With the advent of big data, unique opportunities arise for data collection and analysis and thus for personalized pricing. We simulate a self-learning algorithm setting personalized prices based on additional information about consumer sensitivities in order to analyze market outcomes for...

We study dynamic market competition between a monopoly incumbent and an entrant experimenting with disruptive innovation. The monopolist can only pursue the uncertain innovation if it buys the disruptor, who is more efficient and privately knows its ability. Mergers generate synergies. We...

We study how learning affects an uninformed monopolist's supply and investment decisions under multiplicative uncertainty in demand. The monopolist is uninformed because it does not know one of the parameters defining the distribution of the random demand. Observing prices reveals this...

Online ratings play an important role in many markets. However, how fast they can reveal seller types remains unclear. To study this question, we propose a new model in which a buyer learns about the seller’s type from previous ratings and her own experience and rates the seller if she learns...

This paper will give a brief overview of the work of introducing machine learning intelligence in the Kineta e-markets system, to facilitate auto-hedging, smart price engine algorithms and proprietary automatic positioning within the foreign exchange market. In this paper we will give a brief...

We examine whether earnings forecasts of sell-side equity analysts are influenced by the forecasts of other analysts on other firms in the analyst portfolio. We find that analysts exhibit “social learning” where analyst optimism is negatively correlated with the recent forecast errors among...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

We propose and find that aggregating a small number of earnings signals from highly macroeconomically exposed firms yields an informative leading indicator of future GDP. This challenges the convention of defining aggregate earnings using a comprehensive set of public earnings signals. Our...