Knowledge discovery with SOM networks in financial investment strategy

Recently, the recession of the global economy induces the coming of a new era of low interest-rates, which resulted in the stock market as an alternative investment channel for investors. The diversity and complication of domain knowledge existing in the stock market enhance its importance for developing a decision support system which can gather real-time pricing information for supporting decision-making in financial investment. In this study, we tackle these challenges by proposing an integrated solution on the basis of K-chart analysis and the over-whelming self-organizing map neural networks. We not only endeavor to improve the accuracy of uncovering trading signals, but also to maximize the profits of trading. The resulting decision model can help investment decision-makers of national stable funds make the most profitable decisions. In addition, financial experts can benefit from the ability of verifying or refining their tacit investment knowledge offered by the uncovered knowledge.