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Journal articles on the topic 'Financial time series model'

Author: Grafiati

Published: 20 February 2023

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1

Lupekesa, Chipasha Salome Bwalya, Johannes Tshepiso Tsoku, and Lebotsa Daniel Metsileng. "Econometric Modelling of Financial Time Series." International Journal of Management, Entrepreneurship, Social Science and Humanities 5, no. 2 (December 30, 2022): 52–70. http://dx.doi.org/10.31098/ijmesh.v5i2.622.

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This paper examines the relationship between assets, capital, liabilities and liquidity in South Africa using the Johansen cointegration analysis and the GARCH model using times data for the period 02/2005 to 06/2018. The results obtained from the study suggests that the time series are integrated of order one, I(1). The findings from the Johansen cointegration test indicated that the variables have a long run cointegrating relationship. Furthermore, the results from the GARCH model revealed that the estimated model has statistically significant coefficients at 5% significance level. Additionally, results revealed that assets have a positive relationship with capital, liabilities and liquidity. This implies that a percentage increase in assets will result to a percentage increase in capital, liabilities and liquidity. The results also revealed that shocks decay quickly in the future and that the conditional variance is explosive. The diagnostic tests revealed that the estimated models show the characteristics of a well specified model. The recommendations for future studies were formulated. Keywords: ARCH model; Cointegration; Financial time series; GARCH model; VECM; Volatility

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2

Ince, Huseyin, and Fatma Sonmez Cakir. "Analysis of financial time series with model hybridization." Pressacademia 4, no. 3 (September 30, 2017): 331–41. http://dx.doi.org/10.17261/pressacademia.2017.700.

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3

Jiang, Hui, and Zhizhong Wang. "GMRVVm–SVR model for financial time series forecasting." Expert Systems with Applications 37, no. 12 (December 2010): 7813–18. http://dx.doi.org/10.1016/j.eswa.2010.04.058.

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4

Feng, Y., J. Beran, and K. Yu. "Modelling financial time series with SEMIFAR GARCH model." IMA Journal of Management Mathematics 18, no. 4 (April 26, 2007): 395–412. http://dx.doi.org/10.1093/imaman/dpm024.

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5

Richards, Gordon R. "A fractal forecasting model for financial time series." Journal of Forecasting 23, no. 8 (2004): 586–601. http://dx.doi.org/10.1002/for.927.

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6

Alhnaity, Bashar, and Maysam Abbod. "A new hybrid financial time series prediction model." Engineering Applications of Artificial Intelligence 95 (October 2020): 103873. http://dx.doi.org/10.1016/j.engappai.2020.103873.

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7

Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (January 28, 2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.

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Kiesel, Rüdiger, Magda Mroz, and Ulrich Stadtmüller. "Time-varying copula models for financial time series." Advances in Applied Probability 48, A (July 2016): 159–80. http://dx.doi.org/10.1017/apr.2016.48.

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AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.

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9

Muhammad Najamuddin and Samreen Fatima. "Hybrid BRNN-ARIMA Model for Financial Time Series Forecasting." Sukkur IBA Journal of Computing and Mathematical Sciences 6, no. 1 (July 21, 2022): 62–71. http://dx.doi.org/10.30537/sjcms.v6i1.1027.

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The accurate forecasting of time series is difficult and for exchange rate more difficult as well. Because it is difficult to predict as they continuously fluctuate during trading hours. Exchange rate forecasting plays a vital financial problem in recent era. It is extensively acknowledged that exchange rate stability implies that macroeconomic stability. In this study, a hybrid model is proposed to forecast exchange rates. Bayesian regularized neural network (BRNN) model is assembled with Autoregressive integrated moving average model (ARIMA) and develop hybrid BRNN-ARIMA model. Furthermore, the comparison of proposed hybrid model has been done with standalone BRNN, standalone ARIMA and random walk model. Quarterly exchange rate data from 1970Q1 to 2021Q2 of six countries comprises developed (UK, Canada, and Singapore) and developing (Pakistan, India, and Malaysia) are forecast. To evaluate the performance of these models RMSE, MAE and MAPE are applied. The results indicate that the proposed hybrid BRNN-ARIMA model outperforms the other studied model in forecasting exchange rates.

