Query: impact on stock price Time Series Analysis

title: Impact of Market Forces on Stock Market in Pakistan: A Time Series Analysis
abstract: AUTHORS The key objective of the study is to investigate the impact of macroeconomic variables on the stock market in Pakistan. A stock market is a place or organization where securities instruments are traded among investors. Every type of risk earning assets such as bonds, equity, shares, securities that are traded is included in the financial market. The selected variables in this study were the Gross Domestic Product (GDP) Inflation, Interest Rate, Exchange rate, Cost of Capital, Investment, Money Supply, and Consumer Price Index. The GDP is taken as dependent variables and the impact of all remaining independent variables on GDP. The study used the time series data that covered the period from 1975 2016. This study implies the Augmented Dickey Fuller (ADF) test to check the stationary of data, and the Auto Regressive Distributive Lag Model for the Specification of data. To analyze the long run and short run association among variables the coefficients of the variables are measured. This study concluded that inflation, interest rate, investment, money supply harm the gross domestic product. The impact of the Exchange rate, Cost of capital, and the Consumer Price Index is positive. The cost of capital is the amount of money that is paid by using loans, funds, and instruments. Gulnaz Bhatti Student of M.Phil, Department of Economics, The Women University Multan, Pakistan Hina Ali Department of Economics, The Women University Multan, Pakistan,

title: Analysis of the impact of crude oil price fluctuations on China's stock market in different periods Based on time series network model
abstract: This paper studies the influence of Brent oil price fluctuations on the stock prices of China's two distinct blocks, namely, the petrochemical block and the electric equipment and new energy block, applying the Shannon entropy of information theory. The co movement trend of crude oil price and stock prices is divided into different fluctuation patterns with the coarse graining method. Then, the bivariate time series network model is established for the two blocks stock in five different periods. By joint analysis of the network oriented metrics, the key modes and underlying evolutionary mechanisms were identified. The results show that the both networks have different fluctuation characteristics in different periods. Their co movement patterns are clustered in some key modes and conversion intermediaries. The study not only reveals the lag effect of crude oil price fluctuations on the stock in Chinese industry blocks but also verifies the necessity of research on special periods, and suggests that the government should use different energy policies to stabilize market volatility in different periods. A new way is provided to study the unidirectional influence between multiple variables or complex time series.

title: The Impact Factors of Neural Network Based Time Series Prediction: Taking Stock Price as an Example
abstract: Compared with the traditional time series prediction model, neural network has obvious advantages for the analysis of nonlinear time series data. However, the topology structure and the training algorithm of neural network have a great influence on the prediction accuracy. Taking stock data as an instance, this paper analyzes the impacts factors of prediction ability of neural network such as topology structure, training algorithm and dataset. The experimental results show that the training algorithm and the size of dataset have significant influence on the performance of neural network.

title: Impact of Economic Growth, Foreign Direct Investment and Financial Development on Stock Prices in China: Empirical Evidence from Time Series Analysis
abstract: The study aims to empirically examine the dynamic relationship between gross domestic product (GDP) stock prices, Foreign direct investment (FDI) and domestic credit to the private sector for China by using the autoregressive distributed lag (ARDL) approach for the period 1999Q1:2015Q1. The study confirmed the long run cointegration among the variables. The empirical results revealed that stock prices and the associated regressors are in a long term equilibrium relationship; stock prices converge to the long run equilibrium position by 18.6% speed of adjustment via channel of GDP, stock price, FDI, and domestic credit to the private sector. The findings of the study further revealed that FDI has a positive impact on stock prices in the longrun, while financial development has a negative effect. The robustness of the ARDL bounds test of cointegration was examined by using Johansen and Juselius's (1990) maximum likelihood cointegration approach. Finally, the results of Granger causality under the framework of vector error correction models showed a unidirectional short run Granger causality that runs from stock prices to economic growth and from economic growth to FDI, specifying the absence of the FDI led growth hypothesis. Likewise, a bi directional causality has been found between financial development and stock prices.

title: Time Series Analysis on the Impact Level upon Taiwan Stock Market with Events of Signing International Agreements
abstract: Stock investment is one of the available conventional financial commodities in the investment market. The individual price of stocks including stocks in the field of industrial, medical, financial, overall, etc. Is subject to many different aspects. The relationship between aspects, the international economy, and the stock price is complex and is not easy to be categorized. Moreover, the trend of the world increases the cooperation and links between countries and different nations. Many agreements in tourism, trade, etc. Are signed between countries. In this paper, we focus on the effect on the stock prices caused by the agreements signed between Taiwan and other countries. The time series method is utilized to analyze the effect on the stock price forecasting caused by 5 of the incidents including WTO, Taiwan Nicaragua FTA, Taiwan Honduras FTA, and Taiwan Japan BIA. Both the pre event and the post event periods are included for observing the fluctuation behavior of the stock market price. The MAPE value is used as the criterion to verify the correctness of the forecasting result. The MAPE values obtained in all experiments are all less than 10% It implies that the time series method presents good forecasting ability in the analyzed cases.

