I am an Assistant Professor of Finance at the Mendoza College of Business at the University of Notre Dame. I study Passive Investing, Market Microstructure, High-Frequency Trading, and Financial Market Design.
You can reach me at jshim2@nd.edu.
My CV is here.
Please contact me for an updated draft. Click to download the Project Timeline and Supporting Documents
Note: This paper came from a revision of "In the Blink of an Eye: Exchange-to-SIP Latency and Trade Classification Accuracy," which is with a subset of the authors in the 2025 paper and is reject and resubmit from the Journal of Finance. The revision is substantial and the approach of the paper is completely different. As a result, we treat this draft as a new paper. The previous draft is available as a permanent working paper on SSRN.
Abstract: The NYSE Trade and Quote (TAQ) dataset, used throughout finance research and securities regulation, is generated by a Securities Information Processor (SIP), which aggregates quotes and trades from all U.S. stock exchanges at a central location. We show that the SIP systematically reports events out of sequential order: quote changes that occur after a trade are frequently reported as occurring before the trade. The source of the issue is that trades and quotes are recorded with variable latency (due to, e.g., geography) and are extremely clustered in time. The result is a look-ahead bias: the prevailing SIP quotes, ubiquitous in signing trades and measuring spreads, incorporate price impact from the trade itself. We document that the look-ahead bias leads to incorrect trade signing and downward-biased effective spreads and price impact. The errors are extreme for the large fraction of trades with high reporting latency: approximately 20% of trades are incorrectly signed, and effective spreads and price impact are understated by more than 40%. We propose a signing methodology based on exchange rules that yields 100% accuracy for a significant majority of volume and, over all trades, outperforms existing methodologies.
Abstract: We analyze a multi-period trading model to study the effects of commitment power and trading frequency on a liquidity trader’s execution costs. The trader sells her position using market orders over multiple trading periods to large market makers who submit demand schedules. We show that the liquidity trader’s ability to pre-commit to future orders significantly improves liquidation value compared to a trader that does not commit, regardless of trading frequency. The optimal execution with commitment involves overselling in the first period, then gradually repurchasing the oversold units in subsequent periods. This strategy leverages market makers’ anticipation of future purchases to improve initial selling prices. Optimal execution without commitment power involves a sequence of sell orders decreasing in size over time. An increase in trading frequency unambiguously improves execution for the commitment and non-commitment trader, but is especially beneficial for the commitment trader. However, a trader is worse off if she loses commitment power but is able to trade more frequently. The model has several applications, including understanding some aspects of the transition from human to electronic trading, index-linked rebalancing strategies, and the design of execution algorithms and trading venues.
Abstract: We develop two new measures to quantify active fund decisions at the position level. Our measures are designed to separate flow-based passive scaling from active rebalancing decisions. We find that additive active rebalancing – both for existing and new positions – predicts higher stock-level alpha over the following quarter. We show our results are not driven by mechanical price pressure, and provide evidence that funds may trade on forecasts for future earnings. Finally, we aggregate our stock-level measure to the portfolio level and show that actively adding to positions translates to outsized returns for fund investors.
Press: Financial Times, Bloomberg, ETF.com
Abstract: Can ETFs trigger fire sales in illiquid assets? We develop and empirically examine a model where an authorized participant (AP) holds bond inventory and connects the ETF to the underlying bond market. For redemptions, the AP acts as a buffer between the two markets, holding redeemed bonds to preserve the mark-to-market value of her inventory and avoid a fire sale. For creations, the AP behaves asymmetrically, and transmits ETF purchases to the bond market to boost inventory mark-to-market values. The AP’s costs of handling creations/redemptions are paid by liquidity-demanding ETF investors via premiums/discounts. We document new empirical facts motivated by the model, and provide a novel explanation for why ETFs holding more liquid bonds traded at larger discounts than those holding illiquid bonds during the COVID-induced sell-off in March 2020. Our findings show that ETFs have advantages over mutual funds in managing illiquid assets.
Abstract: I argue that arbitrage mistranslates factor information from ETFs to constituent securities and distorts comovement. The intuition behind this distortion is arbitrageurs trade constituent securities not based on their fundamental exposures but by their portfolio weights, causing securities to comove with the ETF based on a measure I call arbitrage sensitivity — a combination of portfolio weight and price impact sensitivity — rather than fundamental exposures. Arbitrage sensitivity predicts comovement between stock and ETF returns, especially in periods of high ETF volume and volatility, but not before 2008 when ETFs were not as heavily traded. Arbitrage-induced comovement leads to over-reaction to ETF returns for stocks more sensitive to arbitrage and under-reaction for those less sensitive. A long-short portfolio constructed based on arbitrage sensitivity generates an alpha of around 7.5% per year. Unlike most anomalies, arbitrage comovement is strongest in large-cap stocks, which are held by the most actively traded ETFs. Arbitrage comovement implies observed factor loadings are less reliable for assessing risk since they are at least partially driven by mechanical arbitrage trading instead of fundamental exposures.
