scholarly journals Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shambhavi Mishra ◽  
Tanveer Ahmed ◽  
Vipul Mishra ◽  
Manjit Kaur ◽  
Thomas Martinetz ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Stock Prices ◽  
High Frequency ◽  
Time Windows ◽  
Stock Index ◽  
Level Data ◽  
Traditional Classification ◽  
Regression Problems ◽  
The Common ◽  
Online Prediction

This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

Prior Return Effect in Indian Stock Market: An Intra-day Analysis

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Vanita Tripathi ◽  
Shalini Aggarwal
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
High Frequency ◽  
Good News ◽  
High Frequency Data ◽  
Short Term ◽  
Fund Managers ◽  
News Events ◽  
Return Effect ◽  
Indian Stock Market

In a first of this kind, this paper examines the issue of prior return effect in Indian stock market in intra-day analysis using high frequency data. We document that in Indian stock market, security returns exhibit a reversal in their direction within few minutes of extreme price rises as well as price falls. However the speed with which the correction takes place is slightly different for good news events and bad news events. Indian investors tend to be optimistic as they immediately bring stock prices up following unjustified price falls but take time to bring stock prices down following unjustified price rises. These findings lend a further support to short-term overreaction literature. More importantly, these findings serve as a proof of predictability of the direction of future stock prices and consequent returns on an intra-day basis. It forwards important investment implications for traders, fund managers, and investors at large.

scholarly journals Frequency Selective Auto-Encoder for Smart Meter Data Compression

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1521
Author(s):  
Jihoon Lee ◽  
Seungwook Yoon ◽  
Euiseok Hwang
Keyword(s):  
Data Compression ◽  
High Frequency ◽  
Spectral Properties ◽  
Situational Awareness ◽  
Time Windows ◽  
Low Frequency ◽  
Smart Meters ◽  
Compression Performance ◽  
Reconstruction Performance ◽  
Frequency Selective

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

The Effect of Combined Loads on the Destruction of Rocks with a Granular Structure

2021 ◽  
Vol 1022 ◽  
pp. 1-6
Author(s):  
Victoria S. Romanova ◽  
Viktor V. Gabov
Keyword(s):  
Ultimate Strength ◽  
Experimental Research ◽  
High Frequency ◽  
Granular Structure ◽  
Common Method ◽  
Crushed Material ◽  
Combined Loads ◽  
Rock Disintegration ◽  
The Common ◽  
Destruction Of Rocks

The article addresses the features of rock disintegration based on the principles of selective and preferential destruction in high-frequency cone vibratory crushers with a free-turning inner cone. Based on the common method for determining the ultimate strength of rocks, a method for investigating the process of ore destruction under repeated and versatile influences has been proposed depending on the structure of the crushed material. The results of an experimental research of the destruction of rock samples on a press with limited force are given.

Polarization analysis of high-frequency, three-component seismic data

1991 ◽  
Vol 81 (2) ◽  
pp. 622-642
Author(s):  
K. Bataille ◽  
J. M. Chiu
Keyword(s):  
High Frequency ◽  
Time Windows ◽  
Random Noise ◽  
Polarization State ◽  
Principal Component ◽  
Small Time ◽  
Body Waves ◽  
Local Data ◽  
Principal Component Approach ◽  
Component Approach

Abstract We present a method to determine the polarization of body waves from three-component, high-frequency data and examples of its application. The method is based on the principal component approach. One advantage of this approach is that the polarization state can be determined for small time windows compared with the predominant period of the wave. This is particularly useful for identifying converted waves within the crust. The stability of the result is analyzed with synthetic cases by adding simultaneous arrivals from waves and random noise. The method works well with both synthetic and local data in the detection of the polarization of the wave by separating arrivals from different directions. From the local data, some seismic phases related to crustal conversions are observed that require strong lateral variations.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

scholarly journals Microscale dynamics of electrophysiological markers of epilepsy

2020 ◽  
Author(s):  
Jimmy C. Yang ◽  
Angelique C. Paulk ◽  
Sang Heon Lee ◽  
Mehran Ganji ◽  
Daniel J. Soper ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Frequency ◽  
Time Windows ◽  
List Type ◽  
Spatiotemporal Resolution ◽  
Similar Time ◽  
Human Epilepsy ◽  
High Frequency Oscillations ◽  
Peak Tracking ◽  
Interictal Discharges

AbstractObjectiveInterictal discharges (IIDs) and high frequency oscillations (HFOs) are neurophysiologic biomarkers of epilepsy. In this study, we use custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand their microscale dynamics.MethodsElectrodes with spatial resolution down to 50µm were used to record intraoperatively in 30 subjects. For IIDs, putative spatiotemporal paths were generated by peak-tracking, followed by clustering. For HFOs, repeating patterns were elucidated by clustering similar time windows. Fast events, consistent with multi-unit activity (MUA), were covaried with either IIDs or HFOs.ResultsIIDs seen across the entire array were detected in 93% of subjects. Local IIDs, observed across <50% of the array, were seen in 53% of subjects. IIDs appeared to travel across the array in specific paths, and HFOs appeared in similar repeated spatial patterns. Finally, microseizure events were identified spanning 50-100µm. HFOs covaried with MUA, but not with IIDs.ConclusionsOverall, these data suggest micro-domains of irritable cortex that form part of an underlying pathologic architecture that contributes to the seizure network.SignificanceMicroelectrodes in cases of human epilepsy can reveal dynamics that are not seen by conventional electrocorticography and point to new possibilities for their use in the diagnosis and treatment of epilepsy.HighlightsPEDOT:PSS microelectrodes with at least 50µm spatial resolution uniquely reveal spatiotemporal patterns of markers of epilepsyHigh spatiotemporal resolution allows interictal discharges to be tracked and reveal cortical domains involved in microseizuresHigh frequency oscillations detected by microelectrodes demonstrate localized clustering on the cortical surface

