Zihong Huang

Zihong Huang

Assistant Professor @ Rawls, Texas Tech University

Talks and presentations

Data-Driven Venture Capital (DDVC): The Impact of Data-Driven Investment Strategy on VC Firms’ Success

October 23, 2021

Presentation, WeB 2020, CIST 2021, Newport Beach, CA

In the venture capital (VC) industry, a new breed of VC firms has emerged to embrace data-driven investment strategy instead of relying on human judgments. Although data-driven methods has already demonstrated advantages over humans in some domains, it is unclear whether this strategy can lead to superior performance in the venture capital industry. This research fills this gap by estimating the causal impact of a VC firm’s adoption of data-driven investment strategy on its success using matched portfolios of startups from data-driven and non-data-driven VC firms. We find that adoption of data-driven strategy tends to increase the success of a VC firm in terms of successful exits (e.g. IPO and acquisition) of startups it invests in. In addition, we also find that data-driven investment strategy can reduce racial bias but increase gender and local bias. The increase of gender bias is responsible for the superior performance of data-driven investment strategy while the reduction of racial bias and the increase of local bias is not.

Economics of Social Media Fake Accounts

December 15, 2020

Presentation, CWEIST 2020, Wise 2020, PlatStrat 2021, Online

Amid the rise of the influencer economy, fake social media accounts have become a prevalent problem on many social media platforms. Yet the problem of fake accounts is still poorly understood and so is the effectiveness of coping strategies. This research models the ecosystem of fake accounts in an influencer economy and obtains insights on fake-account purchasing behaviors, the impact of anti-fake efforts, and the roles of social media literacy, anti-fake technology, and costs of fake accounts. We show that not only low-quality influencers may buy fake accounts to mimic high-quality ones in a “pooling” equilibrium, high-quality influencers may also buy to prevent mimicry in a “costly-separating” equilibrium. There is also a “naturally-separating” equilibrium where the two types are separated without buying fake accounts. We find that increasing anti-fake efforts and social media literacy may cause more fake accounts. The platform generally prefers either a zero-effort pooling equilibrium or a high-effort naturally-separating equilibrium. Compared to the level of anti-fake efforts preferred by consumers, the platform may be overly or insufficiently aggressive. Some anti-fake strategies, such as increasing social media literacy and fake-account costs, may benefit consumers but not the platform. One exception is increasing the effectiveness of anti-fake technology, which benefits both the platform and consumers and reduces the number of fake accounts.