title: Measuring the Impact of Financial News and Social Media on Stock Market Modeling Using Time Series Mining Techniques
abstract: In this work, we study the task of predicting the closing price of the following day of a stock, based on technical analysis, news articles and public opinions. The intuition of this study lies in the fact that technical analysis contains information about the event, but not the cause of the change, while data like news articles and public opinions may be interpreted as a cause. The paper uses time series analysis techniques such as Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) to study the existence of a relation between price data and textual information, either from news or social media. Pattern matching techniques from time series data are also incorporated, in order to experimentally validate potential correlations of price and textual information within given time periods. The ultimate goal is to create a forecasting model that exploits the previously discovered patterns in order to augment the forecasting accuracy. Results obtained from the experimental phase are promising. The performance of the classifier shows clear signs of improvement and robustness within the time periods where patterns between stock price and the textual information have been identified, compared to the periods where patterns did not exist.

title: Impact of Gold Price on Stock Market Return An Econometric Analysis of BSE and NSE
abstract: The present study examines the shock of domestic gold price on stock price indices in India for the period from January 1, 2008 to August 17, 2018 by using appropriate statistical and financial econometric techniques. The study is based on completely secondary data obtained from World Gold Council (WGC) database, BSE and NSE database and RBI database. In the process of analysis, Correlation statistics indicates the BSE Sensex and NSE Nifty are positively correlated with gold prices and multiple regression results is unauthentic. Further, unit root test indicates that the time series data are not stationary at levels and stationary at 1st difference. Granger causality test illustrate that no causality exists between Nifty and Gold price, Sensex and Gold price, Sensex and Nifty return, Nifty and Sensex and Bi directional causality exists between Gold return and Nifty and Gold_Price and Sensex return.

title: Analysis of Macroeconomic Variables Impact on Stock Market Price in Indonesia Stock Exchange (Idx) From Sector2 Period 2006 2016
abstract: One of the investment instruments available in this era is stock investment. Stock price movements can be closely linked to a country's macroeconomic conditions. There are many of research that has been conducted in terms of stock investment. However, investors are wiling to spend their funds in this type of investment, in order to attain greater return. This research aim to analyze the relationship between macroeconomic variables and stock market price from sector 2 in Indonesia Stock Exchange. This research is conducted to examine the simultan and partial influence between stock market price in and macroeconomic variables such as world oil price, interest rate, gross domestic product, sectoral stock price and money supply. Secondary data is the sources for this research, it will be based on quarterly data from January 2006 to December 2016. Time series regression is be the method that used to analyze the data. The results reveal that the independent variables simultaneously influence stock market price, and by partially, the results confirm that money supply and sectoral stock price are significantly influence the stock market price.

title: Empirical Analysis of the Impact of International Crude Oil Price Fluctuation on China's Stock Market
abstract: The price of oil, known as "black gold" has always attracted the attention of countries around the world. It is already a consensus that international oil prices affect economic operations, and naturally will affect the stock market which is regarded as a barometer of economic operations. The purpose of writing this article is to reveal the impact of international crude oil price fluctuations on China's stock market. In order to solve this problem, the author of this paper selected two sets of time series of Brent crude oil spot price and Shanghai Stock Index, and used Eviews software to perform various econometric tests such as cointegration test, Granger causality test, impulse response function, and decomposition of variance. It is found that there is a long term stable cointegration relationship between the international oil price and the Shanghai Stock Index, and that the international oil price is the Granger cause of the Shanghai Stock Index. Furthermore, the former has a positive dynamic impact on the latter as a whole. It reached its maximum by the middle of the third month, and then slowly decreased to stabilize. Compared with the results of other similar studies, this article draws more accurate conclusions thanks to the latest, more realistic data without much artificial processing. The conclusions of this paper can help stock investors and economic decision makers to make more effective decisions.

title: Sentiment Analysis for Stock Price Prediction
abstract: Stock prices and financial markets are often sentiment driven, which leads to research efforts to predict stock market trend using public sentiments expressed on social media such as Facebook and Twitter. In this project, we investigate the impact of sentiment expressed through StockTwits on stock price prediction. StockTwits is a relatively new microblogging website, which is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial markets. Specifically, we analyze the StockTwits tweet contents and extract financial sentiment using a set of text featurization and machine learning algorithms. The correlation between the aggregated daily sentiment and daily stock price movement is then studied. Finally, the sentiment information is used in addition to the past stock time series data to improve the accuracy of stock price movement prediction. The effectiveness of the proposed work on stock price prediction is demonstrated through experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) using nine month StockTwits data and daily stock data.