Conference Agenda

Cross-product manipulation involves manipulating one financial product to profit from the subsequent reaction in a different but related product. In this paper, we develop a simple model that researchers and regulators can use to scan for the susceptibility of two markets to such misconduct. We also test the model empirically on a set of government bond futures contracts using a complete EUREX ultra-high-frequency dataset. Our findings show that cross-product manipulation is feasible across bond futures with different underlying maturities, issuers and contract expiry dates. The results suggest that cross-product manipulation might be widespread despite an increasing crackdown by regulators and prosecutors.

Manipulation of financial markets has long been a concern. With the automation of financial markets, the potential for high frequency market manipulation has arisen. Yet, such behavior is hidden within vast sums of order book data, making it difficult to define and to detect. We develop a tangible definition of one type of manipulation, spoofing. Using proprietary user-level identified order book data, we show the determinants of spoofing. Exploiting SEC Litigation Releases that exogenously reduce spoofing, we show causal evidence that spoofing increases return volatility, increases trading costs, and decreases price efficiency. The findings indicate that spoofing harms liquidity and price discovery.

Among the many possible complex options trades, just a few dominate the market. Two simple roles largely explain their use. Using a new approach to identify complex trades, we find that vertical, vertical ratio, calendar, and diagonal spreads account for most complex volume, and volatility trades such as straddles and strangles account for a much smaller fraction. Many trades are executed not to obtain the payoffs of the complex packages, but instead to adjust simple options positions by changing either strikes or expiration dates. Others appear intended to make simple bets on price movements with small initial investments.

We develop simple measures of informed trading risk (QIDRes) that capture abnormal undercutting activity, reflecting the intuition that liquidity-providing algorithms compete less to fill incoming marketable orders when adverse selection exposure rises. Despite being conveniently computed from TAQ data, when examined around major information events, QIDRes behaves similarly to existing measures of informed trading intensity/probability whose constructions are complex and demanding. Our measure predicts both arrivals and magnitudes of imminent information events. Moreover, episodes of high QIDRes coincide with weaker subsequent price reversals, increased short selling activity, and more likely informed institutional trades. QIDRes positively predicts stock returns up to six months forward, especially among stocks with tighter short sale constraints. However, QIDRes is by construction orthogonal to stock liquidity and does not constitute a persistent stock characteristic, so we attribute its return predictability to limits to arbitrage.

Private offerings raise about 15 percent less capital during the months preceding local U.S. gubernatorial elections relative to other private offerings issued within the same state and year. Consistent with limited investor pools determining private companies’ access to capital, we observe evidence of smaller capital raisings per investor and no decreases in capital raising for offerings involving venture capitalists, regardless of whether the company eventually has a successful exit or remains privately held. We also observe increased use of investment minimums preceding elections. The findings provide evidence that reduced access to capital leads to smaller private proceeds prior to elections.

Generative AI tools such as ChatGPT can fundamentally change the way investors process information. We probe the economic usefulness of these tools in summarizing complex corporate disclosures using the stock market as a laboratory. The unconstrained summaries are remarkably shorter compared to the originals, whereas their information content is amplified. When a document has a positive (negative) sentiment, its summary becomes more positive (negative). Importantly, the summaries are more effective at explaining stock market reactions to the disclosed information. Motivated by these findings, we propose a measure of information “bloat.” We show that bloated disclosure is associated with adverse capital market consequences, such as lower price efficiency and higher information asymmetry. Finally, we show that the model is effective at constructing targeted summaries that identify firms’ (non-)financial performance. Collectively, our results indicate that generative AI adds considerable value for investors with information processing constraints.

Traditional finance theory asserts that stock prices depend on expected future cash flows. We explore how the growing prominence of non-pecuniary preferences in the form of sustainable investing alters this core financial relationship. Using the setting of earnings announcements, we find that sustainable investing diminishes stock price sensitivity to earnings news by 45%-58%. This decline in announcement-day returns is mirrored by a comparable drop in trading volume. This effect persists beyond the immediate announcement period, implying a lasting alteration in price formation rather than a short-lived mispricing. Our findings suggest that sustainable investing reduces the significance of cash flows in shaping stock prices.

