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on Market Microstructure |
| By: | Alif Aqsha; Philippe Bergault; Leandro S\'anchez-Betancourt |
| Abstract: | We find the equilibrium contract that an automated market maker (AMM) offers to their strategic liquidity providers (LPs) in order to maximize the order flow that gets processed by the venue. Our model is formulated as a leader-follower stochastic game, where the venue is the leader and a representative LP is the follower. We derive approximate closed-form equilibrium solutions to the stochastic game and analyze the reward structure. Our findings suggest that under the equilibrium contract, LPs have incentives to add liquidity to the pool only when higher liquidity on average attracts more noise trading. The equilibrium contract depends on the external price, the pool reference price, and the pool reserves. Our framework offers insights into AMM design for maximizing order flow while ensuring LP profitability. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.22502 |
| By: | David Schaurecker; David Wozabal; Nils L\"ohndorf; Thorsten Staake |
| Abstract: | Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper introduces and evaluates an automated high-frequency trading strategy for battery energy storage systems trading on the intraday market for power while explicitly considering the dynamics of the limit order book, market rules, and technical parameters. The standard rolling intrinsic strategy is adapted for continuous intraday electricity markets and solved using a dynamic programming approximation that is two to three orders of magnitude faster than an exact mixed-integer linear programming solution. A detailed backtest over a full year of German order book data demonstrates that the proposed dynamic programming formulation does not reduce trading profits and enables the policy to react to every relevant order book update, enabling realistic rapid backtesting. Our results show the significant revenue potential of high-frequency trading: our policy earns 58% more than when re-optimizing only once every hour and 14% more than when re-optimizing once per minute, highlighting that profits critically depend on trading speed. Furthermore, we leverage the speed of our algorithm to train a parametric extension of the rolling intrinsic, increasing yearly revenue by 8.4% out of sample. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06932 |
| By: | Chutian Ma; Giacinto Paolo Saggese; Paul Smith |
| Abstract: | Market participants regularly send bid and ask quotes to exchange-operated limit order books. This creates an optimization challenge where their potential profit is determined by their quoted price and how often their orders are successfully executed. The expected profit from successful execution at a favorable limit price needs to be balanced against two key risks: (1) the possibility that orders will remain unfilled, which hinders the trading agenda and leads to greater price uncertainty, and (2) the danger that limit orders will be executed as market orders, particularly in the presence of order submission latency, which in turn results in higher transaction costs. In this paper, we consider a stochastic optimal control problem where a risk-averse trader attempts to maximize profit while balancing risk. The market is modeled using Brownian motion to represent the price uncertainty. We analyze the relationship between fill probability, limit price, and order submission latency. We derive closed-form approximations of these quantities that perform well in the practical regime of interest. Then, we utilize a mean-variance method where our total reward function features a risk-tolerance parameter to quantify the combined risk and profit. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.00846 |
| By: | Darren Shannon; Jin Gong; Barry Sheehan |
| Abstract: | Public announcement dates are used in the green bond literature to measure equity market reactions to upcoming green bond issues. We find a sizeable number of green bond announcements were pre-dated by anonymous information leakages on the Bloomberg Terminal. From a candidate set of 2, 036 'Bloomberg News' and 'Bloomberg First Word' headlines gathered between 2016 and 2022, we identify 259 instances of green bond-related information being released before being publicly announced by the issuing firm. These pre-announcement leaks significantly alter the equity trading dynamics of the issuing firms over intraday and daily event windows. Significant negative abnormal returns and increased trading volumes are observed following news leaks about upcoming green bond issues. These negative investor reactions are concentrated amongst financial firms, and leaks that arrive pre-market or early in market trading. We find equity price movements following news leaks can be explained to a greater degree than following public announcements. Sectoral differences are also observed in the key drivers behind investor reactions to green bond leaks by non-financials (Tobin's Q and free cash flow) and financials (ROA). Our results suggest that information leakages have a strong impact on market behaviour, and should be accounted for in green bond literature. Our findings also have broader ramifications for financial literature going forward. Privileged access to financially material information, courtesy of the ubiquitous use of Bloomberg Terminals by professional investors, highlights the need for event studies to consider wider sets of communication channels to confirm the date at which information first becomes available. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.03311 |
| By: | Niklas Kroner |
| Abstract: | I provide evidence that investors' attention allocation plays a critical role in how financial markets incorporate macroeconomic news. Using intraday data, I document a sharp increase in the market reaction to Consumer Price Index (CPI) releases during the 2021-2023 inflation surge. Bond yields, market-implied inflation expectations, and other asset prices exhibit significantly stronger responses to CPI surprises, while reactions to other macroeconomic announcements remain largely unchanged. The joint reactions of these asset prices point to an attention-based explanation–an interpretation I corroborate throughout the rest of the paper. Specifically, I construct a measure of CPI investor attention and find that: (1) attention was exceptionally elevated around CPI announcements during the inflation surge, and (2) higher pre-announcement attention robustly leads to stronger market reactions. Studying investor attention in the context of Employment Report releases and Federal Reserve announcements, I document a similar importance of attention allocation for market reactions. Lastly, I find that markets tend to overreact to announcements that attract high levels of attention. |
| Keywords: | Macroeconomic News Announcements; Investor Attention; Financial Markets; Inflation; Federal Reserve; High-frequency event study |
| JEL: | E44 E71 G12 G14 G41 |
| Date: | 2025–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-22 |