Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
A knowledge-based decision support system for measuring enterprise performance
Knowledge-Based Systems
Fuzzy aggregation and averaging for group decision making: A generalization and survey
Knowledge-Based Systems
An automated FX trading system using adaptive reinforcement learning
Expert Systems with Applications: An International Journal
Designing a knowledge-based system for benchmarking: A DEA approach
Knowledge-Based Systems
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example
Knowledge-Based Systems
Evaluation of stock trading performance of students using a web-based virtual stock trading system
Computers & Mathematics with Applications
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
Generalized hesitant fuzzy sets and their application in decision support system
Knowledge-Based Systems
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
A decision support system for stock investment recommendations using collective wisdom
Decision Support Systems
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Nowadays, stock market is becoming a popular investment platform for both institutional and individual investors. The current financial information systems serve to provide latest information. However, they lack sophisticated analytical tools. This paper proposes a new architecture for financial information systems. The developed prototype is entitled as the Multi-level and Interactive Stock Market Investment System (MISMIS). It is specially designed for investors to build their financial models to forecast stock price and index. The performance of the financial models can be evaluated on a virtual trading platform. There are other features in MISMIS that are tailor-made to handle financial data; these include synchronized time frame, time series prediction techniques, preprocessing and transformation functions, multi-level modeling and interactive user interface. To illustrate the capability of MISMIS, we have evaluated strategies of trading the future options of Hang Seng Index (HSI). We find that historical HSI, Dow Jones Index, property price index, retailing sales figure, prime lending rate, and consumer price index in Hong Kong are essential factors affecting the performance of the trading of HSI's future option. Also there are some feedbacks from the in-depth interviews of six financial consultant upon how they perceived the prototype MISMIS.