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Resource allocation neural network in portfolio selection
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Resource allocation neural network in portfolio selection (2007)Resource allocation neural network in por…An evolution-based approach with modularized evaluations to forecast financial distress (2006)An evolution-based approach w…Neural network techniques for financial performance prediction: integrating fundamental and technical analysis (2003)Neural network techniques for…Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks (2003)Can value-based stock selecti…An investigation of model selection criteria for neural network time series forecasting (2001)An investigation of model sel…The integrated methodology of rough set theory and artificial neural network for business failure prediction (2000)The integrated methodology of…BRICS Capital Markets Co-Movement Analysis and Forecasting (2022)BRICS Capital Markets Co-Move…A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment (2020)A Machine Learning Integrated…Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market (2019)Neural network forecasting in…Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey (2019)Evaluating the Performance of…Dynamic Portfolio Strategy Using Clustering Approach (2017)Dynamic Portfolio Strategy Us…Stock portfolio selection using Dempster–Shafer evidence theory (2016)Stock portfolio selection usi…PREDICTING NEXT TRADING DAY CLOSING PRICE OF QATAR EXCHANGE INDEX USING TECHNICAL INDICATORS AND ARTIFICIAL NEURAL NETWORKS (2014)PREDICTING NEXT TRADING DAY C…An integrated DEA-MODM methodology for portfolio optimization (2014)An integrated DEA-MODM method…Enhanced stock price variation prediction via DOE and BPNN-based optimization (2011)Enhanced stock price variatio…Mutual Fund Performance (1966)Modern Portfolio Theory and Investment Analysis. (1982)Investment Under Uncertainty. (1994)Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010)Predicting the daily return direction of the stock market using hybrid machine learning algorithms (2019)Optimizing LSTM for time series prediction in Indian stock market (2020)Empirical exchange rate models of the seventies (1983)Forecasting with artificial neural networks: (1998)Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data (1998)Using neural networks for forecasting volatility of S&P 500 Index futures prices (2003)Neural Network Forecasting of Tourism Demand (2005)Quarterly Time-Series Forecasting With Neural Networks (2007)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)ZETATM analysis A new model to identify bankruptcy risk of corporations (1977)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Forecasting financial condition of Chinese listed companies based on support vector machine (2007)Systematic review of bankruptcy prediction models: Towards a framework for tool selection (2017)Artificial intelligence for decision support systems in the field of operations research: review and future scope of research (2021)Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index (2000)Design and Analysis of Experiments (2008)The Measurement of Productive Efficiency (1957)A Simplified Model for Portfolio Analysis (1963)Measuring the efficiency of decision making units (1978)Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach (2022)Robust, extended goal programming with uncertainty sets: an application to a multi-objective portfolio selection problem leveraging DEA (2024)THE PERFORMANCE OF MUTUAL FUNDS IN THE PERIOD 1945–1964 (1968)Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency (1993)Contrarian Investment, Extrapolation, and Risk (1994)Computational intelligence approaches and linear models in case studies of forecasting exchange rates (2006)Using mixture design and neural networks to build stock selection decision support systems (2015)Neural Network Models for Stock Selection Based on Fundamental Analysis (2019)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Methodological Issues Related to the Estimation of Financial Distress Prediction Models (1984)Does ownership structure affect the degree of corporate financial distress in China? (2015)Stock price prediction using genetic algorithms and evolution strategies (2017)ESG performance and financial distress prediction of energy enterprises (2024)PORTFOLIO SELECTION* (1952)On the Impossibility of Informationally Efficient Markets (1980)On the Impossibility of Informationally Efficient Markets (1980)Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty (2018)Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context (2022)Power-Law Distributions in Empirical Data (2009)Power-law distributions in empirical data (2018)A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American integrated market (2020)Analysis of new approaches used in portfolio optimization: a systematic literature review (2020)Network Models to Enhance Automated Cryptocurrency Portfolio Management (2020)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)Risk Aversion in the Small and in the Large (1964)THE VALUATION OF RISK ASSETS AND THE SELECTION OF RISKY INVESTMENTS IN STOCK PORTFOLIOS AND CAPITAL BUDGETS**This paper is another in a series of interrelated theoretical and statistical studies of corporate financial and investment policies being made under grants from the Rockefeller Foundation, and more recently the Ford Foundation, to the Harvard Business School. The generous support for this work is most gratefully acknowledged. The author is also much indebted to his colleagues Professors Bishop, Christenson, Kahr, Raiffa, and (especially) Schlaifer, for extensive discussion and commentary on an earlier draft of this paper; but responsibility for any errors or imperfections remains strictly his own.[Professor Sharpe's paper, “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk” (Journal of Finance, September 1964) appeared after this paper was in final form and on its way to the printers. My first section, which parallels the first half of his paper (with corresponding conclusions), sets the algebraic framework for sections II, III and VI, (which have no counterpart in his paper) and for section IV on the equilibrium prices of risk assets, concerning which our results differ significantly for reasons which will be explored elsewhere. Sharpe does not take up the capital budgeting problem developed in section V below.] (1975)Cryptocurrencies chaotic co‐movement forecasting with neural networks (2020)Dissecting Tether’s Nonlinear Dynamics during Covid-19 (2020)COVID-19 Pandemic: Is the Crypto Market a Safe Haven? The Impact of the First Wave (2021)
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References

  • 2003Neural network techniques for financial performance prediction: integrating fundamental and technical analysis380 cited
  • 2000The integrated methodology of rough set theory and artificial neural network for business failure prediction335 cited
  • 2001An investigation of model selection criteria for neural network time series forecasting257 cited
  • 2006An evolution-based approach with modularized evaluations to forecast financial distress48 cited
  • 2003Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks47 cited

Cited by

  • 2017Dynamic Portfolio Strategy Using Clustering Approach72 cited
  • 2014An integrated DEA-MODM methodology for portfolio optimization40 cited
  • 2016Stock portfolio selection using Dempster–Shafer evidence theory38 cited
  • 2019Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market38 cited
  • 2011Enhanced stock price variation prediction via DOE and BPNN-based optimization31 cited
  • 2020A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment30 cited
  • 2014PREDICTING NEXT TRADING DAY CLOSING PRICE OF QATAR EXCHANGE INDEX USING TECHNICAL INDICATORS AND ARTIFICIAL NEURAL NETWORKS30 cited
  • 2019Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey29 cited

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  • 2022BRICS Capital Markets Co-Movement Analysis and Forecasting21 cited