Dow Jones Industrial Average Index Research Articles

Market reactions to changes in the Dow Jones industrial average index

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value 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.

The Impact of Financial and Macroeconomic Shocks on the Entropy of Financial Markets.

We propose here a method to analyze whether financial and macroeconomic shocks influence the entropy of financial networks. We derive a measure of entropy using the correlation matrix of the stock market components of the DOW Jones Industrial Average (DJIA) index. Using VAR models in different specifications, we show that shocks in production or the DJIA index lead to an increase in the entropy of the financial markets.

Performance Characteristics of Hedge Fund Indices

The focus of the study is to provide a detailed account of how various strategy-specific or composite hedge fund indices have performed in the past. The study analyses returns (monthly) of different hedge funds. The study selected popular categories of Hedge fund index against DJIA (Dow Jones Industrial Average) and Credit Suisse Hedge Fund Index. The Data is taken from January 1994 to December 2018 and is taken from Credit Suisse Hedge fund database. The study finds that most hedge fund indices witness a drop in returns over time, and most hedge funds do not provide additional diversification benefits with respect to traditional asset class. On a risk-adjusted basis, majority of hedge fund indices out-perform the broader equity market, risk-adjusted performances of various hedge fund strategies, does not change drastically with use of different risk-adjusted measures. As opposed to prevalent studies like Atil, Bali and Demirtas [1] [2], indicating Equity market neutral hedge fund index as the best performer in terms of risk-adjusted returns, the current study finds Event Driven Distressed hedge fund Index as the best performer.

A least squares-based parallel hybridization of statistical and intelligent models for time series forecasting

Abstract Combining different models is one of the most well established strategies in the literature in order to capture different patterns of the data, lifting limitations of single models, and improve forecasting accuracy. In recent decades, various hybrid models have been developed by combining different models such as linear/nonlinear, supervised/unsupervised fuzzy/crisp, and statistical/intelligent together. Intelligent and statistical techniques due to their unique advantages are one of the most popular and the most widely-used candidates to build hybrid models for time series forecasting. These models are generally combined together in two different schemas: series and parallel. In series schema, components are sequentially used for modeling the underlying data; while in parallel schema, components are separately used and then their results are combined together. In this paper, a new parallel hybrid model is proposed, incorporating autoregressive integrated moving average (ARIMA) and multilayer perceptrons (MLPs), which are one of the most important intelligent and statistical time series models. In addition, a least squares process is used in order to find the optimum weights of each component. The main idea of the proposed model is to employ special features of ARIMA and MLP models in modeling linear and nonlinear patterns in the data simultaneously and to yield the best performance by reasonable computational costs. To evaluate the effectiveness of the least squares based proposed hybrid model (LPHM) in financial forecasting, three well-known benchmark data sets including the closing of Standard & Poor’s 500 index (S&P500), the closing of the Shenzhen Integrated Index (SZII), and the opening of Dow Jones Industrial Average Index (DJIAI) are elected. Empirical results indicate that LPHM outperforms its constituent as well as genetic algorithm based hybrid model (GAHM) and simple average hybrid model (SAHM) in stock price forecasting. While, the computational cost of the proposed model is significantly lower than the GAHM and roughly equal to the SAHM.

Modeling and Forecasting Financial Volatilities Using a Joint Model for Range and Realized Volatility

There exist many ways to measure financial asset volatility. In this paper, we introduce a new joint model for the high-low range of assets prices and realized measure of volatility: Realized CARR. In fact, the high-low range and realized volatility, both are efficient estimators of volatility. Hence, this new joint model can be viewed as a model of volatility. The model is similar to the Realized GARCH model of Hansen et al. (2012), and it can be estimated by the quasi-maximum likelihood method. Out-of-sample volatility forecasting using Standard and Poors 500 stock index (S&P), Dow Jones Industrial Average index (DJI) and National Association of Securities Dealers Automated Quotation (NASDAQ) 100 equity index shows that the Realized CARR model does outperform the Realized GARCH model.

Temporal Association Rule Mining: With Application to US Stock Market

A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks (stock price time series, in USD) of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with time-parameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance. After which, multi-period portfolio optimization is done to demonstrate the practicality of using the rules in the real world.