Off-exchange trades are often executed by referencing on-exchange prices. In equilibrium, such price referencing softens market makers' on-exchange competition and makes liquidity expensive for investors. Additionally, by equalizing on- and off-exchange prices, price referencing guarantees “best-execution” and makes investors indifferent where to trade. Market makers effectively obtain a license to fragment order flow off exchange, raising their profits but reinforcing market-wide illiquidity. This inefficiency remains tenacious even if more market makers enter and if they are forced to compete off exchange, as in the SEC's proposed order-by-order auction. The model yields important implications for regulating various forms of off-exchange trading.

We explore the potential for automated market makers (AMMs) to enhance traditional financial markets, drawing on their success in the crypto-assets space. The increasing tokenization of assets and regulatory changes, including the SEC’s initiatives to reshape retail order trading, underscore the relevance of considering AMMs in traditional markets. Our study establishes a practical framework to evaluate the viability of AMM liquidity provision in equities and assess if AMMs offer improvements over traditional markets. Analyzing U.S. equity trading data, we find that well-designed AMMs could save U.S. investors billions annually. These savings arise from the distinct characteristics of AMMs, especially the improved risk-sharing and the role of long-term asset holders as liquidity providers. Unlike traditional market makers, long-term asset holders in AMMs seek compensation only for incremental intraday risk relative to a buy-and-hold strategy. They utilize locked-up capital that would otherwise remain idle at brokerages. Small firms, in particular, can benefit by attracting more investors and capital through this approach.

Voluntary Environmental, Social, and Governance (ESG) disclosure by institutional investors enables clients to allocate responsible capital to institutions with better ESG practices. Institutional investors disclose their ESG practices as part of their commitment to the Principles for Responsible Investment (PRI), the world’s largest responsible investment network. After joining the PRI, investors annually file an ESG report, which is assessed and scored by the PRI. Clients allocate more assets toward institutions that receive higher scores on their disclosure, especially when the disclosure is corroborated by third-party ESG fund ratings. Importantly, the disclosure does not appear to be cheap talk since it correlates with more sustainable equity portfolios and more engagements on ESG issues. However, both higher flows and better ESG practices occur only in countries where responsible institutional asset owners have a stronger presence.

We show that after the introduction of leverage ratio constraints on bank-affiliated dealers, bond mutual funds have engaged in more liquidity provision in investment- grade corporate bonds and that the performance of funds with liquidity-supplying strategies has benefited. Not only have regulations transferred profits associated with liquidity provision in the corporate bond market to mutual funds, but the liquidity and returns of investment-grade corporate bonds have become more exposed to redemp- tions from the bond mutual fund industry, suggesting that the regulations may have made investment-grade corporate bonds more volatile. Accordingly, we observe that investment-grade corporate bonds more exposed to leverage ratio constraints experi- enced a more severe deterioration in liquidity and returns at the onset of the COVID-19 pandemic.

This paper studies the role of regulatory oversight in increasing market transparency and facilitating financial intermediation. I exploit an unanticipated reform that substantially expanded the regulatory oversight of private equity (PE) fund advisers, which reduces the information asymmetry faced by investors. Institutional investors that have more pre-existing relationships with regulated PE fund advisers are less likely to bypass external fund vehicles when investing in private companies. While disintermediation in PE markets allows investors to mitigate agency costs associated with intermediation, it could result in capital misallocation. There is little evidence of adverse selection in the deals available to investors, but they tend to finance more mature, larger, and less innovative companies when investing directly, as opposed to investing through PE funds. Overall, my findings highlight the limits of market governance in PE funds and the positive role of regulatory intervention in shaping the organizational structure of PE markets.

We examine the impact of the post-Dodd-Frank change in 2011 on hedge fund disclosure. We find that new disclosure questions added to Form ADV improve the ability to forecast future adverse operational events compared to the information disclosed pre-Dodd-Frank. A byproduct of the analysis is a uni-dimensional operational risk score based on information from the SEC website. The measure is effective in predicting liquidation events, changes in leverage, and other performance metrics. It is also predictive of net fund flows, suggesting that investors take into account operational risk when making investment decisions.