In an era where financial markets operate at the speed of light, a new study from the Bank for International Settlements (BIS) raises provocative questions about the real-world impacts of high-frequency trading (HFT). Published in September 2025 as BIS Working Paper No. 1290, “The Speed Premium: High-Frequency Trading and the Cost of Capital” examines whether the ultra-fast trading strategies of HFT firms ultimately benefit or burden the broader economy. Authored by Matteo Aquilina, Gbenga Ibikunle, Khaladdin Rzayev, and Xuesi Wang, the 47-page paper uses historical NASDAQ upgrades as natural experiments to reveal a nuanced picture: While HFT enhances liquidity for some stocks, it often increases the cost of capital overall, with heterogeneous effects based on stock characteristics. This analysis comes at a critical time, as regulators worldwide grapple with the implications of algorithmic dominance in markets, where HFT accounts for over 50% of U.S. equity volume. Amid debates on financial innovation’s role in economic growth, the paper’s findings suggest that speed isn’t always a virtue—sometimes, it comes at a premium that slows down capital allocation.
The study builds on a growing body of research questioning whether HFT’s millisecond advantages translate to societal gains or merely extract rents from slower participants. As markets evolve with AI and quantum computing on the horizon, understanding HFT’s effects on corporate financing costs is essential for investors, policymakers, and firms alike. This article summarizes the paper’s methodology, key results, implications for market structure, and profiles the authors, providing a comprehensive overview of this timely contribution to financial economics.
The Core Question: Does HFT Raise the Cost of Capital?
At its heart, the paper investigates a fundamental puzzle: When trading accelerates to nanoseconds, does it make capital cheaper or more expensive for companies? The authors argue that HFT, by dominating price discovery and liquidity provision, alters stock risk profiles in ways that affect investor-required returns—the cost of capital.
Using two NASDAQ technological shocks as exogenous events, the study employs a difference-in-differences framework to isolate HFT’s causal impacts. The first shock is the 2005 introduction of co-location services, allowing firms to place servers near exchange data centers for latency advantages. The second is the 2010 latency reduction upgrade, further compressing order processing times. These events serve as proxies for HFT intensification, as faster traders disproportionately benefit from such improvements.
The baseline finding is striking: On average, HFT leads to a higher cost of capital. However, this isn’t uniform. For low-beta stocks (those less sensitive to market movements), HFT amplifies systematic risk by making them more responsive to market-wide information through correlated, speed-driven trades. This beta increase raises required returns, elevating financing costs. Conversely, for highly liquid stocks, HFT reduces the liquidity premium—investors demand less compensation for illiquidity risks—lowering the cost of capital.
A robustness check using Hong Kong’s unfragmented market (via the 2014 Orion Central Gateway upgrade) confirms these effects aren’t artifacts of U.S. fragmentation. Across borders and structures, HFT’s influence persists, suggesting broader policy relevance.
Key Findings: Heterogeneous Impacts and Mechanisms
The paper’s empirical rigor shines in its granular analysis. Drawing on NASDAQ data from 2005-2010 and Hong Kong from 2014, the authors compute cost of capital using implied metrics from analyst forecasts and realized returns.
- Overall Effect: HFT is associated with a 0.5-1% increase in average cost of capital, potentially adding billions in annual financing burdens economy-wide.
- Beta Channel: Low-beta stocks see betas rise by 0.1-0.2 post-HFT shocks, as algorithms amplify reactions to macro news, making “safe” stocks riskier.
- Liquidity Channel: For top-liquidity decile stocks, HFT tightens spreads and depth, cutting liquidity premiums by 0.3-0.5%, benefiting large caps.
- External Validity: Hong Kong results mirror NASDAQ, ruling out fragmentation as the driver—HFT’s speed effects are intrinsic.
Mechanisms explored include HFT’s role in adverse selection (faster informed trading) and inventory management (quicker risk offloading). The authors control for confounding factors like firm size, book-to-market, and profitability, ensuring causality.
These heterogeneous effects have profound implications: Smaller, low-beta firms—often innovators—face higher hurdles, potentially stifling growth, while mega-caps enjoy cheaper capital, exacerbating inequality.
Broader Implications: Policy and Market Design in the Age of Speed
The paper’s conclusions challenge the narrative that HFT unequivocally improves markets. While prior studies (e.g., Brogaard et al., 2014) highlight liquidity benefits, this work links microstructure to macro outcomes, showing speed can distort capital allocation.
For regulators, it suggests targeted interventions: Speed bumps or batch auctions (as in Budish et al., 2015) could mitigate HFT’s risk-amplifying effects without sacrificing liquidity gains. In fragmented markets like the U.S., harmonizing rules might amplify benefits.
Investors should recalibrate: Portfolios heavy in low-beta stocks may underperform in HFT-dominated regimes, while liquidity-focused strategies thrive. Amid 2025’s AI trading surge, the “speed premium” could widen, urging diversification into non-equity assets.
The study also nods to Cochrane’s (2013) skepticism: Millisecond price discovery may not guide real investments efficiently, potentially hindering economic progress.
Meet the Authors: Expertise in Market Microstructure and Policy
The paper’s insights stem from a collaborative team blending academic rigor and policy experience.
- Matteo Aquilina, Senior Economist at the BIS, focuses on market microstructure and financial stability. His work on HFT “arms races” has influenced global regulation. BIS Profile
- Gbenga Ibikunle, Professor and Chair of Finance at the University of Edinburgh, directs the Edinburgh Centre for Financial Innovations. A Fellow of the Royal Society of Edinburgh, his research spans sustainable finance and market efficiency. University of Edinburgh Profile
- Khaladdin Rzayev, Assistant Professor at Koç University and Visiting Fellow at LSE’s Systemic Risk Centre, specializes in HFT and asset pricing. His papers on trading latency have appeared in top journals. Koç University Profile
- Xuesi Wang, PhD Candidate in Finance at the University of Edinburgh, researches macro-finance linkages. Her work on dividend forecasting with machine learning complements the paper’s empirical approach. University of Edinburgh Profile
For the full paper: https://www.bis.org/publ/work1290.pdf
In summary, BIS Working Paper 1290 illuminates HFT’s double-edged sword, urging a rethink of market speed’s economic value. As trading accelerates, so must our understanding of its costs.
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