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10

Kwak, Nae Won, and Dong Hoon Lim. "Financial time series forecasting using AdaBoost-GRU ensemble model." Journal of the Korean Data And Information Science Society 32, no. 2 (March 31, 2021): 267–81. http://dx.doi.org/10.7465/jkdi.2021.32.2.267.

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11

HUANG, Lei, Jinglu HU, and Kotaro HIRASAWA. "A Quasi-ARMA Model for Financial Time Series Prediction." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2007 (May 5, 2007): 64–69. http://dx.doi.org/10.5687/sss.2007.64.

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12

Zhou, Tianle, Shangce Gao, Jiahai Wang, Chaoyi Chu, Yuki Todo, and Zheng Tang. "Financial time series prediction using a dendritic neuron model." Knowledge-Based Systems 105 (August 2016): 214–24. http://dx.doi.org/10.1016/j.knosys.2016.05.031.

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13

Takaishi, Tetsuya. "Multiple Time Series Ising Model for Financial Market Simulations." Journal of Physics: Conference Series 574 (January 21, 2015): 012149. http://dx.doi.org/10.1088/1742-6596/574/1/012149.

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14

Liu, Y., and J. A. Tawn. "Volatility model selection for extremes of financial time series." Journal of Statistical Planning and Inference 143, no. 3 (March 2013): 520–30. http://dx.doi.org/10.1016/j.jspi.2012.08.009.

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15

Virili, Francesco, and Bernd Freisleben. "Neural Network Model Selection for Financial Time Series Prediction." Computational Statistics 16, no. 3 (September 2001): 451–63. http://dx.doi.org/10.1007/s001800100078.

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16

Caporin, Massimiliano, and Giuseppe Storti. "Financial Time Series: Methods and Models." Journal of Risk and Financial Management 13, no. 5 (April 28, 2020): 86. http://dx.doi.org/10.3390/jrfm13050086.

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The statistical analysis of financial time series is a rich and diversified research field whose inherent complexity requires an interdisciplinary approach, gathering together several disciplines, such as statistics, economics, and computational sciences. This special issue of the Journal of Risk and Financial Management on “Financial Time Series: Methods & Models” contributes to the evolution of research on the analysis of financial time series by presenting a diversified collection of scientific contributions exploring different lines of research within this field.

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Sako, Kady, Berthine Nyunga Mpinda, and Paulo Canas Rodrigues. "Neural Networks for Financial Time Series Forecasting." Entropy 24, no. 5 (May 7, 2022): 657. http://dx.doi.org/10.3390/e24050657.

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Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.

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18

Tang, Kai. "Financial Time Series Prediction Based on EMD-SVM." BCP Business & Management 30 (October 24, 2022): 218–27. http://dx.doi.org/10.54691/bcpbm.v30i.2435.

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The market plays an essential role in the national economy and society, and people pay more and more attention to the investment in the capital market. Every valuable investment needs the guidance of scientific theory. With the improvement of China's securities market system, more and more researchers have conducted in-depth research focusing on the development of the stock market. This paper will study the stock price prediction algorithm, select the Bank of Ningbo as the research object, and propose a prediction model based on the combination of EMD and SVM algorithms. This paper first decomposes the original sequence by EMD and comprehensively considers the three related factors of the Shanghai Composite Index, Shenzhen Composite Index, and China Merchants Bank in the prediction model. Then, the SVM model is used to predict multiple decomposition sequences, respectively, and the prediction results are obtained after integrated processing. Then, regression analysis is carried out on them, and the weight is designed according to the constructed regression analysis model. The final prediction result is obtained after reconstruction. Compared with the basic SVM model, the combined prediction model constructed in this paper has a better prediction effect, and the prediction results are more objective and scientific.

