How long the singular value decomposed entropy predicts the stock market? — Evidence from the Dow Jones Industrial Average Index

2016 ◽  
Vol 453 ◽  
pp. 150-161 ◽  
Author(s):  
Rongbao Gu ◽  
Yanmin Shao
Keyword(s):  
Stock Market ◽  
Singular Value ◽  
Dow Jones Industrial Average ◽  
Average Index

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

2022 ◽  
Author(s):  
Ignacio N Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Long Memory ◽  
Trading Volume ◽  
Moving Average ◽  
Single Step ◽  
Arma Process ◽  
Dow Jones Industrial Average ◽  
Fractional Models ◽  
Stock Market Trading

Abstract We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

scholarly journals PENGARUH KRISIS EKONOMI AMERIKA SERIKAT TERHADAP BURSA SAHAM DAN PERDAGANGAN INDONESIA

2013 ◽  
Vol 15 (3) ◽  
pp. 89-103
Author(s):  
Mita Nezky
Keyword(s):  
Stock Market ◽  
Income Tax ◽  
Stock Price ◽  
Value Added ◽  
Vector Autoregressive ◽  
Production Index ◽  
Svar Model ◽  
Dow Jones Industrial Average ◽  
The Us ◽  
The Impact

This paper analyzes the impact of the financial crisis in United States 2008 on Indonesia’s economy, by using Structural Vector Autoregressive (SVAR) model of 5 variables; Dow Jones Industrial Average, exchange rate, composite stock price index (IHSG), production index and trade tax income. The result shows that the US crisis affects the capital market in Indonesia where the Dow Jones Industrial Average plays greater role in explaining the IHSG, compared to Rupiah rate, production index and the trade income tax. In addition, the US crisis affects the volume and the trade income tax in Indonesia. These empirical results bring policy implication for Bappepam-LK as stock market regulator to intervene or to suspend the trade when the volatility exceeds the psychological threshold. It also emphasizes the necessity to diversify the export country destination and to increase the quality and the value added of Indonesian export.Keywords : US Crisis, stock market, trade, SVAR.JEL Classification : G18

scholarly journals Cointegration of Macroeconomics Variables and Dow Jones Industrial Average Index on the Composite Stock Price Index In 2015-2019

2021 ◽  
Vol 2 (3) ◽  
pp. 178-191
Author(s):  
Frisca Novia Sukmawati ◽  
Nadia Asandimitra Haryono
Keyword(s):  
Crude Oil ◽  
Interest Rates ◽  
Stock Price ◽  
Negative Relationship ◽  
Global Market ◽  
Short Term ◽  
Continuous Increase ◽  
Dow Jones Industrial Average ◽  
Cointegration Relationship ◽  
Average Index

This research examines the cointegration of macroeconomic variables and the Dow Jones Industrial Average Index toward IHSG. The Sampling data used is non probability sampling techniques by using historical monthly data from January 2015 to December 2019. The method used in this study are Augmented Dickey-Fuller Test for stationarity test, Johansen Test for Cointegration, and Error Correction Model for short-term relationships with eviews 10. The findings showed that DJIA Index not cointegrated with IHSG because investors are more responsive to global market and domestic sentiment. Exchange rates not cointegrated with the IHSG because exchange rate and IHSG movements do not always had a negative relationship. Interest rates are not cointegrated with IHSG because most of the sectors in the IDX affected by external sentiment than interest rates. Meanwhile, inflation have a cointegration relationship but does not have a short-term relationship with IHSG because inflation is generally known as a continuous increase in the price of goods as a whole. Crude oil have a cointegration relationship but does not have a short-term relationship with IHDG, which implies that an increase or decrease in crude oil in the short term can not affect IHSG.

scholarly journals Sunspots, GDP, and the Stock Market

2021 ◽  
Author(s):  
THEODORE MODIS
Keyword(s):  
Stock Market ◽  
Term Trend ◽  
Dow Jones Industrial Average ◽  
The Us ◽  
Long Term Trend ◽  
Future Horizon

A correlation has been observed between the US GDP and the number of sunspots as well as between the Dow Jones Industrial Average and the number of sunspots. The data cover 80 years of history. The observed correlations permit forecasts for the GDP and for the stock market in America with a future horizon of 10 years. Both being above their long-term trend they are forecasted to go over a peak around Jun-2008.

scholarly journals Impact of money supply on stock bubbles

2013 ◽  
Vol 61 (7) ◽  
pp. 2835-2842 ◽  
Author(s):  
Martin Širůček
Keyword(s):  
Money Supply ◽  
Empirical Analysis ◽  
Capital Market ◽  
Stock Index ◽  
Bubble Rise ◽  
Market Capitalization ◽  
Macroeconomic Factor ◽  
Dow Jones Industrial Average ◽  
The Us ◽  
Average Index

This article focuses on the effect and implications of changes in money supply in the US on stock bubble rise on the US capital market, which is represented by the Dow Jones Industrial Average index. This market was chosen according to the market capitalization. The attention of the paper is drawn to issues – if according to the results of empirical analysis, the money supply is a significant factor which causes the bubbles and if during the time the significance and impact of this macroeconomic factor on stock index increase.

Inter-relationship between performance of Bursa Malaysia and foreign stock markets

2006 ◽  
Vol 3 (1) ◽  
pp. 113
Author(s):  
T. Chantrathevi P. Thuraisingam ◽  
You Hoo Tew ◽  
Dalila Daud
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Asian Financial Crisis ◽  
Moderate Correlation ◽  
Crisis Period ◽  
Dow Jones Industrial Average ◽  
Daily Returns ◽  
Over Time

This paper explores the general perception that the Malaysian stock market is influenced by leading overseas stock markets. Employing correlation analysis comparison was made between the performance ofBiirsa Malaysia's Composite Index and six stock market indices namely Straits Times Index, Hang Seng Index, Nikkei 225 Stock Average, Australia All Ordinaries Index, Dow Jones Industrial Average Index and Financial Times 100 Index. This study also seeks to determine ifthere is any significant stability ofcorrelations over time. These indices were studied over a period offifteen years from I January 1990 to 31 December 2004, beginning with the cessation oftrading ofMalaysian shares on the Singapore stock exchange, which is synonymous with the pre-Asian financial crisis period, the crisis period and a post crisis period of almost five years. The study found that the, daily returns of the Composite Index over the period is positively co-related with the foreign indices indicating that the markets were moving in the same direction, in other words there is interdependency between the stock markets. However, the low to moderate correlation refutes the belief that the Malaysian stock market is influenced by the performance ofthe major stock markets. The study also found that generally the correlations are unstable over lime.    

Financial Crashes are “Outliers”

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Frequency Distribution ◽  
Stock Markets ◽  
Abnormal Returns ◽  
General Phenomenon ◽  
Dow Jones Industrial Average

This chapter provides evidence that large financial crashes are “outliers.” It first considers the limitation of standard analyses for characterizing how crashes are special before explaining what abnormal returns are. It then discusses the results of a study of the frequency distribution of drawdowns, or runs of successive losses, and shows that large financial crashes form a class of their own that can be seen from their statistical signatures. It also examines the expected distribution of “normal” drawdowns, along with the drawdown distributions of stock market indices such as the Dow Jones Industrial Average and the Nasdaq index. The chapter argues that the presence of outliers is a general phenomenon and concludes by describing how large drawdowns and crashes that result from a run of losses over several successive days affect the regulation of stock markets.

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