scholarly journals Information measure for financial time series: Quantifying short-term market heterogeneity

2018 ◽  
Vol 510 ◽  
pp. 132-144 ◽  
Author(s):  
Linda Ponta ◽  
Anna Carbone
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Information Measure ◽  
Short Term ◽  
Financial Time

ANALYSIS OF TIME SERIES WITH WAVELETS

2007 ◽  
Vol 05 (02) ◽  
pp. 241-256 ◽  
Author(s):  
ARMANDO CIANCIO
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Financial Time Series ◽  
Wavelet Coefficients ◽  
Analysis Method ◽  
Short Term ◽  
Financial Time ◽  
Local Characteristics ◽  
Series Analysis ◽  
Analysis Of Time Series

A financial time series analysis method based on the theory of wavelets is proposed. It is based on the transformation of data of the series in the corresponding wavelet coefficients and in the analysis of the latter, which represent the local characteristics of the series better. In particular, an algorithm for short term previsions is defined.

scholarly journals Forecasting the volatility of Financial Time Series by Tree Ensembles

2019 ◽  
Vol 12 (3) ◽  
pp. 82-89
Author(s):  
O. S. Vidmant
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Capital Investment ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Time Interval ◽  
Short Term ◽  
Computing Power ◽  
Financial Time

The use of new tools for economic data analysis in the last decade has led to significant improvements in forecasting. This is due to the relevance of the question, and the development of technologies that allow implementation of more complex models without resorting to the use of significant computing power. The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. More stringent liquidity and transparency standards in relation to the financial sector also encourage participants to experiment with protective mechanisms and to create predictive algorithms that can not only reduce the losses from the volatility of financial instruments but also benefit from short-term investment manipulations. The article discusses the possibility of improving the efficiency of calculations in predicting the volatility by the models of tree ensembles using various methods of data analysis. As the key points of efficiency growth, the author studied the possibility of aggregation of financial time series data using several methods of calculation and prediction of variance: Standard, EWMA, ARCH, GARCH, and also analyzed the possibility of simplifying the calculations while reducing the correlation between the series. The author demonstrated the application of calculation methods on the basis of an array of historical price data (Open, High, Low, Close) and volume indicators (Volumes) of futures trading on the RTS index with a five-minute time interval and an annual set of historical data. The proposed method allows to reduce the cost of computing power and time for data processing in the analysis of short-term positions in the financial markets and to identify risks with a certain level of confidence probability.

scholarly journals Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qi Tang ◽  
Ruchen Shi ◽  
Tongmei Fan ◽  
Yidan Ma ◽  
Jingyan Huang
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Financial Time Series ◽  
Singular Spectrum Analysis ◽  
Percentage Error ◽  
Medium Term ◽  
Short Term ◽  
Financial Time ◽  
Closing Price

In order to further overcome the difficulties of the existing models in dealing with the nonstationary and nonlinear characteristics of high-frequency financial time series data, especially their weak generalization ability, this paper proposes an ensemble method based on data denoising methods, including the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term short-term memory neural network (LSTM) to build a data prediction model. The financial time series is decomposed and reconstructed by WT and SSA to denoise. Under the condition of denoising, the smooth sequence with effective information is reconstructed. The smoothing sequence is introduced into LSTM and the predicted value is obtained. With the Dow Jones industrial average index (DJIA) as the research object, the closing price of the DJIA every five minutes is divided into short term (1 hour), medium term (3 hours), and long term (6 hours), respectively. Based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and absolute percentage error standard deviation (SDAPE), the experimental results show that in the short term, medium term, and long term, data denoising can greatly improve the stability of the prediction and can effectively improve the generalization ability of LSTM prediction model. As WT and SSA can extract useful information from the original sequence and avoid overfitting, the hybrid model can better grasp the sequence pattern of the closing price of the DJIA.

scholarly journals Rényi Transfer Entropy Estimators for Financial Time Series

2021 ◽  
Vol 5 (1) ◽  
pp. 33
Author(s):  
Petr Jizba ◽  
Hynek Lavička ◽  
Zlata Tabachová
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Financial Time Series ◽  
Transfer Entropy ◽  
Information Measure ◽  
Black Swan ◽  
Directional Information ◽  
Financial Time ◽  
Bare Bones ◽  
Bivariate Time Series

In this paper, we discuss the statistical coherence between financial time series in terms of Rényi’s information measure or entropy. In particular, we tackle the issue of the directional information flow between bivariate time series in terms of Rényi’s transfer entropy. The latter represents a measure of information that is transferred only between certain parts of underlying distributions. This fact is particularly relevant in financial time series, where the knowledge of “black swan” events such as spikes or sudden jumps is of key importance. To put some flesh on the bare bones, we illustrate the essential features of Rényi’s information flow on two coupled GARCH(1,1) processes.

Short-term trend prediction in financial time series data

2018 ◽  
Vol 61 (1) ◽  
pp. 397-429
Author(s):  
Mustafa Onur Özorhan ◽  
İsmail Hakkı Toroslu ◽  
Onur Tolga Şehitoğlu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Term Trend ◽  
Financial Time ◽  
Short Term Trend

SHORT-TERM FINANCIAL TIME SERIES ANALYSIS WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

2021 ◽  
Vol 3 (4) ◽  
pp. 165-177
Author(s):  
M. V. Labusov ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Frequency ◽  
Short Term Memory ◽  
Financial Time Series ◽  
Short Term ◽  
Term Memory ◽  
Financial Time ◽  
Long Short Term Memory ◽  
In The Beginning

The process of creating a long short-term memory neural network for high-frequency financial time series analyzing and forecasting is considered in the article. The research base is compiled in the beginning. Further the estimation of long short-term memory neural network parameters is carried out on the learning subsamples. The forecast of future returns signs is made for the horizon of 90 minutes with the estimated neural network. In conclusion the trading strategy is formulated.

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