Search results

The purpose of this study is to explain the mixed results to changes in the DJIA index documented in the literature. The authors show that economic cycles, especially recessionary periods, explain the difference in findings.

The authors examine changes in the Dow Jones Industrial Average (DJIA) from 1929 to 2019 to evaluate immediate and long-term market reactions after a component change. Using multiple event-study methodologies, the authors examine the full era, the pre- and post-exchange traded fund (ETF) windows and economic cycles using both pre and post-estimation windows.

In aggregate, DJIA additions do not present an increase in wealth; however, wealth effects are positive during expansions and negative during recessions. Deletions have a negative wealth effect. The authors find weak evidence of an indexing effect. Additions are positive post-1998, and deletions remain negative regardless of era. In the long run, firms added to the DJIA have positive abnormal returns in the second year after inclusion. Deletions in recessionary times have negative returns three years after removal, a signal of longer-term wealth decline for these firms.

The DJIA changes periodically to better represent industries relevant to the blue-chip market, and the findings have implications for fund managers and active investors.

The DJIA changes periodically to better represent industries relevant to the blue-chip market, and the findings have implications for fund managers and active investors.

Prior literature presents limited time series of data points and mixed results and implications. The authors find that the economic cycle is a driving factor that supports predicted signs and amounts of wealth change. Furthermore, the authors see limited ETF impact on DJIA changes and some impact of the choice of estimation period.

The purpose of this paper is to examine changes in stock returns, liquidity, institutional ownership, analyst following and investor awareness for companies added to and deleted from the Dow Jones Industrial Average (DJIA) index. Previous studies report conflicting evidence regarding the market reactions to changes in the DJIA index membership.

This study uses the event-study methodology to calculate abnormal returns and trading volume around the announcement and effective days of DJIA index changes from 1929 to 2015. It also tests for significant changes in liquidity, institutional ownership, analyst following and investor awareness in the 1990–2015 period. Multivariate regressions are used to perform a simultaneous analysis of competing hypotheses.

This study resolves the mixed results of previous DJIA index papers by documenting different stock price and trading volume reactions over the 1929–2015 period. Focusing on the most recent period, 1990–2015, the study finds that stocks added to (deleted from) the index experience a significant permanent stock price gain (loss). The observed stock price reaction seems to be associated with changes in liquidity proxies thus lending support for the liquidity hypothesis.

Limited data availability for the periods prior to 1990 prevents this study from identifying the exact reasons for different stock price and trading volume reactions across subperiods of the 1929–2015 period.

This study provides the most comprehensive examination of market reactions to changes in the DJIA index and resolves the mixed results of previous studies. A better understanding of market reactions around the DJIA index changes can help both individual and institutional investors with developing effective trading strategies and index managing companies with designing optimal announcement policies.

Purpose: Through globalization, financial markets have become more integrated and their tendency to act together has increased. The majority of the literature states that there is a cointegration between developed and emerging markets. How do positive or negative shocks in developed markets affect emerging markets? And how do positive or negative shocks in emerging markets affect developed markets? For this reason, the aim of the study is to investigate the asymmetric causality relationship between developed and emerging markets with Hatemi-J asymmetric causality test.

Design/methodology/approach: In this study, the Dow Jones Industrial Average (DJIA) index was used to represent developed markets and the Morgan Stanley Capital International (MSCI) Emerging Market Index was used to represent emerging markets. The asymmetric causality relationship between the DJIA Index and the MSCI Emerging Market Index was investigated using monthly data between January 2009 and April 2019. In the first step of the study, the Johansen Cointegration Test was used to determine whether there is a cointegration between the markets. In the next step, the Hatemi-J asymmetric causality test was applied to see the asymmetric causality relationship between the markets.

