Analysis of stock market data by using Dynamic Fourier and Wavelets techniques

Mariani, Maria C.; Bhuiyan, Al Masum; Tweneboah, Osei K.; Beccar-Varela, Maria P.; Florescu, Ionuţ

doi:10.1016/j.physa.2019.122785

Cited by 9 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al proposed a hybrid model based on wavelet transform denoising, ELM, and k-nearest neighbor regression optimization for stock prediction [ 66 ]. There are several other similar studies as well [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ]. Basically, related research has improved the effect of related time-series prediction through wavelet transform.…”

Section: Introductionmentioning

confidence: 86%

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Wang

2021

Entropy

View full text Add to dashboard Cite

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

show abstract

Section: Introductionmentioning

confidence: 86%

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Wang

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…In addition, [18], who examined whether 22 indices were active in poor form in Borsa Istanbul using Fourier unit root tests, obtained that 13 indices were active and 9 indices were inactive. [19] predict the power spectrum by analyzing daily and minute-sample financial stock market data using Dynamic Fourier and Wavelet techniques and prevent spectral leakage or discontinuity in the data.…”

Section: Methodsmentioning

confidence: 99%

Modeling the Variation between Green Energy Stock Prices and Oil Prices Using Fourier Analysis

Avci

Sarıgül

Aldemir

2023

ICSIS

View full text Add to dashboard Cite

Green energy production is crucial to protecting the environment and meeting future energydemand. Therefore, the study aims to investigate the relationship between oil price indices and green energystock prices. In this study, the data of Nasdaq OMX Wind and Nasdaq OMX Solar indices and crude oilindex (Oil) for the period of January 2015-March 2023 are collected and investigated using Fourier analysismethod. Therefore, in this study, the correlation between Oil and other (Solar and Wind) data values isexamined. Fourier power spectrum analysis method is used and according to the results of this powerspectrum analysis; The fact that there is a correlation between the Oil data values and both the Solar andWind data values is demonstrated by the average, maximum and standard deviation values obtained as aresult of the Fourier power spectrum and power spectrum. In conclusion, the findings of this study alsoprovide important recommendations for investors, managers and policy makers.

show abstract

“…According to the spectral analysis, the cycles are selected applying the decomposition of the time series into sinusoidal functions. This type of analysis enables to recognize cyclical fluctuations of different, previously unknown length [62,63]. In this study, the purpose of the spectral analysis was to identify fluctuations of certain periodicity in the dynamics of the real GDP index, which may indicate the influence of cyclical factors on the dynamics of economic development.…”

Section: Methodsmentioning

confidence: 99%

Improving Early Warning System Indicators for Crisis Manifestations in the Russian Economy

Semin

Vasiljeva²,

Sokolov

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

The study is aimed at determining the oscillators of crisis manifestations when the Russian economy tries to make transition to the path for accelerating technological development and forming an innovative economy. Short-term cycles were determined in the development of the Russian economy from 1995 to the first half of 2020 through the Fourier spectral analysis. Using the Granger test, causal relationships between the leading indicators of the economic crisis and the real GDP index in Russia were identified and substantiated. They reflect the influence of the key rate dynamics on the volume of lending, savings, investments, the yield on securities and the exchange rate; volumes of bank loans per the share of non-performing and bad loans and innovative development of the economy. Based on the constructed neural models of the oscillator influence on the level of real GDP in Russia, it was determined that the rapid growth of bank and mortgage lending, the devaluation of the ruble, a decreased volume of gross foreign investment and the level of innovative development predetermine crisis manifestations in the national economy. The lags of the influence of changes in the leading indicators of the economic crisis on the development of the economy were calculated. The results obtained can contribute to the effectiveness of the anti-crisis regulation strategy in Russia. They can serve as a basis for increasing the efficiency of long-term innovative development and creating appropriate conditions for increasing the scientific and technological potential of the country.

show abstract

Analysis of stock market data by using Dynamic Fourier and Wavelets techniques

Cited by 9 publications

References 9 publications

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Modeling the Variation between Green Energy Stock Prices and Oil Prices Using Fourier Analysis

Improving Early Warning System Indicators for Crisis Manifestations in the Russian Economy

Contact Info

Product

Resources

About