Market index price prediction using Deep Neural Networks with a Self-Similarity approach

[1]  Markus Vogl Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framework , 2023, Chaos, Solitons & Fractals.

[2]  R. Criado,et al.  The chaotic, self-similar and hierarchical patterns in Bitcoin and Ethereum price series , 2022, Chaos, Solitons & Fractals.

[3]  Chao Luo,et al.  A novel ConvLSTM with multifeature fusion for financial intelligent trading , 2022, Int. J. Intell. Syst..

[4]  Evgeniya Gospodinova Fractal Time Series Analysis by Using Entropy and Hurst Exponent , 2022, CompSysTech.

[5]  F. Baldovin,et al.  How Fast Does the Clock of Finance Run?—A Time-Definition Enforcing Stationarity and Quantifying Overnight Duration , 2021, Journal of Risk and Financial Management.

[6]  Jingyang Wang,et al.  A CNN-BiLSTM-AM method for stock price prediction , 2020, Neural Computing and Applications.

[7]  W. Schoutens,et al.  Self‐similarity in long‐horizon returns , 2020, Mathematical Finance.

[8]  Ahmet Murat Ozbayoglu,et al.  Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 , 2019, Appl. Soft Comput..

[9]  Lei Ge,et al.  Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction , 2019, Machine Learning and AI in Finance.

[10]  Ling Yang,et al.  DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction , 2019, Expert Syst. Appl..

[11]  Jian Cao,et al.  Financial time series forecasting model based on CEEMDAN and LSTM , 2019, Physica A: Statistical Mechanics and its Applications.

[12]  Xue Ying,et al.  An Overview of Overfitting and its Solutions , 2019, Journal of Physics: Conference Series.

[13]  H. Jang,et al.  Machine learning versus econometric jump models in predictability and domain adaptability of index options , 2019, Physica A: Statistical Mechanics and its Applications.

[14]  Ha Young Kim,et al.  ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module , 2018, Expert Syst. Appl..

[15]  Yang Liu,et al.  Stock Price Movement Prediction from Financial News with Deep Learning and Knowledge Graph Embedding , 2018, PKAW.

[16]  Ha Young Kim,et al.  Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models , 2018, Expert Syst. Appl..

[17]  Thomas Fischer,et al.  Deep learning with long short-term memory networks for financial market predictions , 2017, Eur. J. Oper. Res..

[18]  Chulwoo Han,et al.  Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies , 2017, Expert Syst. Appl..

[19]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

[20]  Germán Hernández,et al.  High-Frequency Trading Strategy Based on Deep Neural Networks , 2016, ICIC.

[21]  Serkan Aras,et al.  A new model selection strategy in time series forecasting with artificial neural networks: IHTS , 2016, Neurocomputing.

[22]  Sahil Shah,et al.  Predicting stock market index using fusion of machine learning techniques , 2015, Expert Syst. Appl..

[23]  Xiaotie Deng,et al.  Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information , 2014, Neurocomputing.

[24]  Aderemi Oluyinka Adewumi,et al.  Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction , 2014, J. Appl. Math..

[25]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[27]  Wei-Chang Yeh,et al.  Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..

[28]  Chin Wen Cheong,et al.  Self-similarity in financial markets: A fractionally integrated approach , 2010, Math. Comput. Model..

[29]  Peter Reinhard Hansen,et al.  The Model Confidence Set , 2010 .

[30]  D. Margaritis,et al.  Forecasting daily volatility with intraday data , 2008 .

[31]  Laurent E. Calvet,et al.  Multifractality in Asset Returns: Theory and Evidence , 2002, Review of Economics and Statistics.

[32]  Saeed Moshiri,et al.  Neural Network versus Econometric Models in Forecasting Inflation , 1999 .

[33]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[34]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[35]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[36]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[37]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[38]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..