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Stock market prediction and Portfolio selection models: a survey
16 references·2 citing·34 second hop
Stock market prediction and Portfolio selection models: a survey (2017)Stock market prediction and Portfolio sel…Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques (2014)Predicting stock and stock pr…An artificial neural network (p,d,q) model for timeseries forecasting (2009)An artificial neural network …Forecasting stock market movement direction with support vector machine (2004)Forecasting stock market move…A hybrid ARIMA and support vector machines model in stock price forecasting (2004)A hybrid ARIMA and support ve…Time series forecasting using a hybrid ARIMA and neural network model (2003)Time series forecasting using…Conditional value-at-risk for general loss distributions (2002)Conditional value-at-risk for…Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index (2000)Genetic algorithms approach t…The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice (1993)The Effect of Errors in Means…On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results (1991)On the Sensitivity of Mean-Va…Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market (1991)Mean-Absolute Deviation Portf…Economic prediction using neural networks: the case of IBM daily stock returns (1988)Economic prediction using neu…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Time Series Analysis Forecasting and Control. (1971)Time Series Analysis Forecast…Portfolio Selection: Efficient Diversification of Investments. (1962)Portfolio Selection: Efficien…Portfolio Selection: Efficient Diversification of Investments (1959)Portfolio Selection: Efficien…Safety First and the Holding of Assets (1952)Safety First and the Holding …GCNET: Graph-based prediction of stock price movement using graph convolutional network (2022)GCNET: Graph-based prediction…A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices (2018)A hybrid supervised semi-supe…EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Financial time series forecasting using support vector machines (2003)Evaluating multiple classifiers for stock price direction prediction (2015)Sentiment analysis on social media for stock movement prediction (2015)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)Modern Portfolio Theory and Investment Analysis. (1982)From stochastic dominance to mean-risk models: Semideviations as risk measures (1999)Heuristics for cardinality constrained portfolio optimisation (2000)Portfolio optimization with conditional value-at-risk objective and constraints (2001)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting with artificial neural networks: (1998)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)PORTFOLIO SELECTION* (1952)Coherent Measures of Risk (1999)Optimization of conditional value-at-risk (2000)On the coherence of expected shortfall (2002)Spectral measures of risk: A coherent representation of subjective risk aversion (2002)Making and Evaluating Point Forecasts (2011)On the Measurement of Poverty (1987)An Engine, Not a Camera (2006)Bank activity and funding strategies: The impact on risk and returns☆ (2010)Risk, Return, and Equilibrium: Empirical Tests (1973)The Cross-Section of Expected Stock Returns (1992)Multifactor Explanations of Asset Pricing Anomalies (1996)Robust Portfolio Selection Problems (2003)Optimal Portfolio Choice with Parameter Uncertainty (2007)A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms (2009)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Generalized autoregressive conditional heteroskedasticity (1986)Some recent development in a concept of causality (1988)Unit root tests in panel data: asymptotic and finite-sample properties (2002)
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References

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  • 1959Portfolio Selection: Efficient Diversification of Investments5,535 cited
  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
  • 2002Conditional value-at-risk for general loss distributions3,847 cited
  • 1962Portfolio Selection: Efficient Diversification of Investments.3,621 cited
  • 1952Safety First and the Holding of Assets2,537 cited
  • 1991Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market1,516 cited
  • 1993The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice1,158 cited
  • 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques1,139 cited
  • 2004Forecasting stock market movement direction with support vector machine1,119 cited
  • 2004A hybrid ARIMA and support vector machines model in stock price forecasting1,000 cited
  • 1991On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results999 cited
  • 2009An artificial neural network (p,d,q) model for timeseries forecasting785 cited

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  • 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index758 cited
  • 1988Economic prediction using neural networks: the case of IBM daily stock returns698 cited
  • Cited by

    • 2022GCNET: Graph-based prediction of stock price movement using graph convolutional network59 cited
    • 2018A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices42 cited