Mostrando 1 - 20 Resultados de 27 Para Buscar '"DJIA"', tiempo de consulta: 0.36s Limitar resultados
  1. 1
    “…The Dow Jones Industrial Average (DJIA) index is selected as a test-bed. The DJIA time-series is normalized and segmented into several time window vectors. …”
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    Online Artículo Texto
    Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization
  2. 2
    por Anagnoste, Sorin, Caraiani, Petre
    Publicado 2019
    “…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.…”
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    Online Artículo Texto
    The Impact of Financial and Macroeconomic Shocks on the Entropy of Financial Markets
  3. 3
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    Design and Tests of Optical Klystron for FEL
  4. 4
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    Upgrade of NSRL Optical Klystron for FEL
  5. 5
    por Machado, José A. Tenreiro
    Publicado 2020
    “…The Dow Jones Industrial Average (DJIA) is one of the most influential financial indices, and due to its importance, it is adopted as a test bed for this study. …”
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    Online Artículo Texto
    Fractal and Entropy Analysis of the Dow Jones Index Using Multidimensional Scaling
  6. 6
    “…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. …”
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    Quantifying the Behavior of Stock Correlations Under Market Stress
  7. 7
    por Qiu, Jiayu, Wang, Bin, Zhou, Changjun
    Publicado 2020
    “…We compared the results with the other three models, including the LSTM model, the LSTM model with wavelet denoising and the gated recurrent unit(GRU) neural network model on S&P 500, DJIA, HSI datasets. Results from experiments on the S&P 500 and DJIA datasets show that the coefficient of determination of the attention-based LSTM model is both higher than 0.94, and the mean square error of our model is both lower than 0.05.…”
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    Forecasting stock prices with long-short term memory neural network based on attention mechanism
  8. 8
    “…The results show that the proposed model is able to generate higher mean return than the benchmark DJIA index at minimum risk. However, short sale is not allowed for the applicability of the proposed model in portfolio investment.…”
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    Financial Network Analysis on the Performance of Companies Using Integrated Entropy–DEMATEL–TOPSIS Model
  9. 9
    por Moon, Jiyeon, Ha, Jisoo, Kang, Daihun
    Publicado 2023
    “…TECP had the greatest correlation with DJIA (r = 0.952, P < 0.001). In the multiple regression analysis, the increase in TECP led to the rise of the NASDAQ 100 index (adjusted R(2) was 0.790, P < 0.001). …”
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    Relationship between Total Expenditure on Cosmetic Procedures in the United States and NASDAQ 100 Index
  10. 10
    por Haffar, Adlane, Le Fur, Éric
    Publicado 2022
    “…We use copula-GARCH models to test the time-varying dependence of CATs, in a portfolio composed of six stock markets (CAC 40, DJIA, EUROSTOXX 50, FTSE 100, HANGSENG, and NIKKEI 225). …”
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    Dependence structure of CAT bonds and portfolio diversification: a copula-GARCH approach
  11. 11
    “…More precisely, we use the [Image: see text] stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. …”
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    Influence of the Time Scale on the Construction of Financial Networks
  12. 12
    por Chen, H, Sun, Z, Zhang, J, Aguilar-benitez, M, Ting, S, Rozhkov, A, Pashnin, A, Bertucci, B, Strigari, L, Zhang, Z, Graziani, M, Formato, V, Bindi, V, Menchaca-rocha, A A, Wei, J, Vecchi, M, De carvalho barao, F J, Lourenco antunes, J C, Verguilov, V Z, Oliva, A, Capell, M H, Choutko, V, Ting, S, Krasnopevtsev, D, Galdames perez, A, Yu, Y, Wallmann, C, Cui, Z, Bordalo, P, Wu, Z, Berdugo perez, J F, Velasco frutos, M A, Yan, Q, Belyaev, N, Qu, Z, Chouridou, S, Battiston, R, Ferreira da gama velho arruda, M L, Wang, Y, Diaz ginzo, C, Plyaskin, V, Hsieh, T, Ambrosi, G, Di felice, V, Xiao, X, Chang, Y, Xu, W, Bartoloni, A, Burger, J, Wang, X, Nikonov, N, Bayyari, A, Basegmez du pree, S, Borchiellini, M, Casaus, J A, Cai, X, Eline, A, Kounine, O, Kulemzin, A, Medvedeva, T, Weng, Z, Behlmann, M D, Jia, Y, Molero gonzalez, M, Chen, Y, Chou, H, Koutsenko, V, Dadzie, K E, Vazquez acosta, M L