State Dependence of Aggregated Risk Aversion: Evidence for the German Stock Market

We propose a dynamic generalization of the Capital Asset Pricing Model (CAPM) that allows for a time-varying market price of risk (MPR) reflecting both cross market dependence and future investment opportunities. The realized volatility approach is employed to determine market risk. The advocated state space model takes autoregressive dynamics of the MPR and predetermined state variables into account. For the case of the DAX, the major German stock index, the empirical analysis strongly underpins time variation of risk compensation. The MPR is conditioned upon the EURIBOR, a national and an international term spread, returns of the Dow-Jones-Industrial-Average-Index (DOW), and a dummy variable hinting at excess activity of noise traders. Moreover, we document forecasting results based on a short horizon trading strategy. The proposed model is characterized by strong market timing ability.

On the index tracking and the statistical arbitrage choosing the stocks by means of cointegration: the role of stock picking

In this paper, we propose a new methology for Index Tracking (IT) by means of cointegration which provides some significant improvements on that field. As the quality of the tracking portfolio (TP) depends highly on the stock selection procedure, we propose picking the stocks using a model selection technique based on optimizing the cointegration level of the TP and the benchmark index instead of selecting, as in previous papers the assets by ad hoc decisions. To illustrate an empirical application of these techniques we use daily closing prices in the Dow Jones Industrial Average (DJIA) index over two different periods; one period which goes from 1 January 1990 to 31 December 2001 previously used by other authors, and the bear and a turmoil period, which goes from January 2007 to May 2012, inside the current financial crisis. Using only five assets we are able to successfully track the DJIA index and our results improve the IT technique based on cointegration that chooses stocks with maximum capitalization level. We also have compared our results with a more traditional procedure based on correlation and again our results reveal superiority. The empirical illustration not only has been focused on the TP itself, but has also been extended to tracking the index with an added profitability of 5, 10, 15 or 20% and to long-short strategies, producing profitable results.

Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions

This study performs the challenging task of examining the forecastability behavior of the stock market returns for the Dow Jones Islamic Market (DJIM) and the Dow Jones Industrial Average (DJIA) indices, using non-parametric regressions. These indices represent different markets in terms of their institutional and balance sheet characteristics. The empirical results posit that stock market indices are generally difficult to predict accurately. However, our results reveal some point forecasting capacity for a 15-week horizon at the 95 per cent confidence level for the DJIA index, and for nine-week horizon at the 99 per cent confidence for the DJIM index, using the non-parametric regressions. On the other hand, the ratio of the correctly predicted signs (the success ratio) shows a percentage above 60 per cent for both indices which is evidence of predictability for those indices. This predictability is however statistically significant only four-weeks ahead for the DJIM case, and twelve weeks ahead for the DJIA as their respective success ratios differ significantly from the 50 percent, the expected percentage for an unpredictable time series. In sum, it seems that the forecastability of DJIM is slightly better than that of DJIA. This result on the forecastability of DJIM adds to its other findings in the literature that cast doubts on its suitability in hedging and asset allocation in portfolios that contain conventional stocks.

Is the DJIA Index Biased?

Since 1997, the Dow Jones Industrial Average (DJIA) Index has replaced companies in the index on six occasions. Overall, 16 companies have been dropped from the index. The first four replacements involved dropping and then replacing companies that still existed. These four discretionary substitutions may potentially have biased the Dow. The most recent changes were made because of terminal events; not replacing these companies would have left the Dow with fewer than 30 companies. <b>TOPICS:</b>Mutual funds/passive investing/indexing, financial crises and financial market history

Quantifying the Behavior of Stock Correlations Under Market Stress

Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.

Stock index forecasting based on a hybrid model

Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network (BPNN) can be used to make forecasts based on time series. In this paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the proposed hybrid model (PHM) is determined by genetic algorithm (GA). The closing of the Shenzhen Integrated Index (SZII) and opening of the Dow Jones Industrial Average Index (DJIAI) are used as illustrative examples to evaluate the performances of the PHM. Numerical results show that the proposed model outperforms all traditional models, including ESM, ARIMA, BPNN, the equal weight hybrid model (EWH), and the random walk model (RWM).

A Historical Perspective on Market Risks Using the DJIA Index Over One Century

AbstractThe availability of the Dow Jones Industrial Average (DJIA) index over one century allows an investigation of the stability through time of the statistical properties of this time series, and a measurement of the performance of the main risk evaluation methodologies. The main risk methodologies are presented and discussed. The difference between fundamental limitations (that cannot be changed) and particular implementations (that can be tested and improved) is clearly made. Thanks to the large sample (28850 days), clear conclusions can be reached with simple statistics. In particular, the various risk methodologies perform just according to their respective ingredients used to build the volatility forecast (short versus long memory) and distributional assumption (normal versus fat‐tail). The time stability analysis is carried out using the algorithms developed for risk evaluations. After the volatility is properly discounted, the statistical properties of the DJIA index appear to be extraordinarily stable over time, despite the very significant changes that occurred during the 20th century.

