Qian Wang

Biography

I am an Assistant Professor of Finance at the College of Business, University of Nevada, Reno. My research interests span empirical corporate finance, household finance, real estate finance, and their intersections. I pay special attention to how financial technology and AI applications influence the financial industry and consumer decisions.

Education

• Ph.D. in Management (Finance), Purdue University, 2019–2026
• M.S. in Economics, Purdue University, 2019–2021
• Master of Finance, University of California, Riverside, 2017–2018
• B.A. in Economics, Sun Yat-sen University, 2010–2014

Research Interests

Empirical Corporate Finance, Cost of Capital, Capital Structure, Small Business, Financial Intermediation, Bankruptcy, Corporate Governance, Household Finance, Real Estate Finance, FinTech, AI in Finance

Working Papers

• Negative Capital Shock, Overseas Buyers, and the Housing Market SSRN Link
  Revise & Resubmit, Journal of Financial and Quantitative Analysis
  Abstract: In this paper, I explore the implications of a negative capital shock from China on local housing markets. By leveraging China's implementation of stricter foreign exchange purchase quota management for its citizens as an exogenous negative demand shock on foreign Chinese buyers in the US single-family homes market, my analysis reveals substantial effects on local housing assets. Not only did the volume of house transactions by foreign Chinese buyers significantly decline compared to other foreign ethnicities (Indian and Russian), but house prices also significantly dropped in neighborhoods that are popular among Chinese buyers. However, the magnitude of the price drop is smaller than expected, especially when compared to positive demand shocks of similar magnitude reported in the literature. Additionally, the elasticity of housing supply, as implied by such a negative demand shock, is higher than that reported in existing literature. My findings explain why cross-border restrictions on capital inflows have had limited effects on local house prices and indicate that capital withdrawals by large corporate investors similarly may exert limited impact.
  Selected Presentations: FMA (2025), AREUEA National (2025), MFA (2025), AFA PhD Student Poster Session (2025)

• Where Do Small Firms Get Debt Financing? (with Sergey Chernenko)
  Revise & Resubmit, Journal of Financial Intermediation
  Best Paper in Financial Intermediation & Markets, Semi-finalist, FMA (2025)
  Abstract: We use detailed claim-level data from bankruptcy filings to study the types and sources of debt financing used by small firms. Over 40% of firms in our data borrow from multiple lenders; 16% borrow from both bank and nonbank financial institutions. Only 26.5% of firms borrow exclusively from banks. We report detailed descriptive statistics on the types of debt used by small firms: credit cards, lines of credit, receivables financing, equipment financing, mortgages, and term loans. The smaller firms rely more on credit cards and term loans, while larger firms rely more on mortgages. Only half of the loans in our data are associated with UCC financing statements, calling for caution in using UCC filings as a proxy for small business lending. We examine the association between the structure of the local banking markets and the composition and sources of small business debt financing. Deposit concentration is associated with higher share of nonbank debt, especially receivables financing debt. Firms in counties with high deposit concentration appear to substitute to receivables financing from nonbank lenders, but they are more likely to have equipment financing with bank lenders. In counties with larger banks, small firms also substitute from bank to nonbank lenders.
  Selected Presentations: FMA (2025), FDIC Bank Research Conference (2025), Florida State University Truist Seminar (2024)

• Algorithms as Equalizers: How Large Investors Narrow the Racial Price Gap in Housing (with Sergey Chernenko and Michael Eriksen)
  Abstract: This paper examines the role of large-scale investors, buy-to-rent institutional investors and iBuyers, in addressing racial disparities in the U.S. housing market. Utilizing a novel dataset of 1.75 million transactions matched to voter registration records, we find that while minority homeowners are 45–120% more likely to sell to these large investors, these transactions actually narrow the racial wealth gap. Specifically, our hedonic pricing models reveal that after controlling for the "cash discount", these large investors pay significant price premiums to Black (4.3–5.9%), Hispanic (5.3–8.8%), and Asian (3.0–5.9%) homeowners compared to what they would receive from traditional buyers. We argue that the characteristic-based, algorithmic pricing models used by these firms act as a "race-blind" mechanism that bypasses the search frictions and face-to-face biases prevalent in traditional real estate transactions. Furthermore, we show that minority sellers respond rationally to these premiums, with their propensity to sell to large investors being highest in counties with the most severe historical Black-White price disparities. These findings suggest that financial innovation in real estate can serve as a corrective force against systemic housing inequities.
  Selected Presentations: FMA (2026 scheduled), AREUEA-ASSA (2027 scheduled)