Abstract: Systems and techniques for developing and implementing a credit risk model using various sources of data, including price data, financial accounting ratios, ESG (Environmental, Social and Governance) data, and textual data are disclosed. Each source of data provides unique and distinct information about the health of an entity, such as a firm or company. The systems and techniques combine information from disparate sources to create a uniquely powerful signal. The systems and techniques can be used to predict a number of events including, but not limited to, probability of default or bankruptcy, loss given default, probability of rating agency rating change, and probability of equity price moves.

Abstract: Systems and techniques are disclosed to value privately-held companies, at least in part, on publicly traded companies. A value is computed for a primary private company using a weighted average valuation multiple and a weighted average total return value associated with publicly traded companies. A confidence rating of the computed valuation is also provided.

Abstract: Systems and techniques are disclosed for identifying a marketing opportunity by associating a set of prediction scores with a set of privately-held entities. Each of the set of prediction scores is based on a likelihood of a privately-held entity initiating an IPO over a set period of time. To derive the set of prediction scores, systems and techniques are disclosed that utilize one or more private company data, investor data, deals data, and market data associated with a privately-held entity. An accompanying confidence rating may also be provided for each of the set of prediction scores.

Abstract: Systems and techniques are disclosed to value privately-held companies, at least in part, on publicly traded companies. A value is computed for a primary private company using a weighted average valuation multiple and a weighted average total return value associated with publicly traded companies. A confidence rating of the computed valuation is also provided.

Abstract: Systems and techniques for developing and implementing a credit risk model using various sources of data, including price data, financial accounting ratios, ESG (Environmental, Social and Governance) data, and textual data are disclosed. Each source of data provides unique and distinct information about the health of an entity, such as a firm or company. The systems and techniques combine information from disparate sources to create a uniquely powerful signal. The systems and techniques can be used to predict a number of events including, but not limited to, probability of default or bankruptcy, loss given default, probability of rating agency rating change, and probability of equity price moves.