AI@CBS

As a result of the digital revolution, new topics and themes have entered consumer research, and, as the digital revolution enters a new phase, additional new concepts and research questions will emerge. To illustrate the variety of themes on digital technology that consumer researchers have studied, I am presenting a collection of five articles that represent this active new research area. Moreover, I will look into the future and propose a research agenda to address key consumer behavior issues occurring during the next phase of the digital transformation.

Mass customization interfaces typically guide consumers through the configuration process in a sequential manner, focusing on one product attribute after the other. What if this standardized customization experience were personalized for consumers on the basis of how they process information? A series of large-scale field and experimental studies, conducted with Western and Eastern consumers, shows that matching the interface to consumers’ culture-specific processing style enhances the effectiveness of mass customization.

We use five years of bidding data to examine the reaction of advertisers to widely disseminated press on the lack of effectiveness of brand search advertising (queries that contain the firm's name) found in a large experiment run by eBay (Blake, Nosko and Tadelis, 2015). We estimate that 11% of firms that did not face competing ads on their brand keywords, matching the case of eBay, discontinued the practice of brand search advertising.

This paper modifies the standard returns-earnings regression in accounting research to show that financial reports convey both cash-flow news and discount-rate (expected-return) news. The paper points to the realization principle, associated as it is with the resolution of risk, as the accounting feature that conveys expected-return news. The modified returns-earnings regressions indicate that the information so conveyed pertains to priced risk.

Two modern economic trends are the increase in firm size and advances in information technology. We explore the hypothesis that big data disproportionately benefits big firms. Because they have more economic activity and a longer firm history, large firms have produced more data. As processor speed rises, abundant data attracts more financial analysis. Data analysis improves investors’ forecasts and reduces equity uncertainty, reducing the firm’s cost of capital. When investors can process more data, large firm investment costs fall by more, enabling large firms to grow larger.

In “The Intelligent Investor in an Era of Autonomous Learning”, Jeffrey Tarrant delineated the concept of Autonomous Learning Investment Strategies (ALIS), the new AI and data-driven strategies he argued would disrupt today’s fundamental and quantitative managers.  This was followed by an article I wrote, “What a long strange trip it’s been...on ALIS”, which explained the best and worst traits of ALIS managers.  This brief article is called “Think Twice, It Ain’t Alright...ALIS Clarified”, repurposing the title of Bob Dylan’s 1963 song, “Don’t Think Twice, It’s Alright”. 

As technology disrupts industry after industry, it is logical to expect the hedge fund industry will be similarly disrupted. We pose the question: How will that disruption happen and where will it come from?