Working Papers

We revisit the recent finding of DeMiguel, Gil-Bazo, Nogales and Santos (2023) that machine learning (ML) methods can identify long-only portfolios of actively managed mutual funds with positive net annual alpha of up to 2.69%. Using their replication package, we show that these conclusions are driven by a coding error that introduces a forward-looking bias in the construction of portfolio returns. Removing the bias eliminates the long-only alpha. Based on an independent replication, we find that nonlinear ML methods consistently identify underperforming funds. However, this also holds for linear models. Consequently, long-short portfolios deliver robust and economically meaningful alphas, driven predominantly by the short leg. We further extend the analysis by expanding the sample period to the end of 2024, by considering forecast horizons up to 36 months and conducting univariate sorts across fund characteristics. These extensions do not materially enhance long-only predictability but offer empirical evidence of the relative advantages of ML at longer horizons. Overall, our results suggest that ML adds value primarily through the consistent avoidance of poorly performing funds rather than by identifying long-only outperforming ones. This has important implications for both the academic interpretation of ML performance and its practical application in active mutual fund selection.

We examine the impact of increasing competition among the fastest traders by analyzing a new low-latency microwave network connecting exchanges trading the same stocks. Using a difference-in-differences approach comparing German stocks with similar French stocks, we find improved market integration, faster incorporation of stock-specific information, and an increased contribution to price discovery by the smaller exchange. Liquidity worsens for large caps due to increased sniping but improves for mid caps due to fast liquidity provision. Trading volume on the smaller exchange declines across all stocks. We thus uncover nuanced effects of fast trader participation that depend on their prior involvement.

We show that “quasi-dark” trading venues, i.e., markets with somewhat non-transparent trading mechanisms, are important parts of modern equity market structure alongside lit markets and dark pools. Using the European MiFID II regulation as a quasi-natural experiment, we find that dark pool bans lead to (i) volume spill-overs into quasi-dark trading mechanisms including periodic auctions and order internalization systems; (ii) little volume returning to transparent public markets; and consequently, (iii) a negligible impact on market liquidity and short-term price efficiency. These results show that quasi-dark markets serve as close substitutes for dark pools and consequently mitigate the effectiveness of dark pool regulation. Our findings highlight the need for a broader approach to transparency regulation in modern markets that takes into consideration the many alternative forms of quasi-dark trading.

We study how the informativeness of stock prices changes with the start of high-frequency trading (HFT). Our estimate is based on the staggered start of HFT participation in a panel of international exchanges. With HFT presence, market prices are a less reliable predictor of future cash flows and investment, even more so for longer horizons. Further, firm-level idiosyncratic volatility decreases, and the holdings and trades by institutional investors deviate less from the market-capitalization weighted portfolio as a benchmark. Our results document that the informativeness of prices decreases subsequent to the start of HFT. These findings are consistent with theoretical models of HFTs' ability to anticipate informed order flow, resulting in decreased incentives to acquire fundamental information.

Many companies issue multiple bonds that are outstanding simultaneously. Market participants surmise that this fragmentation of issuance is one reason for the relative lack of liquidity in the corporate bond market. Using data from the US, we confirm that bond issuance fragmentation predicts lower bond liquidity, as measured by estimates of the bid-ask spread and the intra-quartile range of trade prices. These results hold for both retail and institutional-sized trades. The effect was most severe during the Global Financial Crisis, and it has generally reduced over time. Our results are most consistent with predictions from models of search and bargaining costs, though they do not rule out a role of dealer inventory costs. Bond issuers do not benefit from more concentrated issuance because they are associated with higher yields despite their higher liquidity.

Many exchanges allow or require firms to hire designated market makers (DMM) to improve liquidity. Available evidence suggests that DMMs indeed improve liquidity, and that share prices react favorably to their presence. Little is known, however, about what DMMs actually do. We analyze their trading activity in detail. DMM participation decreases in firm size and trading volume, and decreases over the trading day. They not only provide liquidity but also take liquidity. Other traders take liquidity supplied by DMMs in times of high spreads and informational asymmetries. DMMs do not earn trading profits and thus need to be compensated otherwise for the services they provide.