Environmental Regulations, Green Innovation and Performance: An Analysis of Industrial Sector Companies from Developed Countries and Emerging Countries by Jaluza Maria Lima Silva Borsatto, Camila Bazani, Lara Amui

“…The sample was composed of 159 industrial companies, listed in the Financial Times’ 500 largest companies by market value in 2015. …”
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Determinants of UK companies’ dividend policy by Munther Momany, Khaled Bataineh, Omar Al-Bataineh

“…This study examines the major factors influencing UK companies listed on the Financial Times Stock Exchange (FTSE) 100 stock market's dividend policy (as determined by the dividend payout ratio) over 32 years, from 1990 to 2022. …”
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A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model by Sun Yapeng, Peng Hui, Xie Wenbiao

“…The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. …”
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The Multiplex Dependency Structure of Financial Markets by Nicolò Musmeci, Vincenzo Nicosia, Tomaso Aste, Tiziana Di Matteo, Vito Latora

“…In particular, we consider multiplex networks made of four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. …”
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The Optimal Bandwidth Parameter Selection in GPH Estimation by Weijie Zhou, Huihui Tao, Feifei Wang, Weiqiang Pan

“…Firstly, combining with the stylized facts of financial time series, we generate long memory sequences by using the ARFIMA (1, d, 1) process. …”
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Time Series Prediction Based on Complex-Valued S-System Model by Bin Yang, Wenzheng Bao, Yuehui Chen

“…The hybrid evolutionary algorithm based on complex-valued restricted additive tree and firefly algorithm is proposed to search the optimal CVSS model. Three financial time series data and Mackey–Glass chaos time series are collected to evaluate our proposed method. …”
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Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis by Peng Zheng, Bin Liu, Zhongli Zhou

“…To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving V-statistic on the basis of a modified R/S method to determine whether the time series has a long-range dependence and subsequently to apply wavelet-based multiresolution analysis to study the multifractality of the financial time series to determine the initial data windows. …”
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Hybrid Model for Stock Market Volatility by Kofi Agyarko, Nana Kena Frempong, Eric Neebo Wiah

“…The proposed BSGARCH (1, 1) model was applied to simulated data and two real financial time series data (NASDAQ 100 and S&P 500). The outcome was then compared to the outcomes of the GARCH (1, 1), EGARCH (1, 1), GJR-GARCH (1, 1), and APARCH (1, 1) with different error distributions (ED) using the mean absolute percentage error (MAPE), the root mean square error (RMSE), Theil’s inequality coefficient (TIC) and QLIKE. …”
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Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX by Luca Di Persio, Samuele Vettori

“…We adopt a regime switching approach to study concrete financial time series with particular emphasis on their volatility characteristics considered in a space-time setting. …”
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Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique by Shangkun Deng, Yingke Zhu, Ruijie Liu, Wanyu Xu

“…Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. …”
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Nonstationary Generalised Autoregressive Conditional Heteroskedasticity Modelling for Fitting Higher Order Moments of Financial Series within Moving Time Windows by Luke De Clerk, Sergey Savel’ev

“…When we assume a Gaussian conditional distribution, we fail to capture any empirical data when fitting the first three even moments of financial time series. We show instead that a mixture of normal distributions is needed to better capture the higher order moments of the data. …”
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Evaluation of the Fluctuation Mechanism of Behavioral Financial Market Based on Edge Computing by Xiaoliang Yuan

“…Analysis of the volatility characteristics of financial markets must give priority to the analysis of financial chronological order. Financial time series are characterized by differences in financial markets, which are indeterminate orders, and the analysis of their fluctuations becomes crucial for stimulating the microstructure of financial behavior markets. …”
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Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets by Kubra Noor, Ubaida Fatima

“…This research proposes a Meta learning framework for financial time series forecasting, designed to rapidly adapt to novel market conditions with minimal retraining. …”
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Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach by Shanglei Chai, Zhen Zhang, Mo Du, Lei Jiang

“…The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). …”
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A CNN-LSTM-Based Model to Forecast Stock Prices by Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, Jingyang Wang

“…This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.…”
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Long short-term memory autoencoder based network of financial indices by Kamrul Hasan Tuhin, Ashadun Nobi, Mahmudul Hasan Rakib, Jae Woo Lee

“…Abstract We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. …”
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