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19

Sun, Yanfeng, Minglei Zhang, Si Chen, and Xiaohu Shi. "A Financial Embedded Vector Model and Its Applications to Time Series Forecasting." International Journal of Computers Communications & Control 13, no. 5 (September 29, 2018): 881–94. http://dx.doi.org/10.15837/ijccc.2018.5.3286.

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Inspired by the embedding representation in Natural Language Processing (NLP), we develop a financial embedded vector representation model to abstract the temporal characteristics of financial time series. Original financial features are discretized firstly, and then each set of discretized features is considered as a “word” of NLP, while the whole financial time series corresponds to the “sentence” or “paragraph”. Therefore the embedded vector models in NLP could be applied to the financial time series. To test the proposed model, we use RBF neural networks as regression model to predict financial series by comparing the financial embedding vectors as input with the original features. Numerical results show that the prediction accuracy of the test data is improved for about 4-6 orders of magnitude, meaning that the financial embedded vector has a strong generalization ability.

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20

Sproul, Thomas W. "Time scale and fractionality in financial time series." Agricultural Finance Review 76, no. 1 (May 3, 2016): 76–93. http://dx.doi.org/10.1108/afr-01-2016-0008.

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Purpose – Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem. Design/methodology/approach – Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality. Findings – Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of erroneous rejections of the null hypothesis of efficient markets following an independent random walk. Even with these adjustments, a few futures contracts significantly violate independence for short lags at the 99 percent level, and a number of equities/lags violate independence at the 95 percent level. When testing at the asset level, futures prices are found not to contain fractional properties, while some equities do. Research limitations/implications – Only a subsample of futures and equities, and only a limited number of lags, are evaluated. It is possible that multiplicity adjustments for larger numbers of tests would result in fewer rejections of independence. Originality/value – This paper provides empirical evidence that violations of the RWH for financial time series are likely to exist, but are perhaps less common than previously thought.

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21

Niu, Hongli, and Jun Wang. "Volatility clustering and long memory of financial time series and financial price model." Digital Signal Processing 23, no. 2 (March 2013): 489–98. http://dx.doi.org/10.1016/j.dsp.2012.11.004.

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22

KUWABARA, Masami, and Norio WATANABE. "Financial Time Series Analysis Based on a Fuzzy Trend Model." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 20, no. 2 (2008): 244–54. http://dx.doi.org/10.3156/jsoft.20.244.

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23

Chang, Qingqing, and Jincheng Hu. "Application of Hidden Markov Model in Financial Time Series Data." Security and Communication Networks 2022 (April 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/1465216.

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Financial time series have typical characteristics such as outliers, trends, and mean reversion. The existence of outliers will affect the effectiveness of the unknown parameter estimation in the financial time series forecasting model, so that the forecasting error of the model will be larger. Quantitative forecasting methods are divided into causal forecasting method and time series forecasting method. The causal forecasting method uses the causal relationship between the predictor variable and other variables to predict, and the time series forecasting method infers the future value of the predictor variable based on the structure of the historical data of the predictor. Therefore, this paper proposes a hidden Markov model prediction method based on the observation vector sequence, which can simultaneously consider the influence of the variable sequence structure and related factors.

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24

Guo, Zhiqiang, Huaiqing Wang, Quan Liu, and Jie Yang. "A Feature Fusion Based Forecasting Model for Financial Time Series." PLoS ONE 9, no. 6 (June 27, 2014): e101113. http://dx.doi.org/10.1371/journal.pone.0101113.

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25

Cao, Jian, Zhi Li, and Jian Li. "Financial time series forecasting model based on CEEMDAN and LSTM." Physica A: Statistical Mechanics and its Applications 519 (April 2019): 127–39. http://dx.doi.org/10.1016/j.physa.2018.11.061.

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26

Li, Weimin, Yishu Luo, Qin Zhu, Jianwei Liu, and Jiajin Le. "Applications of AR*-GRNN model for financial time series forecasting." Neural Computing and Applications 17, no. 5-6 (August 2, 2007): 441–48. http://dx.doi.org/10.1007/s00521-007-0131-9.

