Abstract:

Business Management teaching involves contents related to statistical analysis. The mathematical background required for a proper comprehension is sometimes difficult to reach by the students due to the level of abstraction required. In this sense, the use of practical examples will facilitate the understanding of the statistical analysis.
For this reason, it is proposed the introduction of a new computer tool as an educational support for Business Management teaching. The software tool is devoted to the statistical analysis of stock markets based on classical techniques usually taught in the lecture session of the subject.

Initially, the aim of this software was to help the investors in the quantitative decision making. This tool generates recommendations for purchase and sale orders in stock markets. Thanks to an intuitive graphical user interface, the tool can be used by both expert and novice investors in financial markets. In consequence, it has been found to be suitable for educational support in subjects where this kind of statistical analysis are taught.

The software tool includes three different kinds of analysis which are widespread used by investors in finance: the Candlestick Analysis, the Quantitative Analysis and the Chartist Analysis. Candlestick pattern search is performed automatically, so when the tool finds a candlestick pattern the student can easily understand its meaning. Besides, an interesting innovation is the elimination of ambiguity in the indicators of quantitative analysis using a personalized system with the commissions suffered. In this way, students will be able to know which the best indicator is for each company or index. Finally, the software tool allows real-time updating of the stock market data if an Internet connection is provided.

The software tool has been tested during previous years, carrying out several assessment tasks with students during 2011-2012 academic year.

Keywords:

Quantitative Analysis, Stock Markets Tool, Candlestick Analysis, Expert System, Data Mining.