On the average, over $8 billion dollars is spent annually by investment managers in the US alone on data processing and research services. A growing portion is being spent on the processing of analyst reports, news, and filings with the goal of providing short-term alert-based text mining solutions on Twitter and financial news wires.

 

IntuView may also serve in analytics of Financial Markets by deep analysis of sentiment towards companies, markets, areas, and products. IntuView leverages its information extraction and sentiment analysis solutions to target longer term signals that can be leveraged by a greater portion of the investment management community. This application of the IntuView Technology addresses a key gap in the market – extracting “investing” signals from complex information complex sources including (but not limited to) earnings and industry conference transcripts, annual reports, and trade journals and providing analysis with intelligence around why certain terms or concepts are appearing and to what extent the rest of the investment community is paying attention. This incremental insight can provide investment professionals with a better sense of timing of events, critical in capital allocation.

 

The intelligence that IntuView can extract may be “change in sentiment” among the management team of a company based on earnings call transcripts where company is getting more or less positive to its future prospects or industry as a whole. It can also be the identification of company or product specific news that are hard to assess because of its source, like medical databases tracking product defects or lawsuits filed in lower courts. The benefit from such a solution is the speed of which such intelligence can be discovered and delivered to investors and the information that can be extracted from aggregating this data from multiple sources and companies. It matters whether one company reports issue with raw material costs or if the whole industry raises it as a concern. IntuView enables the user to aggregate trends during earnings season without the need to read transcripts or participate on multiple earnings calls.

 

The methodology of the IntuView technology in this regard is based on creating composite dynamic ontological constructs that represent all the relevant information presented in an unstructured text regarding expectations, assessment or analysis of a financial entity. For example, a text that refers to the anticipated earnings of a company in the X quarter of next year as rising from A to B will generate a dynamic ontological instance that comprises the “sentiment holder” (the analyst), the “sentiment object” (the company), the “object attribute” (earnings of the company), the “sentiment” (expectation to go up), the “time frame” (X quarter of Y year). Using this now-structured information, the IntuView platform can now identify trends in relation to companies, groups of companies, sectors, or any other breakdown and enable queries on the information and alerts.