A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
Prediction based – high frequency trading on financial time series
2012
Periodica Polytechnica Electrical Engineering
Preserved Fulltext
In this paper we investigate prediction based trading on financial time series assuming general AR(J) models. ...
FFNNs can provide fast prediction which can give rise to high frequency trading on intraday data series. ...
, ask price y
János Levendovszky / Farhad Kia
Prediction based -high frequency trading on financial time series ...
doi:10.3311/ppee.7165
fatcat:irbsmbulazbtpg2wd5cmnollo4
Prediction based – High Frequency Trading on Financial Time Series
english
2013
Proceedings of the 5th International Joint Conference on Computational Intelligence
english
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper we investigate prediction based trading on financial time series assuming general AR(J) models. ...
FFNNs can provide fast prediction which can give rise to high frequency trading on intraday data series. ...
, ask price y
János Levendovszky / Farhad Kia
Prediction based -high frequency trading on financial time series ...
doi:10.5220/0004555005020506
dblp:conf/ijcci/KiaL13
fatcat:5r7zzbvzxre6rdg2unyctgy7ra
Deep Learning Algorithm-Based Financial Prediction Models
2021
Complexity
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model's prediction of internal financial time series ...
The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, ...
Financial time series analysis deals with discrete time series in most cases, which have multiscale spatial characteristics depending on the sampling frequency of the data. e raw time series data can be ...
doi:10.1155/2021/5560886
fatcat:drllpsqrbfckrfuuc75kbbspra
CNN based Stock Market Prediction
2020
International Journal of Engineering and Advanced Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. ...
In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. ...
Author proposed a deep learning approach based on CNN that forecasts the stock price variations using as large and high frequency time series data obtained from the order book of stock exchanges in [9 ...
doi:10.35940/ijeat.c5282.029320
fatcat:guge42oeyrhobgqwkbu7kvfija
Financial Market Sequence Prediction Based on Image Processing
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The prediction model of financial series data based on CNN-GRU neural network is more effective [18] .
D. ...
On the exploration of financial market series forecasting method, Liu J P proposed a comprehensive carbon price forecasting method based on multiple unstructured data. ...
doi:10.1109/access.2020.3020062
fatcat:gt4dopwusrgsxhbjipipmf2gkm
Green Bond Index Prediction Based on CEEMDAN-LSTM
2022
Frontiers in Energy Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, a hybrid model combining CEEMDAN and LSTM is introduced to predict green bond market in China (represented by CUFE-CNI High Grade Green Bond Index). ...
Meanwhile, we find that indices from the crude oil market and green stock market are both effective predictors, which also provides ground on the correlations between the green bond market and other financial ...
Considering the long time-span and the high volatility characteristics of financial time series, we choose the widely used method, LSTM, to predict the green bond index. ...
doi:10.3389/fenrg.2021.793413
fatcat:5pdq4ahnhbfuhna3lak2567oxm
BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
BERT developed by Google, especially for three actively trading individual stocks in Hong Kong market with at the same time the hot discussion on Weibo.com. ...
On the one hand, we demonstrate a significant enhancement of applying BERT in financial sentiment analysis when compared with the existing models. ...
Then, all 's time-series data form our BERT-based financial sentiment index for stock . ...
arXiv:1906.09024v2
fatcat:qoyct4zeyzgb3l2wu32qu2eia4
Financial Market Prediction and Simulation Based on the FEPA Model
2021
Journal of Mathematics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This model is mainly composed of three components, which are based on financial time series special empirical mode decomposition (FTA-EMD), principal component analysis (PCA), and artificial neural network ...
This model is mainly used to model and predict the complex financial time series. ...
trading strategy (VWAP), volume fixed percentage algorithm trading strategy (VP), time weighted average price algorithm trading strategy (TWAP), etc. ese high-frequency trading algorithms have been widely ...
doi:10.1155/2021/5955375
fatcat:f6z6ucw7xnerbf3a4sw5euffey
Supervised classification-based stock prediction and portfolio optimization
[article]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Our approaches are based on the supervision of prediction parameters using company fundamentals, time-series properties, and correlation information between different stocks. ...
The portfolio our system suggests by predicting the behavior of stocks results in a 3% larger growth on average than the overall market within a 3-month time period, as the out-of-sample test suggests. ...
Machine learning has already attained an important place in trading and finance. One currently major area is high-frequency trading. ...
arXiv:1406.0824v1
fatcat:nkkp4jalu5g4zitvovcvmew4he
Wavelet-based prediction of oil prices
2005
Chaos, Solitons & Fractals
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. ...
The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. ...
Introduction There are two types of trade in oil markets namely one that is based on immediate delivery and one that is based on delivery at some future point in time. ...
doi:10.1016/j.chaos.2004.11.015
fatcat:l74wua4w5rfjhml7onfieg7lci
Price change prediction of ultra high frequency financial data based on temporal convolutional network
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper. ...
Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. ...
Ref [7] proposed a novel time-aware neural feature bag model (bag-of-features, BoF), which is suitable for time series forecasting using high-frequency limit order data. ...
arXiv:2107.00261v1
fatcat:gwoih4iorbg4nfw4dgyyybaspe
LSTM Based Sentiment Analysis for Cryptocurrency Prediction
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. ...
the historical cryptocurrency price movement to predict the price trend for future time frames. ...
We compare our method with the time series auto regression (AR) approach [2] to evaluate the performance. ...
arXiv:2103.14804v4
fatcat:f6i5bx6o25ewfl6reuhrfsdbiu
Lagged correlation-based deep learning for directional trend change prediction in financial time series
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
based on such correlations with other series. ...
Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict ...
decision-making and the implementation of automated decision-making based on these notions, especially in high frequency trading. ...
arXiv:1811.11287v1
fatcat:e6qqa2yhfrfwjcqdmlewqn2bou
Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading
2022
Financial Innovation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
on the market. ...
AbstractIn recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. ...
algorithmic trading based on the predictions. ...
doi:10.1186/s40854-022-00336-7
fatcat:2xxuy7lxa5c27brnphvjbjaf4a
Context Based Predictive Information
2019
Entropy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. ...
We also provide an implementation of the CBPI algorithm on a real dataset of large banks' stock prices in the U.S. ...
change respectively in the financial time series. ...
doi:10.3390/e21070645
pmid:33267359
pmcid:PMC7515138
fatcat:iafgwsciqbdwtdapxa7mno6tba
« Previous
Showing results 1 — 15 out of 99,342 results