scholarly journals The Stock Market Situation and Economic Growth – An Attempt to Assess the Dependence

2021 ◽  
Vol 4 (32) ◽  
pp. 117-128
Author(s):  
Michał Radke
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Pearson Correlation ◽  
Research Work ◽  
Calculated Data ◽  
Stock Index ◽  
Rate Of Return ◽  
Real Economy ◽  
Stock Market Index ◽  
Market Situation

The aim of the article: The main aim of the article is to analyze the relationship between the stock market situation and the real economy, measured by the strength of the correlation between the rate of return on the stock market and the rate of GDP growth in European capital markets. The next objective is to answer the question whether the stock market index changes are ahead of, and if so, by how much, GDP changes. The author’s hypothesis stipulates that the stock exchange situation precedes the change in economic activity and serves as its forecast. Methodology: The empirical research work was carried out on the basis of quarterly data value of the stock index and the GDP between 2010 and the first quarter of 2021 for 20 European countries. For indices and GDP, the quarterly dynamics of the rate of return and GDP were calculated. Data on the value of the stock exchange index was taken from the website www.stooq.pl, while data on GDP was taken from Eurostat. Subsequently, the analysis concerned the correlation relationships between the variables on the basis of the Pearson correlation coefficient. The correlation between the variables was calculated without delay, as well as with a delay of one, two or three quarters of the returns on stock indices. Results of the research: Changes in the value of the stock exchange index is in most cases positively correlated with the change in GDP and the correlation is pronounced, but it is low and moderate. The only market for which a significant correlation was observed, was the Polish market. At the same time, it can be stated that the rates of return on the stock exchange index precede a change in GDP by one or three quarters. No changes were observed for the analyzed countries for two quarters.

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 Time Series Analysis on Monthly Stock Market Returns of the Nigerian Stock Exchange: An Arima Modeling Approach

2019 ◽  
pp. 1-9
Author(s):  
Eke, Charles N.
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Arima Model ◽  
Research Work ◽  
Information Criteria ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Arima Modeling ◽  
Best Fit

This research work studied the autoregressive integrated moving average (ARIMA) model that best fitted monthly stock market returns of the Nigerian Stock Exchange between January, 2008 to September, 2018. The study collected secondary data from Central Bank of Nigeria (CBN) Statistical Bulletin 2018 on monthly stock market index of NSE to compute the monthly stock market returns. The Box-Jenkins ARIMA modeling was adopted for this work. The series was tested for stationarity using Augmented Dickey Fuller test. Several ARIMA (p, d, q) models were applied to the monthly stock market returns to ascertain the best fit model for the series. The ARIMA (2, 0, 3) model was selected as the best fit for the data since it has minimum values of Akaike Information Criteria and Mean Squared Errors. The forecasted period showed a market with an unstable monthly stock market returns. Therefore, investors were advised to weigh the risks before venturing into the market to invest.

scholarly journals DPP: Deep predictor for price movement from candlestick charts

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252404
Author(s):  
Chih-Chieh Hung ◽  
Ying-Ju Chen
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Extensive Study ◽  
Stock Index ◽  
Stock Market Index ◽  
Price Movement ◽  
Market Prices ◽  
Taiwan Stock Exchange ◽  
Market Index ◽  
Political Events

Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

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.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

scholarly journals ANALISIS PORTOFOLIO SEBAGAI DASAR PENGAMBILAN KEPUTUSAN INVESTASI SAHAM YANG TERDAFTAR DI BEI

IQTISHODUNA ◽  
2013 ◽  
Author(s):  
Sri Yati
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Analytical Method ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Portfolio Analysis ◽  
Rate Of Return ◽  
Expected Returns ◽  
Risk Level ◽  
Expected Return

