Dow Jones Industrial Average Index (DJIA)

2011 ◽  
Keyword(s):  
Dow Jones Industrial Average ◽  
Average Index

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 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.

scholarly journals THE ECONOMETRIC ANALYSIS OF THE DYNAMICS OF ETHEREUM IN THE SHORT-TERM PERIOD

2018 ◽  
Author(s):  
Олег Кудрявцев ◽  
Oleg Kudryavtsev ◽  
Кирилл Мозолев ◽  
Kirill Mozolev ◽  
Артур Чивчян ◽  
...  
Keyword(s):  
Composite Index ◽  
Econometric Analysis ◽  
Short Term ◽  
Price Changes ◽  
Nasdaq Composite Index ◽  
Dow Jones Industrial Average ◽  
Survey Results ◽  
Index Changes ◽  
Gold Futures ◽  
Average Index

The article presents an econometric analysis of the effect of stock indicators, such as Comex Gold futures, Dow Jones Industrial Average index and NASDAQ Composite, on the Ethereum cryptocurrency dynamics in the 100-day period. As part of the study, an econometric model of the dynamics of e-currency was built. The survey results show that when the Comex gold futures price changes by 1% on average, the Ethereum price changes by 5.01% in the same direction, when the Dow Jones Industrial Average index changes by 1%, the Ethereum price is 10.897%, and when the NASDAQ Composite index changes, the Ethereum price will change in the opposite direction to 3.59%

AN ENTROPY BASED IN WAVELET LEADERS TO QUANTIFY THE LOCAL REGULARITY OF A SIGNAL AND ITS APPLICATION TO ANALIZE THE DOW JONES INDEX

2012 ◽  
Vol 10 (05) ◽  
pp. 1250048 ◽  
Author(s):  
MARIEL ROSENBLATT ◽  
EDUARDO SERRANO ◽  
ALEJANDRA FIGLIOLA
Keyword(s):  
Financial Analysis ◽  
Financial Data ◽  
Local Regularity ◽  
Data Series ◽  
Hölder Exponent ◽  
Jones Index ◽  
Dow Jones Industrial Average ◽  
Wavelet Leaders ◽  
Pointwise Hölder Exponent ◽  
Average Index

Local regularity analysis is useful in many fields, such as financial analysis, fluid mechanics, PDE theory, signal and image processing. Different quantifiers have been proposed to measure the local regularity of a function. In this paper we present a new quantifier of local regularity of a signal: the pointwise wavelet leaders entropy. We define this new measure of regularity by combining the concept of entropy, coming from the information theory and statistical mechanics, with the wavelet leaders coefficients. Also we establish its inverse relation with one of the well-known regularity exponents, the pointwise Hölder exponent. Finally, we apply this methodology to the financial data series of the Dow Jones Industrial Average Index, registered in the period 1928–2011, in order to compare the temporal evolution of the pointwise Hölder exponent and the pointwise wavelet leaders entropy. The analysis reveals that temporal variation of these quantifiers reflects the evolution of the Dow Jones Industrial Average Index and identifies historical crisis events. We propose a new approach to analyze the local regularity variation of a signal and we apply this procedure to a financial data series, attempting to make a contribution to understand the dynamics of financial markets.

scholarly journals The Dogs of the Dow Theory – Is It Valid?

2019 ◽  
Vol 11 (5) ◽  
pp. 43
Author(s):  
Doh-Khul Kim
Keyword(s):  
Financial Market ◽  
Investment Strategy ◽  
Trading Costs ◽  
Dow Jones Industrial Average ◽  
Average Index

The Dogs of the Dow theory has been a popular tool in the financial market. But while the theory is simple, there have been mixed findings on its validity. Using U.S. data from 2000 through 2017, this paper identifies how consistently an investment strategy that follows the Dogs of the Dow theory outperforms the average market. The results show that the theory has not worked well in the recent U.S. market when trading costs and taxes are included. Rather, holding an equally weighted investment of all firms is more likely to outperform the Dow Jones Industrial Average index and the Dogs of the Dow strategy in the long term.

scholarly journals The asymmetric responses of stock prices in US market

2021 ◽  
Vol 36 ◽  
pp. 01013
Author(s):  
Woan Lin Beh ◽  
Wen Khang Yew
Keyword(s):  
Interest Rates ◽  
Stock Prices ◽  
Price Index ◽  
Consumer Price Index ◽  
Composite Index ◽  
The United States ◽  
Macroeconomic Factors ◽  
Dow Jones Industrial Average ◽  
Short Run ◽  
Average Index

Machine learning and data analytics are so popular in making trading much more efficient by helping the investors to identify opportunities and reduce trading costs. Before applying suitable predictive modelling algorithms, it is crucial for investors or policymaker to understand the nature of the stock data properly. This paper investigates the dependency of macroeconomic factors against the stock markets in the United States using the nonlinear Autoregressive Distributed Lag (NARDL) approach. The analysis considered the Dow Jones Industrial Average Index, NASDAQ Composite Index, and S&P 500 Index. Macroeconomic factors in this country such as consumer price index, export, interest rates, money supply, real effective exchange rates, total reserves, and gold price are considered in this study. In the findings, the NARDL approach shows that the Dow Jones Industrial Average Index and S&P500 Index are having bi-directional positive asymmetric effects to each other in the short run. In short-run, increasing the consumer price index is found to have a negative effect on Dow Jones Industrial Average Index but with a positive effect on S&P500 Index. In conclusion, this study aids investors and other market participants in making a more efficient investment decision.

scholarly journals Programy automatycznej reinwestycji dywidendy jako instrument polityki wypłaty dywidendy w spółce akcyjnej

2020 ◽  
Vol 54 (4) ◽  
pp. 55
Author(s):  
Andrzej Kuciński
Keyword(s):  
Dow Jones Industrial Average ◽  
Average Index

<p><strong>Uzasadnienie teoretyczne</strong>: Zagadnienie programów automatycznej reinwestycji dywidendy jest rzadko poruszane w polskiej literaturze, brak jest całościowych opracowań dotyczących programów automatycznej reinwestycji dywidendy. Zagadnienie to najczęściej pojawia się przy omawianiu narzędzi polityki dywidendy.</p><p><strong>Cel artykułu</strong>: Przedstawienie założeń programów (planów) automatycznej reinwestycji dywidendy, które mogą stanowić narzędzie polityki wypłaty dywidendy w spółce akcyjnej, a także dokonanie analizy programów automatycznej reinwestycji dywidendy oferowanych przez 30 amerykańskich spółek reprezentujących Dow Jones Industrial Average Index.</p><p><strong>Metody badawcze</strong>: W opracowaniu wykorzystano jako metody badawcze analizę literatury oraz analizę dokumentów (broszur) opisujących programy automatycznej reinwestycji dywidendy.</p><p><strong>Główne wnioski</strong>: Programy automatycznej reinwestycji dywidendy zostały wdrożone przez wszystkie spółki wchodzące w skład Dow Jones Industrial Average Index. Znaczna część analizowanych programów zalicza się do grupy tzw. bezpłatnych programów reinwestycji dywidendy (<em>No-Fee Dividend Reinvestment Programs</em>). Z dokonanych obserwacji wynika, że często programy automatycznej reinwestycji dywidendy uzupełniane są o dodatkowe programy zakupu akcji, określane jako <em>Optional Cash Purchase Plans</em> (OCPs) lub <em>Stock Purchase Plans</em> (SPPs).</p>

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