Findings: There is a weak correlation between developed and emerging markets. This result is important for international investors who want to diversify their portfolios. As a result of the Johansen Cointegration Test, it was found that there is a long-term relationship between the MSCI Emerging Market Index and the DJIA Index. Therefore, investors who make long-term investment plans should not forget that these markets act together and take into account the causal relationship between them. According to the asymmetric causality test results, a unidirectional causality relationship from the MSCI Emerging Market Index to the DJIA Index was determined. This causality shows that negative shocks in the MSCI Emerging Market Index have positive effects on the DJIA Index.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the asymmetrical relationship between developed and emerging markets. This study is also useful in predicting the short- and long-term relationship between markets. In addition, this study helps investors, portfolio managers, company managers, policymakers, etc., to understand the integration of financial markets.

Introduction – Emerging markets are under the influence of many external factors in global market conditions. International interest rates and price fluctuations in major stock market indices are also among these factors. The FED policies shape the international capital movements in particular, which significantly affects the emerging markets. For this reason, emerging stock markets may show different reactions especially in times of crisis.

Purpose – The purpose of this study is to investigate whether the BIST30 index acted in accordance with the overreaction hypothesis (ORH) against the return changes in the Dow Jones Industrial Average (DJIA) index in the process of the 2008 global financial crisis.

Methodology – The data set of the study was analysed by dividing it into two periods. The first period is the monetary expansion period between 17 August 2007, when the Federal Reserve (FED) reduced the interest rate for the first time, until 22 May 2013 when the FED announced that it would reduce the bond purchases. The second period is the monetary contraction period including the dates between 23 May 2013 and 1 June 2017. An error correction model (ECM) was established in both periods for the indices, determined as cointegrated. The validity of the ORH was tested by Cumulative Abnormal Return (CAR) Analysis.

Findings – According to the ECM, the authors identified that the effect of short-term changes in the DJIA return in the monetary expansion period on BIST30 index return was higher than that in the monetary contraction period. However, according to the findings obtained from the CAR analysis results, the BIST30 index did not generally act in accordance with the ORH against the DJIA. Findings can be appreciated as a decision-making tool especially for investment specialists and investors interested in securities investments.

This study examines the dynamic linkages between the Indian Nifty index futures traded on the offshore Singapore Exchange (SGX) and US stock indices (DJIA, NASDAQ and S&P 500) under the closure of the spot market for Nifty futures.

With high-frequency 5-min overlapping price data, the authors employ the Johansen cointegration test to investigate long-run relationships, the Granger causality test to assess short-run dynamics and the BEKK-GARCH model for volatility spillover investigation.

The empirical findings reveal that the SGX Nifty futures market is cointegrated with the US DJIA market. The US DJIA stock index strongly influences the price discovery of SGX Nifty futures and past innovations in the US markets strongly impact the current volatility of SGX Nifty futures.

Findings from this study have significant implications for market participants, particularly foreign investors and portfolio managers. These findings might be helpful for market participants to improve the prediction power of expected SGX Nifty futures price and volatility, especially under the closure of the spot market. Also, SGX market participants can take the significant role of the US market into account when formulating hedging and trading strategies with Indian Nifty futures. Besides, our findings have significant implications for policymakers in evaluating market stability.

This article adds to the very limited research on offshore or international stock index futures; it is the first study that empirically examines the international linkages of offshore SGX Nifty futures under the closure of its underlying spot market and also the driving force behind the linkages.

The purpose of this paper is to examine the relation between extreme return correlation and return volatility, in the context of US stock indexes, by detecting clusters of extreme returns using return and volatility thresholds based on an algorithm suggested in Laurini.

The daily returns and conditional volatilities estimated using GARCH (1, 1) serve as inputs to the two threshold algorithm that detects extreme return clusters. The analysis of the relation between correlation and volatility is then based on the extent of overlapping extreme return clusters across DJIA, S&P 500 and NASDAQ composite.

It is found that the correlation positive extreme returns within overlapping clusters significantly increases with volatility between DJIA and S&P 500. The authors did not find any significant change in the pair‐wise correlation between the positive extreme returns within overlapping clusters in each of these indexes with those of NASDAQ composite.

Prior researches examine extreme returns by using a return threshold and have found mixed results on the relation between correlation and volatility. This paper examines the relation between correlation and volatility between clusters of extreme returns and provides consistent results that are of vital interest to investors.