This study aims to analyze rate of return and risk as the tools to form the portfolio analysis on 15 the most actives stocks listed in Indonesian Stock Exchange. Descriptive analytical method is used to describe the correlation between three variables: stock returns, expected returns of stock market, and beta in order to measure the risk of stocks to help the investors in making the investment decisions. The research materials are 15 the most actives stocks listed in Indonesian Stock Exchange during 2008-2009. The results show that PT. Astra International Tbk. has the highest average expected return of individual stock (Ri) of 308,3355685, while PT. Perusahaan Gas Negara Tbk. has the lowest of -477,0827847. The average expected return of stock market (Rm) is 0,00247163. PT. Astra International Tbk. has the highest systematic risk level of 20229,14205, while the lowest of -147,5793279 is PT. Kalbe Farma Tbk. Furthermore, the results also indicate that there are 9 stocks can be combined to form optimal portfolio because they have positive expected returns.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

scholarly journals Trading Volume as a Predictor of Market Movement

2019 ◽  
Vol 8 (2) ◽  
pp. 57-69
Author(s):  
Edson Kambeu
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Regression Model ◽  
Trading Volume ◽  
Logistic Regression Model ◽  
Predictive Analytics ◽  
Stock Exchange ◽  
Stock Market Index ◽  
Trading Volumes ◽  
Popular Language

A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

In Search of Stock Market Proxy to Calculate Systematic Risk (Beta) of Stocks in Indonesia Stock Exchange

2020 ◽  
Vol 29 (2) ◽  
pp. 80-88
Author(s):  
Mochammad Chabachib
Keyword(s):  
Stock Market ◽  
Real World ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
The Other ◽  
Stock Index ◽  
Market Return ◽  
Stock Market Return ◽  
Stock Index Returns

The calculation of beta stock in Indonesia is still debatable to this day. Though many researchers who have used sophisticated methods mathematically, the assumptions applied in developing the methods are impossible to happen in the real world, such as the ability of stock market return the day after (lead) affects the market return today. This study was conducted to assess the stock price index in Indonesia Stock Exchange that can be used as a proxy of stock market in Indonesia. The results of this study showed that there was a gap between beta stocks counted with JCI return as a market proxy with beta stocks counted with index returns of LQ-45, SRI-KEHATI, PEFINDO-25, BISNIS-27, IDX-30 and KOMPAS-100. This study has also found that the beta counted by using KOMPAS-100 return produced the smallest standard error of the estimate (SEE) that it was more applicable compared to the other stock index returns.

scholarly journals Interdependence and Risk Comparison of Slovak, Hungarian and Polish Stock Markets: Policy and Managerial Implications

2019 ◽  
Vol 69 (2) ◽  
pp. 273-287 ◽  
Author(s):  
Florin Aliu ◽  
Besnik Krasniqi ◽  
Adriana Knapkova ◽  
Fisnik Aliu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Exchange ◽  
Weighted Average ◽  
Stock Index ◽  
Risk Level ◽  
Average Return ◽  
Monetary System ◽  
Managerial Implications

Risk captured through the volatility of stock markets stands as the essential concern for financial investors. The financial crisis of 2008 demonstrated that stock markets are highly integrated. Slovakia, Hungary and Poland went through identical centralist economic arrangement, but nowadays operate under diverse stock markets, monetary system and tax structure. The study aims to measure the risk level of the Slovak Stock Market (SAX index), Budapest Stock Exchange (BUX index) and Poland Stock Market (WIG20 index) based on the portfolio diversification model. Results of the study provide information on the diversification benefits generated when SAX, BUX and WIG20 join their stock markets. The study considers that each stock index represents an independent portfolio. Portfolios are built to stand on the available companies that are listed on each stock index from 2007 till 2017. The results of the study show that BUX generates the lowest risk and highest weighted average return. In contrast, SAX is the riskiest portfolio but generates the lowest weighted average return. The results find that the stock prices of BUX have larger positive correlation than the stock prices of SAX. Moreover, the highest diversification benefits are realized when Portfolio SAX joins Portfolio BUX and the lowest diversification benefits are achieved when SAX joins WIG20.

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