About Me

Welcome! I am a PhD Candidate in Finance at the Fuqua School of Business, Duke University.

My research interest includes financial economics, economics of information, limited attention, entrepreneurial finance, and financial media.

I am on the 2021-22 job market and will be available for interviews at ASSA 2022 Annual Meetings.

Download my Curriculum Vitae (updated Nov 15, 2021).

Contact Information

jinge.liu@duke.edu | 100 Fuqua Dr, Durham NC 27708

Working Papers

Content Bias and Information Compression [Draft] (Job Market Paper) (Updated Nov 29, 2021)

Abstract. An information presenter faces a physical limit of information transmission. She selects and reports a given number of signal realizations from a large set to maximize the decision maker’s utility. The appearance of content deviates from the substance, as the picture of selected signals looks systematically different from the fundamental picture. Strategic contexts, including prior belief, utility shapes, and payoff relevance, drive the deviation. Apparent biases in news media and beyond, including slanting to the prior belief or extremes, can be explained by the presenter allocating more space to elaborate on more valuable fundamental realizations against the prior or extremes, effectively appearing to generate reports by recalibrating fundamentals. Such biases improve welfare. An asymptotic mapping from fundamentals to report contents is derived. The model relates to empirical content analysis using frequency-based proxies and can analyze contextual effects on contents.

Investor Inattention, Information, and Firm Investment [Draft] (Updated Nov 29, 2021)

Abstract. An investor with limited attention resources demands information about the types of her portfolio firms before investing. The firms strategically supply good news and withhold bad news. The investor may press companies to reveal more information with an attention cost. Because benefit to attention is convex, the investor will choose to optimally focus on a subset of firms and acquire full information while giving up learning the rest. Firms in the scrutinized subset are with low investigation costs and high Expected Value of Perfect Information (EVPI) and always receive efficient investments. The other firms are invested in the most inefficiently and present maximum asymmetry in information transparency. The result rationalizes the use of convertible debt as a socially optimal financing instrument for private firms. It can be applied to analyze a range of investment relationships, such as between a VC and start-ups or an LP and GPs.


Teaching Assistantships:

TA, (MBA) Derivatives (F2017, S2018, F2018, S2019, F2019, S2020, F2020, S2021)

TA, (MQM) Derivatives (F2017, F2018, F2019, F2020)

TA, (MBA) Global Asset Allocation and Investment (F2017, F2018, S2020, F2020)

TA, (MQM) Fixed Income Securities (S2018)

TA, (MBA) Investments (F2018)

TA, (MMS) Foundations of Capital Markets (F2019)

TA, (PhD) Econometrics I (F2015)