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27

Han, Jianan, Xiao-Ping Zhang, and Fang Wang. "Gaussian Process Regression Stochastic Volatility Model for Financial Time Series." IEEE Journal of Selected Topics in Signal Processing 10, no. 6 (September 2016): 1015–28. http://dx.doi.org/10.1109/jstsp.2016.2570738.

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28

Truong, Buu-Chau, Cathy W. S. Chen, and Mike K. P. So. "Model selection of a switching mechanism for financial time series." Applied Stochastic Models in Business and Industry 32, no. 6 (September 21, 2016): 836–51. http://dx.doi.org/10.1002/asmb.2205.

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29

Bao, Depei. "A generalized model for financial time series representation and prediction." Applied Intelligence 29, no. 1 (June 16, 2007): 1–11. http://dx.doi.org/10.1007/s10489-007-0063-1.

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Bao, Depei. "A generalized model for financial time series representation and prediction." Applied Intelligence 29, no. 1 (November 10, 2007): 12. http://dx.doi.org/10.1007/s10489-007-0104-9.

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31

Aznarte, José Luis, Jesús Alcalá-Fdez, Antonio Arauzo-Azofra, and José Manuel Benítez. "Financial time series forecasting with a bio-inspired fuzzy model." Expert Systems with Applications 39, no. 16 (November 2012): 12302–9. http://dx.doi.org/10.1016/j.eswa.2012.02.135.

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32

Chen, Ying, and Vladimir Spokoiny. "MODELING NONSTATIONARY AND LEPTOKURTIC FINANCIAL TIME SERIES." Econometric Theory 31, no. 4 (October 14, 2014): 703–28. http://dx.doi.org/10.1017/s0266466614000528.

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Financial time series is often assumed to be stationary and has a normal distribution in the literature. Both assumptions are however unrealistic. This paper proposes a new methodology with a focus on volatility estimation that is able to account for nonstationarity and heavy tails simultaneously. In particular, a local exponential smoothing (LES) approach is developed, in which weak estimates with different memory parameters are aggregated in a locally adaptive way. The procedure is fully automatic and the parameters are tuned by a new propagation approach. The extensive and practically oriented numerical results confirm the desired properties of the constructed estimate: it performs stable in a nearly time homogeneous situation and is sensitive to structural shifts. Our main theoretical “oracle” result claims that the aggregated estimate performs as good as the best estimate in the considered family. The results are stated under realistic and unrestrictive assumptions on the model.

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33

Xu, Jialing, Jingxing He, Jinqiang Gu, Huayang Wu, Lei Wang, Yongzhen Zhu, Tiejun Wang, Xiaoling He, and Zhangyuan Zhou. "Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 637–45. http://dx.doi.org/10.46300/9106.2022.16.79.

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Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. Alibaba stock is taken as the research object, using XGBoost to optimize its characteristic factors, and training the optimized characteristic variables with WGAN-GP. We compare the prediction results of WGAN-GP model and classical time series prediction models, long short term memory (LSTM) and gate recurrent unit (GRU). In the experimental stage, root mean square error (RMSE) is chosen as the evaluation index. The results of different models show that the RMSE of WGAN-GP model is the smallest, which are 61.94% and 47.42%, lower than that of LSTM model and GRU model respectively. At the same time, the stock price data of Google and Amazon confirm the stability of WGAN-GP model. WGAN-GP model can obtain higher prediction accuracy than the classical time series prediction model.

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Widiputra, Harya, Adele Mailangkay, and Elliana Gautama. "Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction." Complexity 2021 (October 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/9903518.

