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A new LSTM based reversal point prediction method using upward/downward reversal point feature sets
16 references·5 citing·41 second hop
A new LSTM based reversal point prediction method using upward/downward reversal point feature sets (2019)A new LSTM based reversal point predictio…NSE Stock Market Prediction Using Deep-Learning Models (2018)NSE Stock Market Prediction U…Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)Forecasting the volatility of…An innovative neural network approach for stock market prediction (2018)An innovative neural network …Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Stock price prediction using …Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017)Forecasting Stock Prices from…Deep learning with long short-term memory networks for financial market predictions (2017)Deep learning with long short…A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017)A deep learning framework for…A LSTM-based method for stock returns prediction: A case study of China stock market (2015)A LSTM-based method for stock…Stock Price Prediction Using the ARIMA Model (2014)Stock Price Prediction Using …The use of data mining and neural networks for forecasting stock market returns (2005)The use of data mining and ne…Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (2000)Foundations of Technical Anal…Simple Technical Trading Rules and the Stochastic Properties of Stock Returns (1992)Simple Technical Trading Rule…Some anomalous evidence regarding market efficiency (1978)Some anomalous evidence regar…EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)EFFICIENT CAPITAL MARKETS: A …Efficient Capital Markets: A Review of Theory and Empirical Work (1970)Efficient Capital Markets: A …Filter Rules and Stock-Market Trading (1966)Filter Rules and Stock-Market…An improved DenseNet model for prediction of stock market using stock technical indicators (2023)An improved DenseNet model fo…Stock index futures price prediction using feature selection and deep learning (2022)Stock index futures price pre…A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning (2022)A novel two-stage method for …Prediction of Bitcoin price based on manipulating distribution strategy (2021)Prediction of Bitcoin price b…Improving Stock Prediction Accuracy Using CNN and LSTM (2020)Improving Stock Prediction Ac…The Behavior of Stock-Market Prices (1965)An Anatomy of Trading Strategies (1998)Using genetic algorithms to find technical trading rules (1999)The illusory nature of momentum profits (2003)Does the Stock Market Overreact? (1985)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Market underreaction to open market share repurchases (1995)Do Investors Trade Too Much? (1999)Improved Methods for Tests of Long‐Run Abnormal Stock Returns (1999)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets (1965)ON THE PRICING OF CORPORATE DEBT: THE RISK STRUCTURE OF INTEREST RATES* (1974)Theory of the firm: Managerial behavior, agency costs and ownership structure (1976)Users of the world, unite! 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(1971)Time Series Analysis: Forecasting and Control (1977)25 years of time series forecasting (2006)Stock Closing Price Prediction using Machine Learning Techniques (2020)Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test (1988)Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency (1993)The Econometrics of Financial Markets (1997)WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS? (2007)Forecasting Stock Returns (2013)Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014)Dividend Policy, Growth, and the Valuation of Shares (1961)The Structure of Scientific Revolutions (1963)A critique of the asset pricing theory's tests Part I: On past and potential testability of the theory (1977)The Noise Trader Approach to Finance (1990)Consumer Reaction to Measures of Poor Quality: Evidence from the Mutual Fund Industry (1992)Inefficient Markets (2000)A systematic review of fundamental and technical analysis of stock market predictions (2019)Stock Market Analysis: A Review and Taxonomy of Prediction Techniques (2019)A CNN-LSTM-Based Model to Forecast Stock Prices (2020)
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References

  • 1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*15,723 cited
  • 1970Efficient Capital Markets: A Review of Theory and Empirical Work13,616 cited
  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 1992Simple Technical Trading Rules and the Stochastic Properties of Stock Returns2,298 cited
  • 1978Some anomalous evidence regarding market efficiency1,419 cited
  • 2014Stock Price Prediction Using the ARIMA Model1,153 cited
  • 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory1,131 cited
  • 2000Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation1,118 cited
  • 2017Stock price prediction using LSTM, RNN and CNN-sliding window model1,096 cited
  • 1966Filter Rules and Stock-Market Trading832 cited
  • 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models786 cited
  • 2015A LSTM-based method for stock returns prediction: A case study of China stock market734 cited
  • 2018NSE Stock Market Prediction Using Deep-Learning Models615 cited
  • 2018An innovative neural network approach for stock market prediction476 cited

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  • 2005The use of data mining and neural networks for forecasting stock market returns465 cited
  • 2017Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks394 cited
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