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Journal articles on the topic 'Financial time series'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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1

Politis, Dimitris N. "Financial time series." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 2 (August 19, 2009): 157–66. http://dx.doi.org/10.1002/wics.24.

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2

Dingli, Alexiei, and Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

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3

Anderson, Gordon, and Stephen Taylor. "Modelling Financial Time Series." Economic Journal 97, no. 386 (June 1987): 512. http://dx.doi.org/10.2307/2232901.

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4

Ruiz, Esther, and Lorenzo Pascual. "Bootstrapping Financial Time Series." Journal of Economic Surveys 16, no. 3 (July 2002): 271–300. http://dx.doi.org/10.1111/1467-6419.00170.

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5

Gemmill, Gordon. "Modelling financial time series." International Journal of Forecasting 4, no. 3 (January 1988): 496–97. http://dx.doi.org/10.1016/0169-2070(88)90115-x.

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6

Kinsella, A., and Stephen Taylor. "Modelling Financial Time Series." Statistician 36, no. 4 (1987): 433. http://dx.doi.org/10.2307/2348865.

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7

Baillie, Richard T. "Modelling financial time series." European Journal of Operational Research 32, no. 1 (October 1987): 156–58. http://dx.doi.org/10.1016/0377-2217(87)90287-6.

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8

Taivan, Ariuna. "Financial Development And Economic Growth Revisited: Time Series Evidence." International Journal of Trade, Economics and Finance 9, no. 3 (June 2018): 116–20. http://dx.doi.org/10.18178/ijtef.2018.9.3.599.

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9

Anderson, G. "Correction: Modelling Financial Time Series." Economic Journal 98, no. 391 (June 1988): 566. http://dx.doi.org/10.2307/2233416.

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10

Audrino, Francesco. "Synchronizing multivariate financial time series." Journal of Risk 6, no. 2 (February 2004): 81–106. http://dx.doi.org/10.21314/jor.2004.105.

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11

Stoyanov, Jordan. "Handbook of Financial Time Series." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 4 (September 20, 2010): 934. http://dx.doi.org/10.1111/j.1467-985x.2010.00663_2.x.

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12

Rao, Suhasini Subba. "Handbook of Financial Time Series." Journal of Time Series Analysis 31, no. 1 (January 2010): 64. http://dx.doi.org/10.1111/j.1467-9892.2009.00640.x.

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13

Ziegel, Eric R. "Analysis of Financial Time Series." Technometrics 44, no. 4 (November 2002): 408. http://dx.doi.org/10.1198/tech.2002.s96.

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14

Lin, Liang-Ching, and Li-Hsien Sun. "Modeling financial interval time series." PLOS ONE 14, no. 2 (February 14, 2019): e0211709. http://dx.doi.org/10.1371/journal.pone.0211709.

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15

Makowiec, Danuta, and Andrzej Posiewnik. "Beauty of financial time series." Physica A: Statistical Mechanics and its Applications 301, no. 1-4 (December 2001): 429–40. http://dx.doi.org/10.1016/s0378-4371(01)00402-2.

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16

D’Urso, Pierpaolo, Carmela Cappelli, Dario Di Lallo, and Riccardo Massari. "Clustering of financial time series." Physica A: Statistical Mechanics and its Applications 392, no. 9 (May 2013): 2114–29. http://dx.doi.org/10.1016/j.physa.2013.01.027.

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17

Christie-David, Rohan. "Analysis of Financial Time Series." Journal of Financial Research 25, no. 3 (September 2002): 445–46. http://dx.doi.org/10.1111/1475-6803.00029.

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18

Zinenko, Anna, and Alena Stupina. "Financial time series forecasting methods." ITM Web of Conferences 59 (2024): 02005. http://dx.doi.org/10.1051/itmconf/20245902005.

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The paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model. These models were applied to non-stationary time series of stock quotes after bringing these series to a stationary form. In the paper, ARIMA and Fourier Expansion model were constructed, using Python development environment. The developed algorithms were tested on Russian and American stock indices using the Mean Absolute Percentage Error metric.
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19

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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20

Hadaś-Dyduch, Monika. "Approximating Financial Time Series with Wavelets." Argumenta Oeconomica Cracoviensia, no. 16 (2017): 9–22. http://dx.doi.org/10.15678/aoc.2017.1601.

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21

Lyubushin, Alexey Alexandrovich, and Yuri Anatolievich Farkov. "Synchronous components of financial time series." Computer Research and Modeling 9, no. 4 (August 2017): 639–55. http://dx.doi.org/10.20537/2076-7633-2017-9-4-639-655.

