Björn Hagströmer

How Aggressive are High-Frequency Traders?

We study order aggressiveness of market-making high-frequency traders (MM-HFTs), opportunistic HFTs (Opp-HFTs), and non-HFTs. We find that MM-HFTs follow their own groups previous order submissions more than they follow other traders’ orders. Opp-HFTs and non-HFTs tend to split market orders into small portions submitted in sequence. HFTs submit more (less) aggressive orders when the same-side (opposite-side) depth is large, and supply liquidity when the bid–ask spread is wide. Thus, HFTs adhere strongly to the tradeoff between waiting cost and the cost of immediate execution. Non-HFTs care less about this tradeoff, but react somewhat stronger than HFTs to volatility.

Causality in crude oil prices

Crude oil markets witness growing disparity between the quality of crudes supplied and demanded in the market. The market share of low‐quality crudes is increasing due to the depletion of old fields and increasing demand. This is unnerving the practitioners and affecting the relevance of the traditional benchmark crudes due to the lack of lower quality benchmarks (Montepeque, 2005). In this article, we apply Granger causality tests to study the price dependence of 32 crudes in order to establish which crudes drive other prices and which ones simply follow general market trends. Our results indicate that some of the old benchmarks are still relevant while others can be disregarded. Our results also interestingly show that the low-quality Mediterranean Russian Urals crude, introduced in the late 1990s, has emerged recently as a significant driver of global prices.

Risk and Return in High-Frequency Trading

We study performance and competition among high-frequency traders (HFTs). We construct measures of latency and find that differences in relative latency account for large differences in HFTs’ trading performance. HFTs that improve their latency rank due to colocation upgrades see improved trading performance. The stronger performance associated with speed comes through both the short-lived information channel and the risk management channel, and speed is useful for various strategies including market making and cross-market arbitrage. We find empirical support for many predictions regarding relative latency competition.

Does Commonality in Illiquidity Matter to Investors

This paper investigates whether investors are compensated for taking on commonality risk in equity portfolios. A large literature documents the existence and the causes of commonality in illiquidity, but the implications for investors are less understood. In a more than fifty year long sample of NYSE stocks, we find that commonality risk carries a return premium of at least 2.0 per cent annually. The commonality risk premium is statistically and economically significant, and substantially higher than what is found in previous studies. It is robust when controlling for illiquidity level effects, transaction costs, as well as variations in illiquidity measurement.

Alchemy in the 21st Century: Hedging with Gold Futures

Recently, the Shanghai Futures Exchange (SHFE) introduced gold futures trading in China. This paper is the first to study the SHFE gold futures, and to evaluate the futures hedging effectiveness si ...

A Network Map of Information Percolation

How does information get revealed in decentralized markets? We test several hypotheses inspired by recent dealer-network theory. To do so we construct an empirical map of information revelation where two dealers are connected based on the synchronicity of their quote changes. The tests, based on EUR/CHF quote data including the 2015 crash, largely support theory: strongly connected (i.e., central) dealers are more informed. Connections are weaker when there is less to be learned. The crash serves to identify how a network forms when dealers are transitioned from no-learning to learning, that is, from a fixed to a floating rate.

Dynamics in Systematic Liquidity

We develop the principal component analysis (PCA) approach to systematic liquidity measurement by introducing moving and expanding estimation windows. We evaluate these methods along with traditional estimation techniques (full sample PCA and market average) in terms of ability to explain (1) cross-sectional stock liquidity and (2) cross-sectional stock returns. For several traditional liquidity measures our results suggest an expanding window specification for systematic liquidity estimation. However, for price impact liquidity measures we find support for a moving window specification. The market average proxy of systematic liquidity produces the same degree of commonality, but does not have the same ability to explain stock returns as the PCA-based estimates.

Determinants of Limit Order Cancellations

We investigate the economic rationale behind limit order cancellations from the perspective of liquidity suppliers. We predict that an order is cancelled whenever its expected revenue no longer exceeds the expected cost and we model how order profitability variation can be determined from changes in the state of the order book and the order queue position. Our empirical evidence supports the predictions in general and for orders submitted by high-frequency trading firms in particular. Consistent with our model approach, we find that order cancellation patterns are more consistent with market making than with liquidity demand strategies.

Overestimated Effective Spreads: Implications for Investors

The effective bid-ask spread measured relative to the spread midpoint overstates the true effective bid-ask spread in markets with discrete prices and elastic liquidity demand. The average bias is 13-20% for S&P 500 stocks in general, depending on the estimator used as benchmark, and up to 97% for low-priced stocks. Cross-sectional bias variation across stocks, trading venues, and investor groups can influence research inference. The use of the midpoint also undermines liquidity timing and trading performance evaluations, and can lead non-sophisticated investors to overpay for liquidity. To overcome these problems, the paper proposes new estimators of the effective bid-ask spread.

Liquidity : Systematic Liquidity, Commonality, and High-Frequency Trading

Empirical work investigating commonality in liquidity and systematic liquidity risk utilizes various different estimators of systematic liquidity. This chapter is the first to compare and contrast such estimators. We distinguish two classes of systematic liquidity estimators that both have many followers in the literature: (1) weighted average estimators based on concurrent liquidity shocks and (2) principal components estimators based on both concurrent and past liquidity shocks. Our results show that the simpler weighted average estimators perform at least as well as the more complex principal components estimators. This finding is robust across different evaluation criteria and different underlying liquidity measures.