Trading Volatility of the Dow Jones Industrial Average Stocks

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.

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Mexican Stock Exchange Performance after the Crisis of 2008: Application of Data Mining

Dimensión Empresarial ◽

10.15665/dem.v18i(1).2246 ◽

2020 ◽

Vol 18 ((1)) ◽

Author(s):

Eliseo Ramírez Reyes ◽

Arturo Morales Castro ◽

Néstor Juan Sanabria Landazábal

Keyword(s):

Neural Networks ◽

Default Risk ◽

Regression Models ◽

Prediction Models ◽

Stock Exchange ◽

Sovereign Default ◽

Linear Regression Models ◽

Exchange Performance ◽

Sovereign Default Risk ◽

Dow Jones Industrial Average

Different prediction models are explored to analyze the performance of the Mexican Stock Exchange (PQI) after the 2008 crisis. These models have demonstrated a good prognostic capacity for both multivariable and univariable approaches given their non-parametric characteristics. The selected variables were: Dow Jones Industrial Average Index (DJIA), CPI, International Reserves (IR), CETES28, USDMX exchange rate, (M1) and the sovereign default risk of Mexico (MRDS). The models were evaluated with MAPE and compared with linear regression models (LR) and neural networks (NN). The results show that the models have a similar performance according to the percentages of error they presented.

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

Econometrics Journal ◽

10.1093/ectj/utac001 ◽

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.

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Dow Jones Industrial Average Index (DJIA)

10.1007/springerreference_1320 ◽

2011 ◽

Cited By ~ 18

Keyword(s):

Dow Jones Industrial Average ◽

Average Index

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The Dow-Jones Industrial Average Re-Examined

Financial Analysts Journal ◽

10.2469/faj.v22.n6.83 ◽

1966 ◽

Vol 22 (6) ◽

pp. 83-88 ◽

Cited By ~ 2

Author(s):

Robert D. Milne

Keyword(s):

Dow Jones Industrial Average

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Do DJIA Firms Reflect Stationary Debt Ratios?

Economies ◽

10.3390/economies8040076 ◽

2020 ◽

Vol 8 (4) ◽

pp. 76

Author(s):

Feng-Li Lin

Keyword(s):

Capital Structure ◽

Stationary Processes ◽

Debt Ratio ◽

Pecking Order ◽

Trade Off ◽

Listed Firms ◽

Growth Opportunity ◽

Dow Jones Industrial Average ◽

The Mean ◽

The Stability

To form optimum firm capital structure strategies to face unanticipated economic events, firm managers should understand the stability of a firm’s capital structure. The aim of this research was to study whether the debt ratio is stationary in listed firms on the Dow Jones Industrial Average (DJIA). Two vital capital structure concepts regarding pecking order and trade-off theory are fairly contradictory. Using opposing theoretical contexts, the Sequential Panel Selection Method apparently categorizes which and how many series are stationary processes in the panel. This method was used to test the mean reverting properties of the 25 companies listed on Dow Jones Industrial Average between 2001 and 2017 in this study, which is expected to fill the current gap in the literature. The overall results show that stationary debt ratios exist in 10 of the 25 studied firms, supporting the trade-off theory. Moreover, the 10 firms utilizing trade-off theory are affected by firm size, profitability, growth opportunity, and dividend payout ratio. These results provide vital information for firms to certify strategies to optimize capital structure.

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Abnormal Returns or Mismeasured Risk? Network Effects and Risk Spillover in Stock Returns

Journal of Risk and Financial Management ◽

10.3390/jrfm12020050 ◽

2019 ◽

Vol 12 (2) ◽

pp. 50 ◽

Cited By ~ 1

Author(s):

Arnab Bhattacharjee ◽

Sudipto Roy

Keyword(s):

Stock Returns ◽

Network Effects ◽

Cross Correlation ◽

Factor Model ◽

Empirical Work ◽

Abnormal Returns ◽

Recent Event ◽

Dow Jones Industrial Average ◽

Abnormal Stock Returns ◽

Short Event

Recent event study literature has highlighted abnormal stock returns, particularly in short event windows. A common explanation is the cross-correlation of stock returns that are often enhanced during periods of sharp market movements. This suggests the misspecification of the underlying factor model, typically the Fama-French model. By drawing upon recent panel data literature with cross-section dependence, we argue that the Fame-French factor model can be enriched by allowing explicitly for network effects between stock returns. We show that recent empirical work is consistent with the above interpretation, and we advance some hypotheses along which new structural models for stock returns may be developed. Applied to data on stock returns for the 30 Dow Jones Industrial Average (DJIA) stocks, our framework provides exciting new insights.

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