Communications of the ACM
Multilayer feedforward networks are universal approximators
Neural Networks
Automated knowledge acquisition
Automated knowledge acquisition
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Generalized Analytic Rule Extraction for Feedforward Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Stock market trading rule discovery using pattern recognition and technical analysis
Expert Systems with Applications: An International Journal
A knowledge-based decision support system for measuring enterprise performance
Knowledge-Based Systems
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Back propagation networks for credit card fraud prediction using stratified personalized data
ISP'06 Proceedings of the 5th WSEAS International Conference on Information Security and Privacy
Resource allocation neural network in portfolio selection
Expert Systems with Applications: An International Journal
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Predicting financial activity with evolutionary fuzzy case-based reasoning
Expert Systems with Applications: An International Journal
Option valuation based on the neural regression model
Expert Systems with Applications: An International Journal
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
A binary classification method for bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
A portfolio optimization model using Genetic Network Programming with control nodes
Expert Systems with Applications: An International Journal
Closed loop knowledge discovery for decision support in intensive care medicine
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Constructing portfolio investment strategy based on time adapting genetic network programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A model of portfolio optimization using time adapting genetic network programming
Computers and Operations Research
Multidimensional decision support indicator (mDSI) for time series stock trend prediction
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Using fast adaptive neural network classifier for mutual fund performance evaluation
Expert Systems with Applications: An International Journal
An intelligent computing algorithm to analyze bank stock returns
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Applying text and data mining techniques to forecasting the trend of petitions filed to e-People
Expert Systems with Applications: An International Journal
Evaluation approach to stock trading system using evolutionary computation
Expert Systems with Applications: An International Journal
Genetic relation algorithm with guided mutation for the large-scale portfolio optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evaluation of neural network variable influence measures for process control
Engineering Applications of Artificial Intelligence
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
Improving financial data quality using ontologies
Decision Support Systems
Algorithmic determination of the maximum possible earnings for investment strategies
Decision Support Systems
A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading
International Journal of Fuzzy System Applications
A Technical Analysis Indicator Based On Fuzzy Logic
Electronic Notes in Theoretical Computer Science (ENTCS)
Measuring firm performance using financial ratios: A decision tree approach
Expert Systems with Applications: An International Journal
Combining VIKOR-DANP model for glamor stock selection and stock performance improvement
Knowledge-Based Systems
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This research project investigates the ability of neural networks, specifically, the backpropagation algorithm, to integrate fundamental and technical analysis for financial performance prediction. The predictor attributes include 16 financial statement variables and 11 macroeconomic variables. The rate of return on common shareholders' equity is used as the to-be-predicted variable. Financial data of 364 S&P companies are extracted from the CompuStat database, and macroeconomic variables are extracted from the Citibase database for the study period of 1985-1995. Used as predictors in Experiments 1, 2, and 3 are the 1 year's, the 2 years', and the 3 years' financial data, respectively. Experiment 4 has 3 years' financial data and macroeconomic data as predictors. Moreover, in order to compensate for data noise and parameter misspecification as well as to reveal prediction logic and procedure, we apply a rule extraction technique to convert the connection weights from trained neural networks to symbolic classification rules. The performance of neural networks is compared with the average return from the top one-third returns in the market (maximum benchmark) that approximates the return from perfect information as well as with the overall market average return (minimum benchmark) that approximates the return from highly diversified portfolios. Paired t tests are carried out to calculate the statistical significance of mean differences. Experimental results indicate that neural networks using 1 year's or multiple years' financial data consistently and significantly outperform the minimum benchmark, but not the maximum benchmark. As for neural networks with both financial and macroeconomic predictors, they do not outperform the minimum or maximum benchmark in this study. The experimental results also show that the average return of 0.25398 from extracted rules is the only compatible result to the maximum benchmark of 0.2786. Consequentially, we demonstrate rule extraction as a postprocessing technique for improving prediction accuracy and for explaining the prediction logic to financial decision makers.