On Some Characteristics of Liquidity Proxy Time Series. Evidence from the Polish Stock Market

2018 ◽  
pp. 177-189 ◽  
Author(s):  
Joanna Olbrys ◽  
Michal Mursztyn
Keyword(s):  
Time Series ◽  
Stock Market

Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

Predicting US recessions with stock market illiquidity

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Cross Section ◽  
Market Liquidity ◽  
Credit Spreads ◽  
Stock Market Liquidity ◽  
Predictive Content ◽  
Dynamic Link

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

scholarly journals Fractional differencing in stock market price and online presence of global tourist corporations

2019 ◽  
Vol 24 (48) ◽  
pp. 194-204 ◽  
Author(s):  
Francisco Flores-Muñoz ◽  
Alberto Javier Báez-García ◽  
Josué Gutiérrez-Barroso
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Market Price ◽  
Google Trends ◽  
Data Sets ◽  
Market Prices ◽  
Content Type ◽  
Search Results ◽  
Online Presence ◽  
Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

scholarly journals Comparative Research on Influencing Factors of LSTM Deep Neural Network in Stock Market Time Series Prediction

2019 ◽  
Vol 4 (1) ◽  
pp. 84
Author(s):  
TANG Yin ◽  
YANG Jin Yu ◽  
CHEN Jian
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Influencing Factors ◽  
Network Structure ◽  
Time Series Prediction ◽  
Structure Optimization ◽  
Optimization Techniques ◽  
Training Process ◽  
Bear Market ◽  
Network Structure Optimization

<p><em>During training process of LSTM, the prediction accuracy is affected by a variation of factors, including the selection of training samples, the network structure, the optimization algorithm, and the stock market status. This paper tries to conduct a systematic research on several influencing factors of LSTM training in context of time series prediction. The experiment uses Shanghai and Shenzhen 300 constituent stocks from 2006 to 2017 as samples. The influencing factors of the study include indicator sampling, sample length, network structure, optimization method, and data of the bull and bear market, and this experiment compared the effects of PCA, dropout, and L2 regularization on predict accuracy and efficiency. Indice sampling, number of samples, network structure, optimization techniques, and PCA are found to be have their scope of application. Further, dropout and L2 regularization are found positive to improve the accuracy. The experiments cover most of the factors, however have to be compared by data overseas. This paper is of significance for feature and parameter selection in LSTM training process.</em></p>

scholarly journals Monetary Policy and Liquidity of Vietnam’s Stock Market

2016 ◽  
Vol 23 (02) ◽  
pp. 02-21
Author(s):  
Ly Tran Thi Hai
Keyword(s):  
Time Series ◽  
Monetary Policy ◽  
Stock Market ◽  
Money Supply ◽  
Industrial Production ◽  
Market Liquidity ◽  
Var Model ◽  
Market Returns ◽  
Liquidity Measures ◽  
The Impact

This study investigates the impact of monetary policy on liquidity of Vietnam’s stock market from September 2007 to November 2014. Time series of liquidity are determined by monthly liquidity data for 643 enterprises in the surveyed period. Two variables of the monetary policy, including growth in money supply and interbank rate, are employed in VAR model along with four different measures of market liquidity. The results show that unexpected variance in the two monetary policy variables has no significant impact on the market liquidity, which, in turn, may be improved by the positive shocks of market returns, inflation, and growth in industrial production. Market variance does produce certain effects, but discrepancies occur in the signs of various liquidity measures.

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