scholarly journals ВИКОРИСТАННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧАХ ПРОГНОЗУВАННЯ ФІНАНСОВИХ ІНДЕКСІВ: ОГЛЯД НАУКОВИХ ДЖЕРЕЛ

2020 ◽  
pp. 108-117
Author(s):  
Николай Ярославович Кушнир ◽  
Катерина Токарева
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Large Scale ◽  
Scientific Literature ◽  
Financial Time Series ◽  
Technical Analysis ◽  
Soft Data ◽  
Scientific Publications ◽  
Financial Time ◽  
Stock Indices

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. The usage of well-known methods of artificial intelligence in forecasting and analysis of time series is investigated. Financial time series are inherently highly dispersed, complex, dynamic, nonlinear, nonparametric, and chaotic nature, so large-scale and soft data mining techniques should be used to predict future values. As the scientific literature superficially describes the numerous artificial intelligence algorithms to be used in forecasting financial time series, a detailed analysis of the relevant scientific literature was conducted in scientometric databases Scopus, Science Direct, Google Scholar, IEEExplore, and Springer. It is revealed that the existing scientific publications do not contain a comprehensive analysis of literature sources devoted to the use of artificial intelligence methods in forecasting stock indices. Besides, the analyzed works, which are related in detail to the object of our study, have a limited scope because they focus on only one family of artificial intelligence algorithms, namely artificial neural networks. It was found that the analysis of the use of artificial intelligence systems should be based on two well-known approaches to predicting the behavior of financial markets: fundamental and technical analysis. The first approach is based on the study of economic factors that have a possible impact on market dynamics and more common in long-term planning. Representatives of technical analysis, on the other hand, argue that the price already contains all the fundamental factors that affect it. In this regard, technical analysis involves forecasting the dynamics of price changes based on the analysis of their change in the past, ie time series. Although today there are many developed models for forecasting stock indices using artificial intelligence algorithms, in the scientific literature there is no established methodology that defines the main elements and stages of the algorithm for forecasting financial time series. Therefore, this study has improved the methodology for forecasting financial time series.

Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 64-80
Author(s):  
Shiya Wang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Financial Time Series ◽  
Special Kind ◽  
Future Trend ◽  
Financial Time ◽  
Series Analysis ◽  
Depth Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

scholarly journals An Exploratory Study of Geospace Perturbations Using Financial Analysis Tools in the Context of Complex Systems

Geosciences ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 239
Author(s):  
Pavlos I. Zitis ◽  
Stelios M. Potirakis ◽  
Georgios Balasis ◽  
Konstantinos Eftaxias
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Financial Analysis ◽  
Financial Time Series ◽  
Technical Analysis ◽  
Analysis Approach ◽  
Financial Time ◽  
Analysis Methods ◽  
Analysis Tools ◽  
High Volatility

In the frame of complex systems research, methods used to quantitatively analyze specific dynamic phenomena are often used to analyze phenomena from other disciplines on the grounds that are governed by similar dynamics. Technical analysis is considered the oldest, currently omnipresent, method for financial markets analysis, which uses past prices aiming at the possible short-term forecast of future prices. This work is the first attempt to explore the applicability of technical analysis tools on disturbance storm time (Dst) index time series, aiming at the identification of similar features between the Dst time series during magnetic storms (MSs) and asset price time series. We employ the following financial analysis tools: simple moving average (SMA), Bollinger bands, and relative strength index (RSI), formulating an analysis approach based on various features, appearing in financial time series during high volatility periods, that could be found during the different phases of the evolution of an MS (onset, main development, and recovery phase), focusing on the temporal sequence they occur. The applicability of the proposed analysis approach is examined on several MS events and the results reveal similar behavior with the financial time series in high volatility periods. We postulate that these specialized data analysis methods could be combined in the future with other statistical and complex systems time series analysis methods in order to form a useful toolbox for the study of geospace perturbations related to natural hazards.

Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

2017 ◽  
Vol 28 (02) ◽  
pp. 1750028 ◽  
Author(s):  
Yang Yujun ◽  
Li Jianping ◽  
Yang Yimei
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Stock Indices ◽  
Generalized Hurst Exponent ◽  
Detrended Fluctuation

This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

scholarly journals SEPARACJA FINANSOWYCH SZEREGÓW CZASOWYCH Z WYKORZYSTANIEM DEKORELACJI Z OPÓŹNIENIAMI

2020 ◽  
Vol 20 (4) ◽  
pp. 305-315
Author(s):  
Ryszard Szupiluk ◽  
Paweł Rubach
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Time Series ◽  
Methodological Issues ◽  
Market Data ◽  
Financial Time ◽  
Stock Indices

In this paper, we present the separation of financial time series using algorithms based on the decoralation procedure. The SOBI and AMUSE algorithms are used, tested and compared on real stock market data. We also present a discussion of theoretical and methodological issues related to the application of separation algorithms. The study is carried out using the WIG20 and SP500 stock indices.

An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting

2016 ◽  
Vol 25 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Heng-Li Yang ◽  
Han-Chou Lin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Linear Part ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Nonlinear Part ◽  
Financial Time ◽  
Stock Indices ◽  
Mode Decomposition ◽  
Original Time

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

scholarly journals Fractional Refined Composite Multiscale Fuzzy Entropy of International Stock Indices

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 914 ◽  
Author(s):  
Zhiyong Wu ◽  
Wei Zhang
Keyword(s):  
Time Series ◽  
White Noise ◽  
Financial Time Series ◽  
Gaussian White Noise ◽  
Fuzzy Entropy ◽  
Large Fluctuation ◽  
Short Term ◽  
Empirical Results ◽  
Financial Time ◽  
Stock Indices

Fractional refined composite multiscale fuzzy entropy (FRCMFE), which aims to relieve the large fluctuation of fuzzy entropy (FuzzyEn) measure and significantly discriminate different short-term financial time series with noise, is proposed to quantify the complexity dynamics of the international stock indices in the paper. To comprehend the FRCMFE, the complexity analyses of Gaussian white noise with different signal lengths, the random logarithmic returns and volatility series of the international stock indices are comparatively performed with multiscale fuzzy entropy (MFE), composite multiscale fuzzy entropy (CMFE) and refined composite multiscale fuzzy entropy (RCMFE). The empirical results show that the FRCMFE measure outperforms the traditional methods to some extent.

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