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Dow Jones Industrial Average – DJIA (or the Dow) Definition
It is the price-weighted average of 30 actively-traded stocks of significant publicly owned US industrial corporations listed on the New York Stock Exchange. The trend in the movement of these stocks market value reflects the trend of the entire US stock market.
A Little More on What is the Dow Jones Industrial Average
Dow Jones Industrial Average also known as ‘the Dow’ was introduced by Charles Dow in 1896. It is named after Charles Dow and his business partner Edward Jones. It is one of the oldest single most watched stock market indexes in the world. Initially, there were only 12 corporations included in this index. The aim of devising this was to serve as a proxy for the entire US market. All the 12 corporations included in the list were purely industrial in nature. General Electric is the only company which held its position on the list until recently since its beginning. The General Electric was replaced by Walgreens Boots Alliance, Inc. on June 26, 2018.
The companies listed are considered to be the representatives of the US economy and the composition of the list changes as and when a company faces financial distress and loses the representative status. The other companies included on the list are Walt Disney Company, Microsoft Corporation, Exxon Mobil Corporation, Apple Inc, The American Express, JPMorgan Chase and co. etc.
As it is a price-weighted index, the stocks with higher share prices are given more weight in the index. Initially, it was calculated by adding up the prices of the 12 listed stocks and dividing it by 12. The index went through additions and subtractions over the time, new companies entered, some failed to hold their representative status thus was removed from the list. On the occasions of merger and stock splits, the divisor is adjusted to prevent any distortion in index’s value.
In 1928 the number of its components was increased from 12 to 30 and till now the same number is maintained.
References for the Dow Jones Industrial Average
Academic Research on Down Jones Industrial Average
- ● Low q-moment multifractal analysis of Gold price, Dow Jones Industrial Average and BGL-USD exchange rate, Ivanova, K., & Ausloos, M. (1999). The European Physical Journal B-Condensed Matter and Complex Systems, 8(4), 665-669. The objective of this research is to show that the transactions of Gold price, Dow Jones Industrial average and BGL-USD exchange, are not fractal but multifractal by analysis of their low q-moment values over a span of 6 ½ years.
- ● Information costs and liquidity effects from changes in the Dow Jones Industrial Average list, Beneish, M. D., & Gardner, J. C. (1995). Journal of Financial and Quantitative Analysis, 30(1), 135-157. This research examines the stock market change effect of Dow Jones Industrial Average as opposed to S&P 500. Its aim is to show that DIJA listings are consistent with the information cost/liquidity explanation.
- ● Price barriers in the Dow Jones industrial average, Donaldson, R. G., & Kim, H. Y. (1993). Journal of Financial and Quantitative Analysis, 28(3), 313-330. The objective of this research is to show that the rise and fall of DJIA prices is as a result of its support and resistance level at multiples of 100. Results however show that at this value, there are more factors involved.
- ● The introduction of derivatives on the Dow Jones Industrial Average and their impact on the volatility of component stocks, Rahman, S. (2001). Journal of Futures Markets: Futures, Options, and Other Derivative Products, 21(7), 633-653. This research examines the impact of trading in the Dow Jones Industrial Average (DJIA) index futures and futures options on the conditional volatility of component stocks. Its aim is to show that the introduction of futures and futures option will not affect volatility of component stocks.
- ● Dividends, nonsynchronous prices, and the returns from trading the Dow Jones Industrial Average, Day, T. E., & Wang, P. (2002). Journal of empirical finance, 9(4), 431-454. This research explores nonsynchronous prices and returns from trading DJIA stocks by use of different experiments.
- ● Are there psychological barriers in the Dow–Jones index?, Ley, E., & Varian, H. R. (1994). Applied Financial Economics, 4(3), 217-224. This research examines the usefulness of past closing values of Dow Jones Index in predicting future market returns.
- ● Stock prices and Wall Street weather, Saunders, E. M. (1993). The American Economic Review, 83(5), 1337-1345. This research explores the relationship between local weather and stock prices exchange in New York City.
- ● Price discovery and volatility spillovers in the DJIA index and futures markets, Tse, Y. (1999). Journal of Futures markets, 19(8), 911-930. This article examines the minute‐by‐minute price discovery process and volatility spillovers between the DJIA index and the index futures recently launched by the CBOT. The objective of this research is to show that the innovations in one market can predict that of another.
- ● Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013, Charles, A., & Darné, O. (2014). Journal of Banking & Finance, 43, 188-199. The objective of this research is to show the events that caused the large shock in volatility of the DJIA index by means of applied tests.
- ● Intraday trading volume and return volatility of the DJIA stocks: A note, Darrat, A. F., Rahman, S., & Zhong, M. (2003). Journal of Banking & Finance, 27(10), 2035-2043. This article explores the contemporaneous correlation between volume and volatility of DJIA stocks by use of a 5-minute intraday data.
- ● Assessing market microstructure effects via realized volatility measures with an application to the dow jones industrial average stocks, Awartani, B., Corradi, V., & Distaso, W. (2009). Journal of Business & Economic Statistics, 27(2), 251-265. This article aims to show that market microstructure effects affect prices of financial assets, where the Dow Jones Industrial Average stocks is used as a case study.
- ● New findings regarding day‐of‐the‐week returns over trading and non‐trading periods: a note, Rogalski, R. J. (1984). The Journal of Finance, 39(5), 1603-1614. This paper examines daily ‘close to close’ returns into trading and non-trading day returns. The results show that the average returns for trading days are identical for all days of a week, and that all average negative returns usually occur on non-trading days.