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22

Zanin, Massimiliano. "Forbidden patterns in financial time series." Chaos: An Interdisciplinary Journal of Nonlinear Science 18, no. 1 (March 2008): 013119. http://dx.doi.org/10.1063/1.2841197.

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23

McAleer, Michael, and Les Oxley. "The Econometrics of Financial Time Series." Journal of Economic Surveys 16, no. 3 (July 2002): 237–43. http://dx.doi.org/10.1111/1467-6419.00168.

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24

EVERTSZ, CARL J. G. "FRACTAL GEOMETRY OF FINANCIAL TIME SERIES." Fractals 03, no. 03 (September 1995): 609–16. http://dx.doi.org/10.1142/s0218348x95000539.

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A simple quantitative measure of the self-similarity in time-series in general and in the stock market in particular is the scaling behavior of the absolute size of the jumps across lags of size k. A stronger form of self-similarity entails that not only this mean absolute value, but also the full distributions of lag-k jumps have a scaling behavior characterized by the above Hurst exponent. In 1963, Benoit Mandelbrot showed that cotton prices have such a strong form of (distributional) self-similarity, and for the first time introduced Lévy’s stable random variables in the modeling of price records. This paper discusses the analysis of the self-similarity of high-frequency DEM-USD exchange rate records and the 30 main German stock price records. Distributional self-similarity is found in both cases and some of its consequences are discussed.
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25

Yarushkina, Nadezhda, Aleksey Filippov, and Anton Romanov. "Contextual Analysis of Financial Time Series." Mathematics 13, no. 1 (December 27, 2024): 57. https://doi.org/10.3390/math13010057.

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The evaluation of the financial state of small and medium-sized companies is a pressing issue today. This article introduces a novel method to evaluate a company’s financial state, implemented as a module within a decision support system. This component uses fuzzy logic and knowledge engineering methods. The article describes an ontological model that provides the framework for data analysis and financial time-series modeling. The ontological framework simplifies the representation of the trends in the financial indicators under analysis. Integrating an ontology and a set of fuzzy rules makes it possible to develop control systems based on fuzzy inference. This approach provides the analysis and interpretation of results. The experimental results validate the accuracy and effectiveness of the proposed method.
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26

Lisi, Francesco. "Testing asymmetry in financial time series." Quantitative Finance 7, no. 6 (December 2007): 687–96. http://dx.doi.org/10.1080/14697680701283739.

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27

Holdom, B. "From turbulence to financial time series." Physica A: Statistical Mechanics and its Applications 254, no. 3-4 (June 1998): 569–76. http://dx.doi.org/10.1016/s0378-4371(98)00078-8.

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28

Gimeno, Ricardo, Benjamı́n Manchado, and Román Mı́nguez. "Stationarity tests for financial time series." Physica A: Statistical Mechanics and its Applications 269, no. 1 (July 1999): 72–78. http://dx.doi.org/10.1016/s0378-4371(99)00081-3.

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29

Fukuda, Kosei. "Distribution switching in financial time series." Mathematics and Computers in Simulation 79, no. 5 (January 2009): 1711–20. http://dx.doi.org/10.1016/j.matcom.2008.08.012.

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30

Basalto, Nicolas, Roberto Bellotti, Francesco De Carlo, Paolo Facchi, Ester Pantaleo, and Saverio Pascazio. "Hausdorff clustering of financial time series." Physica A: Statistical Mechanics and its Applications 379, no. 2 (June 2007): 635–44. http://dx.doi.org/10.1016/j.physa.2007.01.011.

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31

Kanjamapornkul, Kabin, Richard Pinčák, and Erik Bartoš. "Cohomology theory for financial time series." Physica A: Statistical Mechanics and its Applications 546 (May 2020): 122212. http://dx.doi.org/10.1016/j.physa.2019.122212.

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32

Abberger, Klaus. "Quantile smoothing in financial time series." Statistical Papers 38, no. 2 (June 1997): 125–48. http://dx.doi.org/10.1007/bf02925220.

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33

Zhang, Hong, and Ke Qiang Dong. "Fractal Properties of Financial Time Series." Key Engineering Materials 439-440 (June 2010): 683–87. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.683.

