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 lagged spoofing profitability and SEC Litigation Releases as instruments, we show causal evidence that spoofing increases volatility and adverse selection, and decreases price efficiency. The findings indicate that spoofing harms market quality.

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.