The development of technology of information processing has led investors to wider possibilities of receiving and processing economical and business information. The responsible approach to investment now requires deeper analysis of the situation, and first of all it needs more reasonable use of technical and fundamental analysis in investment decision making process. During the last two years there are more data created than in the past 2000 years together and amount of data are rising year to year by creating data processing and understanding as top challenge. The idea to apply AI algorithms to financial data analysis comes from the field of molecular biology. Gene expression profiling is the measurement of the activity of thousands of genes at once, to create a global picture of cellular function. At the core of the new AI data analysis methodology is pattern recognition. Pattern recognition algorithm slices time series database with financial data on smaller length time series and performs step by step comparison with defined patterns using different similarity measures depending on time series length, structure and other criteria. Use of pattern recognition algorithm in data analysis gives possibility to find similar behaving historical data in the large data sets within the seconds. This AI technology is a solution to find similarities and anomalies in large financial data sets to analyses markets and individuals financial behaviour using “top-down” and “bottom-up” approaches that will result in smart investment decision-making improvement.
Time and available information is critical when making investment decisions.
