Bong Ko
Thank you for visiting my website!
I am a Finance Ph.D. candidate at the Paul Merage School of Business, University of California, Irvine. I am on the 2025-2026 job market.
My research interests include Investments, Asset Management, and Return Predictability.
Email: bongk1 [at] uci.edu
Returns to scale in passive management: Evidence from ETFs (Job Market Paper, solo-authored)
Presentations: MFA Doctoral Symposium (2026), AFA PhD Student Poster Session (2025), FMA Special PhD Paper Presentation (2025), FMA Annual Meeting Regular Session (2024), FMA Doctoral Student Consortium (2025, 2024), UCI Finance PhD Alumni Summer Conference (2024), UC Irvine Brownbag (2024)
Abstract: Leveraging recursive demeaning, we find that ETF’s benchmark-adjusted return increases with fund size. One standard deviation increase in fund size is associated with an increase in monthly performance by 5.1-7.6 basis points, depending on various indexes. We examine potential drivers and show that economies of scale are partially explained by fund’s tracking error, Morningstar rating, and operational shorting. Liquidity and turnover, which affect scale economies in active funds, are unlikely to play a role in passive funds. Further tests support the persistence of increasing returns to scale and skewed distribution of ETF sizes, mirroring the real-world ETF space.
Capitalizing on Retail Investor Sentiment: Evidence from FinTech ETFs (with Jinfei Sheng, Zheng Sun, and Wanyi Wang)
Presentations: FMA Annual Meeting (2025), Bretton Woods Accounting and Finance Ski Conference (2024), Shanghai-Edinburgh-UCL Fintech Conference (2023), UC Irvine Brownbag (2023)
Abstract: This paper studies how professional money managers respond to retail investors’ sentiment toward new technology through the lens of FinTech ETFs. Retail investor sentiment about FinTech, measured by Google Search Volume, has tripled from 2015 to 2022. Using a hand-collected dataset, we find that the number of FinTech ETFs surges when investor sentiment toward FinTech is high. On average, 34% of FinTech ETFs engage in “FinTech Washing,” as evidenced by the low fraction of FinTech stocks in their portfolios and low risk-exposure to the FinTech space. Despite their high fees (68 bps per annum), FinTech ETFs yield annual excess returns of −13%, significant even after accounting for various risk factors. The investor base, primarily comprised of retail investors, is not aware of the FinTech washing and appears unresponsive to unsatisfactory performance. Instead, they respond to Bitcoin returns and FTX scandal. Their underperformance can be attributed to market timing and the activeness of funds. The disappointing performance cannot be explained by low-quality fund families launching those ETFs. Overall, the evidence suggests that asset managers capitalize on the retail investors’ sentiment toward the FinTech space by offering misrepresented and low-quality ETFs, which carries significant policy and welfare implications for both regulators and investors.
Reversal in benchmark-adjusted returns: Evidence from ETFs (solo-authored)
Presentations: FMA Annual Meeting (2025), UC Irvine Brownbag (2025)
Abstract: Underperforming (outperforming) ETFs relative to the index over the previous month tend to outperform (underperform) their benchmark in the next month. A one standard deviation increase in lagged benchmark-adjusted return predicts a 7.2 bps decline in the future return. Reversal persists over the one-month horizon, and flagship ETFs exhibit stronger patterns. We examine potential drivers and show that reversal becomes weaker by arbitrage in the primary market, but more pronounced during the COVID-19 pandemic due to spillover from binding capital constraints in the bond segment. Further tests reveal that ETF and stock limits of arbitrage have differential effects on predictability.
Risk-scaled anomalies (solo-authored)
Presentations: Australasian Finance & Banking Conference (2022), New Zealand Finance Meeting (2022), World Finance Conference (2022), UC Irvine Brownbag (2022)
Abstract: Using a comprehensive set of 125 anomaly portfolios, I analyze the effectiveness of risk management. Volatility-managed portfolios are unlikely to produce greater alphas and Sharpe ratios relative to their original counterparts. Managed portfolio returns are 43% lower in the pre-sample and 61% lower in the post-sample, implying that the deteriorations in performance are present in the managed portfolio similar to the original portfolio. However, momentum-based strategies characterized by more negative skewness with a longer tail tend to improve after adjusting risk. The finding suggests that the benefits of risk management are concentrated on reducing crash risk.
Information Environment and Return Predictability of Conglomerates, Korean Journal of Business Administration, 2020 (with Wonho Cho and Saeyeul Park)
This article is a revised version of my master's thesis.