The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by VIX, the “fear gauge index” of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty (VIX) as the mixing variable. In contrast to studies on the effects of VIX on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance.

GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VIX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period.

Adding the “fear gauge” VIX index and a dummy variable for recessions to the variance equation in GARCH-M models, the VIX coefficient always increases variance and the recession dummy has mixed effects. Overall, VIX acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VIX is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks.

In theory, the effects of VIX on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VIX effects on individual stock variance, controlling for business cycles.

Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of VIX. Second, recent research emphasizes the formal predictive power of VIX in US stock markets. For the variance equation, existing works report positive values for the VIX-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.

Does the method of divisor adjustment used for stocksplits in the Dow Jones Industrial Average (DJIA) cause a downward bias in the average’s level and does this method of adjustment cause increased volatility in the average? To investigate these issues, two averages are created using DJIA stocks. One average is adjusted for stock splits through adjustment in the divisor. This method is identical to the DJIA method of adjustment.The other average makes adjustment for stock splits by adjusting the stock value in the numerator. Relative to these two methods of adjustment for stock splits, there sults of the study demonstrate that there is no downward bias of the DJIA. Additionally, it is found that the method of divisor adjustment for stock splits does not increase the volatility of the average. When compared to the Standard and Poor’s Industrial Index, the DJIA does show downward bias.

The purpose of this paper is to examine the relationship between the US and China stock markets between 2000 and 2007. This study attempts to categorize the event on February 27, 2007, i.e. 9 per cent plunge in Shanghai stock market followed by the $1.5 trillion global market shake out, as irrational, i.e. herd mentality.

To test for this relationship, the Morgan Stanley Capital International daily price index data was collected from April 15, 2002 to April 12, 2007. Daily Dow Jones Industrial Average (DJIA), Nikkei 225 (Nikkei), Hang Seng Index, and the Shanghai Stock Exchange Composite Index (SSECI) were collected from finance.yahoo.com from January 1, 2000 until April 3, 2007. The running beta and correlation coefficients, defined as the cumulative coefficients, are used to determine the co‐movement of the SSECI and DJIA.

The strength of the relationship between the US and China stock markets has significantly increased since 2005, maybe attributed to China's policy change in 2005 to move toward a more free market economy. Because of the unique characteristics of China's stock market, it is hard to conclude that the $1.5 trillion global market shake out was ignited by the 9 per cent plunge in the Shanghai stock market on February 27, 2007.

China's economic reform is unique since the country followed no blue print for the economic institutions to model after and policies were adopted through experimentation. Fueled by its fast growing economy (10.4 per cent in 2005 and 10.7 per cent in 2006), using past patterns or trends to predict the future of China's financial market requires further research as its stock market emerges. Research in this area requires more observations as China's stock market grows and becomes more transparent.

Results here suggest that the strength of the relationship between the US and China stock markets has significantly increased since 2005 and that China's 2005 policy moves toward a more free market economy are most likely responsible.

A better understanding of the influence of China's emerging stock market on the global stock market offers significant value to portfolio managers worldwide.

The present work endeavors to explore the potential nonlinear and asymmetric effects of supply fundamental properties of Bitcoin mining process (velocity, size and stock of Bitcoins, cost of production and mining revenue), DJIA, VIX, economic policy uncertainty and Google Trend on the price of Bitcoin (PB).

The authors apply the Nonlinear Autoregressive Distributed lag (NARDL) approach for the period from November 31, 2013 to December 30, 2020.

The asymmetric effects of inflation, the size of Bitcoin economy, reveal a positive impact on the PB in the short and long run. In the short run, Bitcoin price shows negative statistically significant sensitivity to positive (negative) changes in DJIA (VIX) index. In addition, Google Trends have an impact on Bitcoin prices indicating that the Bitcoin market is also driven by investors' sentiments. In the long run, negative policy uncertainty shocks increase the PB while in the short run, negative shocks decrease it.

The authors give credence to the best ways of understanding the existence of asymmetries in the link between the PB and a number of influential macro-finance variables to improve the appropriate asset allocation and portfolio management.