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In this paper, we analyze the stock of Nanjing Panda Electronics Co Ltd for the 44-year period, from May 2, 1996, to October 9, 2009, a total of 3200 trading days. Using the Box-counting dimension method, we find that the financial data have different power law exponents in the plot for the number of box and diameter of box, which indicates the multifractality exist in the time series. In order to investigate the latent properties in the data, the width and maximum of the singular spectrum are calculated. The results show the strong degree of multifractality in the time series.
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34

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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35

Buonocore, R. J., T. Aste, and T. Di Matteo. "Measuring multiscaling in financial time-series." Chaos, Solitons & Fractals 88 (July 2016): 38–47. http://dx.doi.org/10.1016/j.chaos.2015.11.022.

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36

Guerrero, Víctor M., and Adriana Galicia-Vázquez. "Trend estimation of financial time series." Applied Stochastic Models in Business and Industry 26, no. 3 (February 13, 2009): 205–23. http://dx.doi.org/10.1002/asmb.763.

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37

Elliman, Dave. "Pattern recognition and financial time-series." Intelligent Systems in Accounting, Finance and Management 14, no. 3 (July 2006): 99–115. http://dx.doi.org/10.1002/isaf.279.

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38

Vlad, Sorin, and Mariana Vlad. "Nonlinear Analysis of Financial Time Series." Ovidius University Annals. Economic Sciences Series 22, no. 2 (January 31, 2023): 478–83. http://dx.doi.org/10.61801/ouaess.2022.2.64.

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39

Kipel, Ali, Cüneyt Yurt, Yıldırım Adigüzel, Zehra Sude Sari, and Mehmet Fatih Akay. "TIME SERIES BASED FINANCIAL FORECASTING MODELS." International Journal of Professional Business Review 10, no. 3 (March 21, 2025): e05382. https://doi.org/10.26668/businessreview/2025.v10i3.5382.

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Objective: The purpose of this study is to develop financial component forecasting models that enable more accurate financial planning, allowing businesses to gain a competitive advantage. Theoretical Framework: Time series-based Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Multi-Quantile Recurrent Neural Network (MQRNN), and Autoregressive Integrated Moving Average (ARIMA) models have been developed for forecasting financial components. Method: Forecasting models have been developed for two different months (June and July) using a dataset containing 291 rows of weekly data from 01.01.2017 to 31.07.2022. The dataset includes data for five different finanacial components including sales, purchase, cash payment, cash collection, and card collection. The performance of the models has been evaluated using Mean Absolute Percentage Error (MAPE). Results and Discussion: The MAPE values obtained with the developed forecasting models range from 2.44% to 26.57%. The CNN-LSTM model exhibits the highest predictive performance among the evaluated models. Research Implications: This research makes significant contributions to financial forecasting and planning by highlighting effectiveness of time series methods. It demonstrates that models developed using CNN-LSTM, MQRNN, and ARIMA perform differently for various financial components, with each model excelling under specific conditions. Practically, these models help businesses improve financial planning, optimize costs, and enhance profit margins. Originality/Value: This study contributes to literature by evaluating the effectiveness of time series methods in financial forecasting models. The findings are applicable in areas such as financial services, retail, and business strategy, offering value for financial risk management, sales forecasting, and long-term decision-making.
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40

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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41

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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42

Carbone, A., G. Castelli, and H. E. Stanley. "Time-dependent Hurst exponent in financial time series." Physica A: Statistical Mechanics and its Applications 344, no. 1-2 (December 2004): 267–71. http://dx.doi.org/10.1016/j.physa.2004.06.130.

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43

Rateiwa, Ronald, and Meshach Jesse Aziakpono. "Financial structure and economic performance in selected African countries: time series evidence." Banks and Bank Systems 11, no. 2 (July 2, 2016): 45–60. http://dx.doi.org/10.21511/bbs.11(2).2016.05.

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In this paper, the authors investigate the long-debated question of whether or not a country’s financial structure matters for economic performance and, if so, how exactly it matters. The study uses the Johansen cointegration and vector error correction modelling framework within a country-specific setting to examine empirically the existence of a long-run equilibrium relationship between the financial structure of a country and per capita GDP and the causality thereof. The empirical assessment is based on evidence from selected African countries over the period 1971-2013, notably Egypt, Nigeria and South Africa. Firstly, cointegration test results reported in this paper show that there exists a strong relationship between the financial structure of Egypt and South Africa, and per capita GDP in these countries. However, such a relationship is weak in Nigeria, mainly attributable to its low level of financial development and the possibility of the natural resource curse emanating from the oil industry. Secondly, the evidence also strongly suggests that the nature of the relationship between the financial structure of Egypt and South Africa and per capita GDP is positive, albeit based on different measures of financial structure. In Egypt, financial structure is measured by the S-Size ratio, while, in South Africa, it is proxied by the S-Activity ratio. In Nigeria, there is no evidence suggesting that the country’s financial structure influences per capita GDP. Lastly, coefficients of the error correction term for all three countries are low, suggesting inefficiencies in the financial system and possible rigidities within the economies
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44