DJIA Index Membership and the Flow of Information Between Stock Returns

We contribute to the limited literature on interactions between individual stocks at the market microstructure level by utilizing an asynchronous multi-asset data integration methodology. For the periods before and after the Dow Jones Industrial Average (DJIA) revisions in 1999 and 2004, we investigate effects of membership in the DJIA on the changes in the relative speed of price adjustment to common information. Although, for the analyzed stocks, index membership in many cases is associated with a higher relative speed of price adjustment, it is neither a necessary nor a sufficient condition for having a lead in price movement.

Microscopic origin of non-Gaussian distributions of financial returns

In this paper we study the possible microscopic origin of heavy-tailed probability density distributions for the price variation of financial instruments. We extend the standard log-normal process to include another random component in the so-called stochastic volatility models. We study these models under an assumption, akin to the Born–Oppenheimer approximation, in which the volatility has already relaxed to its equilibrium distribution and acts as a background to the evolution of the price process. In this approximation, we show that all models of stochastic volatility should exhibit a scaling relation in the time lag of zero-drift modified log-returns. We verify that the Dow–Jones Industrial Average index indeed follows this scaling. We then focus on two popular stochastic volatility models, the Heston and Hull–White models. In particular, we show that in the Hull–White model the resulting probability distribution of log-returns in this approximation corresponds to the Tsallis (t-Student) distribution. The Tsallis parameters are given in terms of the microscopic stochastic volatility model. Finally, we show that the log-returns for 30 years Dow Jones index data is well fitted by a Tsallis distribution, obtaining the relevant parameters.

Intraday Price Discovery in the DJIA Index Markets

Abstract: This paper explores the dynamics of price discovery between the Dow Jones Industrial Average (DJIA) index and its three derivative products: the DIAMOND exchange‐traded fund (ETF), the floor‐traded regular futures, and the electronically traded mini futures. Even though the American Stock Exchange is the primary listing exchange for the ETF, the analysis indicates that the electronically traded ETF on the Archipelago (ArcaEx) electronic communications network dominates the price discovery process for DIAMOND shares. The E‐mini futures contribute the most to price discovery, followed by the ArcaEx DIAMOND. The DJIA index and regular futures contribute least to price discovery. The analysis is repeated using the derivatives of the S&amp;P 500 index as a robustness check. The results indicate that multi‐market trading ensures greater pricing efficiency. Informed traders favor electronic trading because of immediate and anonymous trade execution.

Estimating time-varying conditional correlations between stock and foreign exchange markets

This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.

Price discovery and volatility spillovers in the DJIA index and futures markets

The Dow Jones Industrial Average (DJIA) is the most widely quoted stock index worldwide. 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 Hasbrouck (1995) cointegrating model suggests that most of the price discovery takes place at the futures market. However, by examining the volatility spillovers between the markets based on a bivariate EGARCH model, a significant bidirectional information flow is found. That is, innovations in one market can predict the future volatility in another market, but the futures market volatility-spillovers to the stock market more than vice versa. Both markets also exhibit asymmetric volatility effects, with bad news having a greater impact on volatility than good news. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 911–930, 1999

Price discovery and volatility spillovers in the DJIA index and futures markets

The Dow Jones Industrial Average (DJIA) is the most widely quoted stock index worldwide. 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 Hasbrouck (1995) cointegrating model suggests that most of the price discovery takes place at the futures market. However, by examining the volatility spillovers between the markets based on a bivariate EGARCH model, a significant bidirectional information flow is found. That is, innovations in one market can predict the future volatility in another market, but the futures market volatility-spillovers to the stock market more than vice versa. Both markets also exhibit asymmetric volatility effects, with bad news having a greater impact on volatility than good news. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 911–930, 1999

On the Peculiar Distribution of the U.S. Stock Indexes' Digits

Abstract Recent research has focused on studying the patterns in the digits of closely followed stock market indexes. In this paper we find that the series of 1-day returns on the Dow-Jones Industrial Average Index (DJIA) and the Standard and Poor's Index (S&P) reasonably agrees with Benford's law and therefore belongs to the family of anomalous or outlaw numbers.