scholarly journals Factor analysis of the Lithuanian equity market indices

2009 ◽  
Vol 50 ◽  
Author(s):  
Svetlana Danilenko
Keyword(s):  
Factor Analysis ◽  
Stock Market ◽  
Stock Prices ◽  
Equity Market ◽  
Stock Index ◽  
Change Dynamics ◽  
Statistical Measures ◽  
Components Method ◽  
Main Components ◽  
Stock Indexes

Statistical measures that can reproduce the state of the stock market and the tendencies of its change dynamics are the stock indexes. Having in mind the more complicated state of the finance system it is important to answer the question of what impacts the fluctuations of the stock prices. The article discusses various factors that impact the fluctuations of the Lithuanian stock index OMXV ; also stock index factor analysis is performed. Factors are determined using the main components method.

scholarly journals FAKTOR PENGARUH RASIO KEUANGAN TERHADAP HARGA SAHAM PERUSAHAAN JAKARTA ISLAMIC INDEX (JII)

2021 ◽  
Vol 12 (1) ◽  
pp. 42-55
Author(s):  
Nadiah Ayu Salsabila ◽  
Titis Miranti
Keyword(s):  
Stock Prices ◽  
Stock Exchange ◽  
Secondary Data ◽  
Profit Margin ◽  
Stock Index ◽  
Financial Ratios ◽  
Analysis Model ◽  
Current Ratio ◽  
Net Profit ◽  
Equity Ratio

Jakarta Islamic Index is a stock index in the IDX that can use as an alternative In Islamic investment. In choosing an investment object in Islamic stocks, it necessary to pay attention to the financial ratios and stock prices of companies. The purpose of this study was to determine the effect of financial ratios on stock prices on companies listed on the Jakarta Islamic Index (JII). The type of this research is quantitative. The population of 56 companies registered on the Jakarta Islamic Index (JII) for the 2012-2018 period with a sample of 11 companies. The analysis model use panel data regression using Eviews software. The type of data uses secondary data accessed through the Indonesia Stock Exchange (IDX) website. The results showed that earning per share variable has a significant effect on stock prices. While the current ratio, debt to equity ratio, total assets turnover and net profit margin variables have no significant impact on stock prices. Simultaneously variables of current ratio, debt to equity ratio, total assets turnover, net profit margin and earning per share have significant effects on stock prices. The contribution of this research can use as a reference for companies to pay attention to financial ratios that affect stock prices.

The Common Application and Student Choice

2021 ◽  
Vol 111 ◽  
pp. 460-464
Author(s):  
Brian Knight ◽  
Nathan Schiff
Keyword(s):  
Student Choice ◽  
Single Application ◽  
Individual Level ◽  
Level Data ◽  
First Choice ◽  
The Common ◽  
Common Application ◽  
Over Time

We study the effects of the Common Application (CA) platform, which allows students to submit a single application to multiple institutions, on student choice. Using individual-level data from freshman surveys over the period 1982-2014, we develop two proxies for student choice, one based upon the number of applications submitted and another based upon students attending non-first-choice institutions. Using these proxies, we first document sharp increases in student choice over time. Linking these outcomes to the timing of CA membership, we provide evidence of a link between CA entry and increased student choice.

Analisis Pengaruh Inflasi, Nilai Tukar Rupiah, dan BI-Rate terhadap Harga Indeks Saham Syariah Indonesia

2021 ◽  
Vol 4 (2) ◽  
pp. 47-56
Author(s):  
Fifi Afiyanti Tripuspitorini
Keyword(s):  
Exchange Rate ◽  
Interest Rates ◽  
Stock Prices ◽  
Stock Price ◽  
Partial Least Square ◽  
Least Square ◽  
Stock Index ◽  
Macroeconomic Factors ◽  
Negative Effect ◽  
Monthly Period

Islamic investment is experiencing an upward trend from year to year. Many investors are starting to look at Islamic stocks. One of the Islamic stocks in Indonesia is the Indonesian Sharia Stock Index (ISSI). Investors must have many careful considerations to invest. One of the factors that may influence stock prices is macroeconomic factors. This study aims to determine how macroeconomic variables in the form of inflation, the rupiah exchange rate against the dollar, and Bank Indonesia interest rates can affect the ISSI stock price. This study uses a quantitative data approach. The data is obtained from the Sharia Stock Index (ISSI) in the monthly period January 2016 to December 2018.Meanwhile, data analysis used Partial Least Square (PLS) with the help of WarpPLS. The results showed that inflation and the rupiah exchange rate had no effect on the ISSI stock price. while the BI rate has a significant negative effect on the ISSI stock price.

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