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Price change prediction of Ultra high frequency financial data based on temporal convolutional network
9 references·1 citing·52 second hop
Price change prediction of Ultra high frequency financial data based on temporal convolutional network (2022)Price change prediction of Ultra high fre…Portfolio optimization with return prediction using deep learning and machine learning (2020)Portfolio optimization with r…Gold volatility prediction using a CNN-LSTM approach (2020)Gold volatility prediction us…Forecasting daily stock trend using multi-filter feature selection and deep learning (2020)Forecasting daily stock trend…Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data (2018)Temporal Bag-of-Features Lear…Deep learning models for bankruptcy prediction using textual disclosures (2018)Deep learning models for bank…Deep learning with long short-term memory networks for financial market predictions (2017)Deep learning with long short…Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Deep learning networks for st…Modelling high-frequency limit order book dynamics with support vector machines (2015)Modelling high-frequency limi…Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest (2006)Forecasting Stock Index Movem…A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment (2025)A Dual Output Temporal Convol…Price Dynamics in Limit Order Markets (1998)Order flow composition and trading costs in a dynamic limit order market (1999)Limit Order Book as a Market for Liquidity (2005)Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods (2018)Deep learning for limit order books (2018)Stock Prediction Using Convolutional Neural Network (2018)Dividend yields and expected stock returns (1988)Business conditions and expected returns on stocks and bonds (1989)The Cross-Section of Expected Stock Returns (1992)Prediction of Stock Market Index Movement by Ten Data Mining Techniques (2009)Forecasting price movements using technical indicators: Investigating the impact of varying input window length (2017)Variable Selection in Time Series Forecasting Using Random Forests (2017)Does the Stock Market Overreact? (1985)Hierarchical structure in financial markets (1999)Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2007)A systematic review of fundamental and technical analysis of stock market predictions (2019)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)Applications of deep learning in stock market prediction: Recent progress (2021)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)On Persistence in Mutual Fund Performance (1997)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)A Coefficient of Agreement for Nominal Scales (1960)Support vector machine with adaptive parameters in financial time series forecasting (2003)Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017)Deep Reinforcement Learning for Financial Trading Using Price Trailing (2019)Exploiting intra-day patterns for market shock prediction: A machine learning approach (2019)Research on Quantitative Investment Strategies Based on Deep Learning (2019)Financial Ratios As Predictors of Failure (1966)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks (2011)CatBoost model and artificial intelligence techniques for corporate failure prediction (2021)Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering (2022)Financial applications of machine learning: A literature review (2023)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold (2010)An Advanced CNN-LSTM Model for Cryptocurrency Forecasting (2021)Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)Stock Price Prediction Using CNN-BiLSTM-Attention Model (2023)Portfolio Selection: Efficient Diversification of Investments (1959)Optimization of conditional value-at-risk (2000)Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform (2022)Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets (2023)Financial time series forecasting using support vector machines (2003)Stock market index prediction based on reservoir computing models (2021)The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality (2021)An adaptive feature selection schema using improved technical indicators for predicting stock price movements (2022)Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction (2014)Stock Price Prediction Using the ARIMA Model (2014)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)
this work references cited by second hop

References

  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies833 cited
  • 2018Deep learning models for bankruptcy prediction using textual disclosures480 cited
  • 2020Portfolio optimization with return prediction using deep learning and machine learning299 cited
  • 2020Gold volatility prediction using a CNN-LSTM approach274 cited
  • 2006Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest244 cited
  • 2020Forecasting daily stock trend using multi-filter feature selection and deep learning166 cited
  • 2015Modelling high-frequency limit order book dynamics with support vector machines145 cited
  • 2018Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data49 cited

Cited by

  • 2025A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment35 cited

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