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At the macroeconomic level, the movement of the stock market index, which is determined by the moves of other stock market indices around the world or in that region, is one of the primary factors in assessing the global economic and financial situation, making it a critical topic to monitor over time. As a result, the potential to reliably forecast the future value of stock market indices by taking trade relationships into account is critical. The aim of the research is to create a time-series data forecasting model that incorporates the best features of many time-series data analysis models. The hybrid ensemble model built in this study is made up of two main components, each with its own set of functions derived from the CNN and LSTM models. For multiple parallel financial time-series estimation, the proposed model is called multivariate CNN-LSTM. The effectiveness of the evolved ensemble model during the COVID-19 pandemic was tested using regular stock market indices from four Asian stock markets: Shanghai, Japan, Singapore, and Indonesia. In contrast to CNN and LSTM, the experimental results show that multivariate CNN-LSTM has the highest statistical accuracy and reliability (smallest RMSE value). This finding supports the use of multivariate CNN-LSTM to forecast the value of different stock market indices and that it is a viable choice for research involving the development of models for the study of financial time-series prediction.

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35

Gruevski, Ilija. "Basic Time Series Models in Financial Forecasting." Journal of Economics 6, no. 1 (2021): 76–89. http://dx.doi.org/10.46763/joe216.10076g.

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36

Panorska, A. K., S. Mittnik, and S. T. Rachev. "Stable GARCH models for financial time series." Applied Mathematics Letters 8, no. 5 (September 1995): 33–37. http://dx.doi.org/10.1016/0893-9659(95)00063-v.

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37

Vlasenko, Vlasenko, Vynokurova, Bodyanskiy, and Peleshko. "A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting." Data 4, no. 3 (August 23, 2019): 126. http://dx.doi.org/10.3390/data4030126.

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Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

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38

Wang, Jie, Jun Wang, Wen Fang, and Hongli Niu. "Financial Time Series Prediction Using Elman Recurrent Random Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4742515.

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In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

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Jia, Helin. "Deep Learning Algorithm-Based Financial Prediction Models." Complexity 2021 (March 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/5560886.

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In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured.

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40

Cheng, Ching-Hsue, Chia-Pang Chan, and Jun-He Yang. "A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress." Computational Intelligence and Neuroscience 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/1067350.

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The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

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41

Li, Xinhui. "Application of Neural Networks in Financial Time Series Forecasting Models." Journal of Function Spaces 2022 (August 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/7817264.

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At present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index.

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42

Lieberman, Offer. "ASYMPTOTIC THEORY OF STATISTICAL INFERENCE FOR TIME SERIES." Econometric Theory 18, no. 4 (May 17, 2002): 993–99. http://dx.doi.org/10.1017/s0266466602004103.

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Modern time series econometrics involves a diversity of models. In addition to the more traditional vector autoregressive (VAR) and autoregressive moving average (ARMA) systems, cointegration and unit root models are in widespread use for macroeconomic data, nonlinear and non-Gaussian models are popular for financial data, and long memory models are becoming more common in both macroeconomic and financial applications. Much econometric thought relates to issues of estimation and hypothesis testing, and so, in the absence of a usable finite sample theory (as is the case for the models just mentioned), an enormous amount of effort has been given to developing adequate asymptotics for statistical inference. There is often a lag between the introduction of a new model and the development of an asymptotic theory. In consequence, applied econometricians sometimes have to estimate time series models for which no asymptotic theory is available. For instance, multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models have been in use in empirical research for a while, and practitioners have been using asymptotic normality of estimators in this model even though a theoretical justification is not available.

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Yu, Yan. "A Study of Stock Market Predictability Based on Financial Time Series Models." Mobile Information Systems 2022 (August 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/8077277.

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In today’s era of economic globalization and financial integration, the stock market is constantly complex, showing many deviations that cannot be explained by classical financial analysis, but at the same time, some classic financial statistical features have striking similarities. This suggests that although the stock market is intricate, there are universal laws that can be found through data mining to find its underlying operating rules. In this paper, we construct financial time series models such as ARIMA, ARCH, and GARCH to predict the stock market price fluctuations and trends. The ARIMA model is used to fit the linear financial time series, and the GARCH model is used to fit the nonlinear time series residuals. The results show that the integrated tree model based on the idea of weight voting has high accuracy in predicting stock market bulls and bears, with XGBoost prediction accuracy up to 96%, and the neural network model is also very effective, with an accuracy rate of over 90%.

