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The Decision Tree Approach to Stock Selection
1 references·9 citing·32 second hop
The Decision Tree Approach to Stock Selection (2000)The Decision Tree Approach to Stock Selec…Risk, Return, and Equilibrium: Empirical Tests (1973)Risk, Return, and Equilibrium…Growing the efficient frontier on panel trees (2025)Growing the efficient frontie…Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators? (2022)Can Ensemble Machine Learning…Building portfolios based on machine learning predictions (2021)Building portfolios based on …Machine Learning-Based Financial Statement Analysis (2020)Machine Learning-Based Financ…A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets (2018)A Robust Regression-Based Sto…Using mixture design and neural networks to build stock selection decision support systems (2015)Using mixture design and neur…The benefits of tree-based models for stock selection (2012)The benefits of tree-based mo…Trend discovery in financial time series data using a case based fuzzy decision tree (2010)Trend discovery in financial …Mortgage Default: Classification Trees Analysis (2005)Mortgage Default: Classificat…Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Multicriteria classification and sorting methods: A literature review (2002)Financial time series forecasting using support vector machines (2003)Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms (2014)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)The arbitrage theory of capital asset pricing (1976)Financial Trading Strategy System Based on Machine Learning (2020)The Behavior of Stock-Market Prices (1965)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)On Persistence in Mutual Fund Performance (1997)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency (1993)An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study (2019)Deep Learning in Stock Portfolio Selection and Predictions (2022)Optimizing investment portfolios with a sequential ensemble of decision tree-based models and the FBI algorithm for efficient financial analysis (2024)A fusion model of HMM, ANN and GA for stock market forecasting (2006)A Bayesian regularized artificial neural network for stock market forecasting (2013)Forecasting daily stock market return using dimensionality reduction (2016)The Cross-Section of Expected Stock Returns (1992)Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress (1985)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options (2000)Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques (2013)Evaluation of individuals’ intention to pay a premium price for ecotourism: An exploratory study (2016)Forecasting Loan Default in Europe with Machine Learning (2020)A five-factor asset pricing model (2014)Stock market and securities index prediction using artificial intelligence: A systematic review (2024)Common risk factors in the returns on stocks and bonds (1993)PORTFOLIO SELECTION* (1952)(Generative) AI in Financial Economics (2025)
this work references cited by second hop

References

  • 1973Risk, Return, and Equilibrium: Empirical Tests14,248 cited

Cited by

  • 2005Mortgage Default: Classification Trees Analysis77 cited
  • 2010Trend discovery in financial time series data using a case based fuzzy decision tree54 cited
  • 2015Using mixture design and neural networks to build stock selection decision support systems47 cited
  • 2021Building portfolios based on machine learning predictions41 cited
  • 2018A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets31 cited
  • 2022Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?28 cited
  • 2020Machine Learning-Based Financial Statement Analysis27 cited
  • 2025Growing the efficient frontier on panel trees25 cited
  • 2012The benefits of tree-based models for stock selection19 cited

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