A Time Series Analysis of the IT Stock Market during the 2007 – 2009 Recession

2018 ◽  
Author(s):  
Shilpa Balan ◽  
Tejas Agara Chandrakumar ◽  
Sohong Chakraborty
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Series Analysis

scholarly journals A Time Series Analysis of the Nexus Between Macroeconomic Fundamentals and Stock Prices in Nigeria

2018 ◽  
Vol 13 (2) ◽  
pp. 69-91
Author(s):  
Amassoma Ditimi ◽  
Bolarinwa Ifeoluwa
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Macroeconomic Factors ◽  
Series Analysis ◽  
Integration Test ◽  
Macroeconomic Fundamentals ◽  
The Government

AbstractSince macroeconomic fundamentals have been found to play a vital role for changes in the economy of a country. Consequently, the onus is on the appropriate regulatory authorities to take measures in making amendments in these policies to put the economy on the right development track. The aim of this study is to use time series analysis to empirically showcase the nexus between macroeconomic fundamentals and stock prices in Nigeria. The method used for this study was the Co-integration test and the EGARCH technique to estimate the possible influence of the selected macroeconomic fundamentals on stock prices. Volatility was captured by using quarterly data and estimated using GARCH (1,1) respectively. The study found there is a positive relationship between macroeconomic factors and stock prices in Nigeria. Therefore, the study recommends that the Federal authority should put in place policy measures that will enable the exchange rate to be relatively stabilized. This is because empirical evidence from studies has shown that exchange rate affects stock market prices. In addition, the government authority should ensure an enabling environment that would build the mindset of institutional investors in the Nigerian stock market due to the existence of information asymmetry problems among potential investors.

Stock market analysis and prediction using time series analysis

2021 ◽  
Author(s):  
J. Christy Jackson ◽  
J. Prassanna ◽  
Md. Abdul Quadir ◽  
V. Sivakumar
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Market Analysis ◽  
Series Analysis

scholarly journals Peramalan Data IHSG Menggunakan Fuzzy Time Series

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Real Time ◽  
Soft Computing ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Time Data ◽  
Series Analysis

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHSG

Effects of Intraday Patterns on a Time Series Analysis of the Korean Stock Market Index

2011 ◽  
Vol 58 (2) ◽  
pp. 396-399 ◽  
Author(s):  
Doo Hwan Kim ◽  
Seong Eun Maeng ◽  
Yu Sik Bang ◽  
Hyung Wooc Choi ◽  
Moon-Yong Cha ◽  
...  
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Market Index ◽  
Series Analysis ◽  
Market Index ◽  
Korean Stock Market ◽  
Intraday Patterns
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