Press: Bloomberg, Bloomberg (no paywall), Bloomberg Newsletter (no paywall), Rational Reminder Podcast (~35 min mark), The Economist
Awards: 2025 ASU Sonoran Winter Finance Conference, Jacob Gold & Associates Best Paper Prize
Abstract: We find that firms are the primary sellers who clear the market for index fund buying, providing shares at a nearly one-for-one rate. Most demand-side institutions trade in the same direction as index funds rather than accommodating passive demand. We use two instruments for index fund demand and show that firms causally respond to exogenous passive demand, with prices serving as the coordinating mechanism. Firms satisfy passive demand mostly through non-primary market issuance, for example through employee stock-based compensation. Our results suggest that passive investing has systematically supplied capital to firms by enabling equity issuance over the last two decades.
Press: Bloomberg
Abstract: Value-weighted index funds implicitly engage in market timing when adjusting positions in response to compositional changes in the stock market. Long-short portfolios representing intensive- and extensive-margin rebalancing trades earn -4% annually and load negatively on value and profitability factors. We estimate these trades impose a 60 bps per year performance drag. We explore alternative value-weighted total market indexes that rebalance less frequently and delay responses to market composition changes. These policies benefit investors by reducing trading costs and improving market timing, yielding savings that are an order of magnitude larger than typical index fund expenses.
Press: Bloomberg; Policy: SEC Commissioner, SEC Proposal on Market Data Infrastructure
Abstract: Will stock exchanges innovate to address latency arbitrage and the arms race for speed? This paper models how exchanges compete in the modern electronic era and how this shapes incentives for market design innovation. In the status quo, exchange trading fees are competitive while exchanges earn economic rents from selling speed. These rents create a wedge between private and social incentives to innovate, and support the persistence of an inefficient market design in equilibrium of a market design adoption game. We discuss implications for policy and insights for the literatures on market design, innovation, and platforms.
Press: Financial Times, Bloomberg, Chicago Tribune, Bloomberg Editorial Board, The Economist, Chicago Booth Review; Policy: SEC Chair, European Commission, New York Attorney General
Awards: AQR Insight Award (First Prize), Utah Winter Finance Conference (Best Paper Award)
Abstract: The high-frequency trading arms race is a symptom of flawed market design. Instead of the continuous limit order book market design that is currently predominant, we argue that financial exchanges should use frequent batch auctions: uniform price double auctions conducted, for example, every tenth of a second. That is, time should be treated as discrete instead of continuous, and orders should be processed in a batch auction instead of serially. Our argument has three parts. First, we use millisecond-level direct-feed data from exchanges to document a series of stylized facts about how the continuous market works at high-frequency time horizons: (i) correlations completely break down; which (ii) leads to obvious mechanical arbitrage opportunities; and (iii) competition has not affected the size or frequency of the arbitrage opportunities, it has only raised the bar for how fast one has to be to capture them. Second, we introduce a simple theory model which is motivated by and helps explain the empirical facts. The key insight is that obvious mechanical arbitrage opportunities, like those observed in the data, are built into the market design—continuous-time serialprocessing implies that even symmetrically observed public information creates arbitrage rents. These rents harm liquidity provision and induce a never-ending socially wasteful arms race for speed. Last, we show that frequent batch auctions directly address the flaws of the continuous limit order book. Discrete time reduces the value of tiny speed advantages, and the auction transforms competition on speed into competition on price. Consequently, frequent batch auctions eliminate the mechanical arbitrage rents, enhance liquidity for investors, and stop the high-frequency trading arms race.
Abstract: Our recent research (Budish, Cramton, and Shim, 2013) proposes frequent batch auctions—uniform-price sealed-bid double auctions conducted at frequent but discrete time intervals—as a market design alternative to continuous-time trading in financial markets. This short paper discusses the implementation details of frequent batch auctions. We outline the process flow for frequent batch auctions, discuss a modification to the market design that accommodates market fragmentation and Reg NMS, and discuss the engineering and economic considerations relevant for determining the batch interval. Open questions are discussed throughout.
Speed Limits in Asset Prices, with Ben Matthies and Chen Wang.
The Arithmetic of Passive Management, with Marco Sammon.
Depth and Adverse Selection: Theory and Evidence, with Yenan Wang.
