time behavior with dynamical critical exponent z = 2 + (1/2) δ2.

scholarly journals Volatility Spillover Effect of International Crude Oil Futures and China-Russia Stock Market: A Multivariate BEKK-GARCH Model Based on Wavelet Multiresolution Analysis

2019 ◽  
Vol 11 (1) ◽  
pp. 183
Author(s):  
Maoguo Wu ◽  
Daimin Lu
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Explanatory Power ◽  
Composite Index ◽  
Spillover Effect ◽  
Stock Index ◽  
Oil Futures ◽  
Crude Oil Futures ◽  
Futures Price ◽  
Volatility Spillover Effect

The increasingly prominent strategic position of crude oil determines its high impact on macro-economy. The value of crude oil is reflected in the price of crude oil futures. Stock market is the barometer of macro economy. To what extent does international crude oil futures price affect stock market? China and Russia are the biggest importer and exporter of crude oil, respectively. Crude oil is of strategic value to both countries. This study empirically investigates the volatility spillover effect of international crude oil futures and China-Russia stock market from April 24th, 2015 to April 20th, 2018, based on the data of international crude oil futures prices, China-Russia stock market composite index, and industry stock index. The empirical results show that there is a short-term relationship between China-Russia stock market composite index and international crude oil futures price. The international crude oil futures price has a greater explanatory power to Russian RTS index, but a smaller explanatory power to Shanghai composite index. All industry stock indices are cointegrated with international crude oil futures prices. Except for China industry and Russia energy, the adjustment coefficient of international crude oil futures price on stock index volatility of other industries is insignificant. This study mainly studies the relationship between international crude oil futures price and the comprehensive stock index and industry stock index of China and Russia, and compares the impact of international crude oil futures price on the stock market of the largest importer and the largest exporter of crude oil to explore the linkage between crude oil futures price and stock market, and puts forward policy implications based on the empirical results.

scholarly journals Whether the CMIP5 Models Can Reproduce the Long-Range Correlation of Daily Precipitation?

2021 ◽  
Vol 9 ◽  
Author(s):  
Tianyun Dong ◽  
Shanshan Zhao ◽  
Ying Mei ◽  
Xiaoqiang Xie ◽  
Shiquan Wan ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Long Range ◽  
Daily Precipitation ◽  
Cmip5 Models ◽  
High Latitudes ◽  
Scaling Exponents ◽  
Absolute Value ◽  
Four Seasons ◽  
Range Correlation ◽  
The Tropics

In this study, we investigated the performance of nine CMIP5 models for global daily precipitation by comparing with NCEP data from 1960 to 2005 based on the detrended fluctuation analysis (DFA) method. We found that NCEP daily precipitation exhibits long-range correlation (LRC) characteristics in most regions of the world. The LRC of daily precipitation over the central of North American continent is the strongest in summer, while the LRC of precipitation is the weakest for the equatorial central Pacific Ocean. The zonal average scaling exponents of NCEP daily precipitation are smaller in middle and high latitudes than those in the tropics. The scaling exponents are above 0.9 over the tropical middle and east Pacific Ocean for the year and four seasons. Most CMIP5 models can capture the characteristic that zonal mean scaling exponents of daily precipitation reach the peak in the tropics, and then decrease rapidly with the latitude increasing. The zonal mean scaling exponents simulated by CMCC-CMS, GFDL-ESM2G and IPSL-CM5A-MR show consistencies with those of NCEP, while BCC_CSM1.1(m) and FGOALS-g2 cannot capture the seasonal variations of daily precipitation’s LRC. The biases of scaling exponents between CMIP5 models and NCEP are smaller in the high latitudes, and even less than the absolute value of 0.05 in some regions, including Arctic Ocean, Siberian, Southern Ocean and Antarctic. However, for Western Africa, Eastern Africa, Tropical Eastern Pacific and Northern South America, the simulated biases of scaling exponents are greater than the absolute value of 0.05 for the year and all four seasons. In general, the spatial biases of LRC simulated by GFDL-ESM2G, HadGEM2-AO and INM-CM4 are relatively small, which indicating that the LRC characteristics of daily precipitation are well simulated by these models.

scholarly journals Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1612
Author(s):  
Yuxuan Xiu ◽  
Guanying Wang ◽  
Wai Kin Victor Chan
Keyword(s):  
Stock Market ◽  
Power Law ◽  
Stock Exchange ◽  
Composite Index ◽  
Detection Algorithm ◽  
Value Theory ◽  
Visibility Graph ◽  
Stock Index ◽  
Power Law Model ◽  
Market Crashes

This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.

scholarly journals Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yonghui Dai ◽  
Dongmei Han ◽  
Weihui Dai
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Stock Market ◽  
Bp Neural Network ◽  
Transition Probability ◽  
Back Propagation ◽  
Transition Probability Matrix ◽  
Stock Index ◽  
Long Time ◽  
Stock Index Forecasting

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

scholarly journals The Stability of the Indonesian Sharia Stock Index to Economic Shocks

2020 ◽  
Vol 13 (2) ◽  
pp. 138
Author(s):  
Faizul Mubarok ◽  
Mohammad Nur Rianto Al Arif ◽  
Muhammad Arief Mufraini
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Historical Record ◽  
Stock Index ◽  
Series Data ◽  
Strategic Role ◽  
Long Time ◽  
Short And Long Term ◽  
The Stability

<p>The stock market has a strategic role in the development of a country's economy in the era of globalization, including the Islamic stock market. The rapid growth of the Islamic stock market, especially in developing countries, is a historical record in Indonesia's financial sector. This study aims to analyze the factors that influence the return of the Indonesian Sharia Stock Index (ISSI) in the short and long term, how long shocks occur, and how much the contribution of these factors. This study uses monthly time series data from January 2012 to December 2019 using the Vector Error Correction Model (VECM) method. VECM estimation results show the price of gold has a significant effect on the short and long term, while inflation has an impact on a long time. ISSI's return quickly reaches stability when it receives a shock from the exchange rate. The price of gold dominates the diversity of ISSI's performances. Stakeholders should consider several things that affect the ISSI return, pay attention to the economic climate, and anticipate quickly the shock that occurs.</p>

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