    Publicado 2002
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    Alpha Magnetic Spectrometer (AMS) for Extraterrestrial Study of Antimatter, Matter and Missing Matter on the International Space Station
  13. 13
    “…We employ daily returns of Bitcoin, Gold, DJIA, CAC40, NSE50, S&P 500, NASDAQ, and EUROSTOXX from 05–01–2015 to 31–12–2020. …”
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    Testing the safe-haven properties of gold and bitcoin in the backdrop of COVID-19: A wavelet quantile correlation approach
  14. 14
    “…We extend the literature on stock market forecasting by applying a hybrid model that combines wavelet transform (WT), long short-term memory (LSTM), and an adaptive genetic algorithm (AGA) based on individual ranking to predict stock indices for the Dow Jones Industrial Average (DJIA) index of the New York Stock Exchange, Standard & Poor’s 500 (S&P 500) index, Nikkei 225 index of Tokyo, Hang Seng Index of Hong Kong market, CSI300 index of Chinese mainland stock market, and NIFTY50 index of India. …”
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    A hybrid model integrating long short-term memory with adaptive genetic algorithm based on individual ranking for stock index prediction
  15. 15
    por Xidonas, Panos, Essner, Eric
    Publicado 2022
    “…The validity of the proposed model is assessed through an extensive empirical testing on the EURO STOXX 50, the DAX, the CAC 40 and the DJIA, for a 5-year period. The results are considered as highly satisfactory, since the optimal ESG portfolios produced by the model provide consistently higher risk-adjusted returns, in comparison to their respective market benchmarks.…”
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    Online Artículo Texto
    On ESG Portfolio Construction: A Multi-Objective Optimization Approach
  16. 16
    “…In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. …”
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    The Effects of Twitter Sentiment on Stock Price Returns
  17. 17
    “…The main empirical findings are the following: (i) all the country specific pairs of SSR with stock markets have significant multifractal characteristics (ii) the pairs of NZ-SSR/NZX50, US-SSR/DJIA, and CN-SSR/S&P TSX have the highest multifractal patterns while EU-SSR/Euro-area Index has the lowest multifractal patterns (iii) Australian and New Zealand stock markets exhibit anti-persistent cross-correlation with SSR while the remainder have persistent cross-correlation in their multifractality. …”
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    Online Artículo Texto
    Interplay of multifractal dynamics between shadow policy rates and stock markets
  18. 18
    “…Specifically, long range positively correlated ARFIMA processes with differencing parameter [Formula: see text] , [Formula: see text] and [Formula: see text] are consistent with moving average cluster entropy results obtained in time series of DJIA, S&P500 and NASDAQ. The findings clearly point to a variability of price returns, consistently with a price dynamics involving multiple temporal scales and, thus, short- and long-run volatility components. …”
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    Long-Range Dependence in Financial Markets: A Moving Average Cluster Entropy Approach
  19. 19
    por Vințe, Claudiu, Ausloos, Marcel
    Publicado 2022
    “…We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor’s 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. …”
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    The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator
  20. 20
    por Curto, José Dias
    Publicado 2021
    “…In this paper, we provide evidence in favor of a “quietness” in the stock markets, interrupted by COVID-19, by analyzing dispersion, skewness and kurtosis characteristics of the empirical distribution of nine returns series that include individual FATANG stocks (FAANG: Facebook, Amazon, Apple, Netflix and Google; plus Tesla) and US indices (S&P 500, DJIA and NASDAQ). In comparison with the years before, the daily average return after COVID-19 was 6.48, 2.58 and 2.34 times higher for Tesla, Apple and NASDAQ, respectively. …”
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    To keep faith with homoskedasticity or to go back to heteroskedasticity? The case of FATANG stocks
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