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PAVLIDIS, N. G., D. K. TASOULIS, V. P. PLAGIANAKOS, and M. N. VRAHATIS. "COMPUTATIONAL INTELLIGENCE METHODS FOR FINANCIAL TIME SERIES MODELING." International Journal of Bifurcation and Chaos 16, no. 07 (July 2006): 2053–62. http://dx.doi.org/10.1142/s0218127406015891.

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In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountered in real-life applications. An improvement in the one-step-ahead forecasting accuracy was achieved compared to a global feedforward neural network model for the time series of the exchange rate of the German Mark to the US Dollar.

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Andreoli, Alessandro, Francesco Caravenna, Paolo Dai Pra, and Gustavo Posta. "Scaling and Multiscaling in Financial Series: A Simple Model." Advances in Applied Probability 44, no. 4 (December 2012): 1018–51. http://dx.doi.org/10.1239/aap/1354716588.

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We propose a simple stochastic volatility model which is analytically tractable, very easy to simulate, and which captures some relevant stylized facts of financial assets, including scaling properties. In particular, the model displays a crossover in the log-return distribution from power-law tails (small time) to a Gaussian behavior (large time), slow decay in the volatility autocorrelation, and multiscaling of moments. Despite its few parameters, the model is able to fit several key features of the time series of financial indexes, such as the Dow Jones Industrial Average, with remarkable accuracy.

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Andreoli, Alessandro, Francesco Caravenna, Paolo Dai Pra, and Gustavo Posta. "Scaling and Multiscaling in Financial Series: A Simple Model." Advances in Applied Probability 44, no. 04 (December 2012): 1018–51. http://dx.doi.org/10.1017/s0001867800006030.

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We propose a simple stochastic volatility model which is analytically tractable, very easy to simulate, and which captures some relevant stylized facts of financial assets, including scaling properties. In particular, the model displays a crossover in the log-return distribution from power-law tails (small time) to a Gaussian behavior (large time), slow decay in the volatility autocorrelation, and multiscaling of moments. Despite its few parameters, the model is able to fit several key features of the time series of financial indexes, such as the Dow Jones Industrial Average, with remarkable accuracy.

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He, Han, Yuanyuan Hong, Weiwei Liu, and Sung-A. Kim. "Data mining model for multimedia financial time series using information entropy." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5339–45. http://dx.doi.org/10.3233/jifs-189019.

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At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

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Ahmed, Jameel. "A conditionally heteroskedastic binary choice model for macro-financial time series." Journal of Statistical Computation and Simulation 86, no. 10 (October 16, 2015): 2007–35. http://dx.doi.org/10.1080/00949655.2015.1099159.

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Wang, Qingsheng, Aifan Ling, Tao Huang, Yong Jiang, and Min Chen. "A Trend-Switching Financial Time Series Model with Level-Duration Dependence." Mathematical Problems in Engineering 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/345093.

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The financial time series model that can capture the nonlinearity and asymmetry of stochastic process has been paid close attention for a long time. However, it is still open to completely overcome the difficult problem that motivates our researches in this paper. An asymmetric and nonlinear model with the change of local trend depending on local high-low turning point process is first proposed in this paper. As the point process can be decomposed into the two different processes, a high-low level process and an up-down duration process, we then establish the so-called trend-switching model which depends on both level and duration (Trend-LD). The proposed model can predict efficiently the direction and magnitude of the local trend of a time series by incorporating the local high-low turning point information. The numerical results on six indices in world stock markets show that the proposed Trend-LD model is suitable for fitting the market data and able to outperform the traditional random walk model.

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Cai, Yuzhi. "A General Quantile Function Model for Economic and Financial Time Series." Econometric Reviews 35, no. 7 (October 22, 2014): 1173–93. http://dx.doi.org/10.1080/07474938.2014.976528.

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