Wu, Chunchi, Chihwa Kao, and Cheng F. Lee. "Time-Series Properties of Financial Series and Implications for Modeling." Journal of Accounting, Auditing & Finance 11, no. 2 (April 1996): 277–303. http://dx.doi.org/10.1177/0148558x9601100207.

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This paper investigates the time-series properties of a wide range of corporate financial and accounting series. Unit root tests developed by Dickey and Fuller (1979) are applied to these series. The results support the hypothesis that most of these series contain both permanent (random walk) and transitory components. The results show that most financial series are dominated by a random walk component. However, for some series, such as net sales, net income, earnings per share, and returns on investments, there is a relatively significant stationary component, which suggests the presence of successful smoothing for these series. We show that smoothing may reduce volatility of financial series but it cannot produce a deterministic growth trend. Implications of nonstationarity for financial modeling are explored.
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45

Mykola, Kushnir, and Tokarieva Kateryna. "FINANCIAL TIME SERIES MODELLING: RETURN ON ASSETS." Technology audit and production reserves 5, no. 2 (49) (October 31, 2019): 50–55. https://doi.org/10.15587/2312-8372.2019.183868.

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<em>The paper discovers certain aspects of financial time series, in particular, modeling of return on assets. The object of research is a system of indicators for analyzing the returns of financial time series. There is a key feature that distinguishes the analysis of financial time series from the analysis of other time series, as financial theory and its empirical time series contain an element of uncertainty. As a result of this additional uncertainty, statistical theory and its methods and models play an important role in the analysis of financial time series.</em> <em>One of the most problematic places is the use of asset prices and their volatility in the analysis and forecasting of financial time series, which is false because such series contain an element of uncertainty. Therefore, the so-called return on financial assets and instruments should be used in tasks of this type.</em> <em>The paper deals with the types of return on financial assets that can be used in mathematical modeling and forecasting of stock indices. Static methods are used to eliminate the disadvantages of using financial asset prices in the analysis and forecasting of financial time series. The empirical properties of financial time series are examined using the PFTS (First Stock Trading System) and S&amp;P&nbsp;500 indices.</em> <em>A comprehensive system of indicators of time series analysis of financial assets is obtained. The proposed system involves the use of numerous methods of calculating the profitability (return) of assets in order to determine significant statistical characteristics of the data. Compared to similarly known methods of using prices (rather than profitability) of assets, this provides a key advantage that allows elements of uncertainty in financial and economic data.</em>
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46

Seemann, Lars, Jia-Chen Hua, Joseph L. McCauley, and Gemunu H. Gunaratne. "Ensemble vs. time averages in financial time series analysis." Physica A: Statistical Mechanics and its Applications 391, no. 23 (December 2012): 6024–32. http://dx.doi.org/10.1016/j.physa.2012.06.054.

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47

Sarantsev, Andrey. "IID Time Series Testing." Theory of Stochastic Processes 27(43), no. 1 (November 16, 2023): 41–52. http://dx.doi.org/10.3842/tsp-8836211480-29.

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Traditional white noise testing, for example the Ljung-Box test, studies only the autocorrelation function (ACF). Time series can be heteroscedastic and therefore not i.i.d. but still white noise (that is, with zero ACF). An example of heteroscedasticity is financial time series: times of high variance (financial crises) can alternate with times of low variance (calm times). Here, absolute values of time series terms are not white noise. We could test for white noise separately for original and absolute values, for example using Ljung-Box tests for both. In this article, we create an omnibus test which combines these two tests. Moreover, we create a general framework to create various i.i.d. tests. We apply tests to simulated data, both autoregressive linear and heteroscedastic.
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48

Wagle, Aumkar. "Deep Learning for Financial Time Series using Long Short-Term Memory Model." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 1944–72. http://dx.doi.org/10.21275/sr24418141736.

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49

Kushnir, Mykola, and Kateryna Tokarieva. "Financial time series modelling: return on assets." Technology audit and production reserves 5, no. 2(49) (October 31, 2019): 50–55. http://dx.doi.org/10.15587/2312-8372.2019.183868.

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50

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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