News Trading and Speed

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1 News Trading and Speed Ioanid Roşu (HEC Paris) with Johan Hombert and Thierry Foucault Paris December 12, 2012 Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

2 Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

3 Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

4 Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

5 High Frequency Traders on News Flow of news is enormous and virtually continuous Some traders able to react in real time and trade on news at high frequency High Frequency Traders on News (HFTNs) Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

6 Issues How do HFTNs trade on news? What are the effects of HFTNs on liquidity, volatility, price discovery? How to detect these effects empirically? Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

7 What we do Informed trader s informational advantage has 2 components: 1. More precise info As in Kyle (1985) and most of literature 2. Faster reaction to public info New contribution Main result Even an infinitesimal speed advantage generates large effects on equilibrium trading strategies and market performance Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

8 Some related literature Models of informed trading Kyle 1985; Kim and Verrecchia 1994; Back and Pedersen 1998; Chau and Vayanos 2008 No speed issue Models of traders with speed advantage Liquidity providers: Jovanovic and Menkveld 2011; Cartea and Penalva 2011 Liquidity takers: Biais, Foucault and Moinas 2011 Empirical analysis of HFT Hendershott, Jones and Menkveld 2011; Menkveld 2011; Brogaard, Hendershott and Riordan 2012; Kirilenko, Kyle, Samadi and Tuzun 2011; Hasbrouck and Saar 2011; Zhang 2012 Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

9 Model: Asset Continuous time t [0, 1] Asset liquidated at t = 1 Fundamental value follows random walk process v t Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

10 Model: Market participants Informed trader: observes v 0 and dv t Risk-neutral: maximizes expected profits Market order dxt Uninformed noise trader Market order dut Market maker: observes dz t = dv t + de t Observes aggregate order flow dyt = dx t + du t Competitive and risk-neutral: set price equal to expected value Informed trader has more precise info Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

11 Model: Timing During (t, t + dt]: 1. Informed trader observes dv t 2. Trading: MM sets quote q t and price impact λ t 2. Trading: Informed trader submits dx t and noise trader du t 2. Trading: MM executes OF at price p t+dt = q t + λ t (dx t + du t ) 3. Market maker observes dz t = dv t + de t Informed trader has higher speed Compare to benchmark with no speed advantage: switch 2. and 3. Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

12 Equilibrium: Optimal trading strategy Informed trader s optimal strategy: dx t = β t (v t q t )dt + γ t dv t β part = forecast error component Trade on the MM s pricing error, as in literature Correlated with long-run return γ part = news trading component Trade on the innovation in fundamental, to anticipate next quote update Correlated with short-run return In benchmark: γ t = 0 Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

13 Empirical implications: Informed order flow Compared to benchmark, in model with speed advantage: Informed trading volume is larger by an order of magnitude Level trading is a drift News trading is stochastic fraction of volume due informed trading > 0 versus = 0 in benchmark Auto-correlation of informed order flow = 0 versus > 0 in benchmark Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

14 Empirical implications: Price impact Compared to benchmark, in model with speed advantage: Immediate price impact (Kyle s λ) is higher Speed advantage = additional source of adverse selection Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

15 Empirical implications: Price discovery Informational efficiency: E [(v t p t ) 2 ] Compared to benchmark, in model with speed advantage: 1. Price changes more correlated with innovation in v: Cov(dp t, dv t ) Price better tracks news 2. but less correlated with pricing error: Cov(dp t, v t p t ) Because trade less aggressively on pricing error when also trade on news High frequency news trading is unrelated to fundamental Overall, informational efficiency is unaffected Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

16 Empirical implications: Volatility Volatility can be decomposed into two components (Hasbrouck 1991) Var(dp t ) = Var(p t+dt q t ) + Var(q t p t ) }{{}}{{} Trades Quotes Compared to benchmark, in model with speed advantage: 1. Volatility due to trades is higher Because trades are more informative about imminent news 2. but volatility due to news is lower Quotes less sensitive to news because info already incorporated through trading Overall, volatility is unaffected Equal to fundamental volatility Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

17 Empirical implications: The determinants of HFTNs If precision of market maker s signal improves (Var(de t ) lower): Informed trader trades more aggressively on news Better anticipates next quote update Liquidity improves Less info asymmetry Spurious correlation between HFTNs and liquidity if econometrician does not control for precision of MM info Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

18 Measuring Price Impact Following Hasbrouck (1991), empiricists often estimate VAR models Our model implies r t = λ x t + µ γ ( 1 γρ ) xt 1 + ε t, x t = η t, The VAR produces permanent price impact λ + µ γ (1 γρ), which overestimates the correct value, which is λ Need to adjust permanent price impact when there is public information Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

19 State Space Model Brogaard, Hendershott, and Riordan (2012), Menkveld, Koopman, and Lucas (2007) decompose the price into permanent and transitory components p t = m t + s t Our model implies state space model with m t = v t, s t = p t v t : with m t = κ x t + ε t, s t+1 = φs t + ψ x t + η t κ = 1 γ > 0, φ = 1 νβ t, ψ = 1 (1 µ νγ) < 0 γ HFTN s order flow negatively correlated with pricing error: ψ < 0 The HFTN has market power, so does not impound in prices all of dv Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

20 Conclusion Speed advantage gives rise to news trading Caveat Informed trades much more volatile Informed traders much less autocorrelated Market less liquid Returns more correlated with news, less with fundamental (market efficiency stays the same) Vol coming from trading increases, vol unrelated to trading decreases (total vol stays the same) Paper on High Frequency Trading on News (HFTNs), not all HFTs Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

21 Formulas Speed advantage Benchmark Quote: q t q t = p t + µ t dz t Informed trade: dx t = β t (v t q t ) }{{} F.E. trading + γ t dv t }{{} News trading dx t = β t (v t q t ) Execution price: p t+dt = q t + λ t dy t p t+dt = q t + λ t dy t Quote update: q t+dt = p t+dt + µ t (dz t ρ t dy t ) }{{} Unexpected part of signal Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

22 Other Sources of News Trading Martinez and Roşu (2012): News trading is generated by Ambiguity aversion about the initial value v0 Speed advantage over other news traders (regardless of the reason) Ladley and Roşu (2012) Risk aversion generates news trading, but effect not strong Ioanid Roşu (HEC Paris) News Trading and Speed Paris Microstructure, / 22

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