Modern physics has demonstrated that matter behaves very differently as it approaches the speed of light. This paper explores the implications of modern physics to the operation and regulation of financial markets. Information cannot move faster than the speed of light. The geographic separation of market centers means…
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48 results for “market speed”
Study shows time matters in automated trading, improving simple strategies over complex ones.
problem Effects of reaction speed and trading urgency on automated trading strategies.
method Simulated financial markets with public limit order book and continuous double auction matching. Examined reaction speed and trading urgency.
result Simple strategies outperform complex ones when considering reaction speed and trading urgency.
Exchanges implement intentional trade delays to limit the harmful impact of low-latency trading. Do such "speed bumps" curb investment in fast trading technology? Data is scarce since trading technologies are proprietary. We build an experimental trading platform where participants face speed bumps and can invest in fa…
Exchanges acquire excess processing capacity to accommodate trading activity surges associated with zero-sum high-frequency trader (HFT) "duels." The idle capacity's opportunity cost is an externality of low-latency trading. We build a model of decentralized exchanges (DEX) with flexible capacity. On DEX, HFTs acquire …
Investigates market dynamics with informed traders and high-frequency traders.
problem Trading large orders in a market with multiple high-frequency traders.
method Analyzes a three-period Kyle's model with a normal-speed informed trader and multiple anticipatory high-frequency traders under different inventory pressures.
result Surprising results: improving HFTs' speed or prediction can harm them but benefit the informed trader.
Assessing the stability of economic systems is a fundamental research focus in economics, that has become increasingly interdisciplinary in the currently troubled economic situation. In particular, much attention has been devoted to the interbank lending market as an important diffusion channel for financial distress d…
Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor, the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its accuracy. Relying o…
In a recent paper, Alfonsi, Fruth and Schied (AFS) propose a simple order book based model for the impact of large orders on stock prices. They use this model to derive optimal strategies for the execution of large orders. We apply these strategies to an agent-based stochastic order book model that was recently propose…
HSR reduces analyst earnings forecast errors by lowering travel friction.
problem How HSR connectivity affects analyst earnings forecast errors in China.
method Firm-year panel data from 2008-2019; placebo test to rule out pre-existing trends.
result HSR reduces analyst earnings forecast errors after connectivity, not before.
We study the optimal execution of market and limit orders with permanent and temporary price impacts as well as uncertainty in the filling of limit orders. Our continuous-time model incorporates a trade speed limiter and a trader director to provide better control on the trading rates. We formulate a stochastic control…
GPU acceleration speeds up financial machine learning training time.
problem Time-intensive classifier training in financial machine learning.
method Deployed NVIDIA GPUs for parallel high-speed arithmetic operations.
result Significantly faster training time achieved.
Study examines how institutional differences and crises affect volatility in ASEAN stock markets.
problem Understanding how institutional differences and crises impact volatility in emerging Asian stock markets.
method By-window EGARCH/TGARCH analysis of daily stock index returns for Indonesia, Malaysia, and the Philippines from 2010 to 2024.
result All three markets show strong volatility persistence and fat-tailed returns; crises increase persistence and asymmetry, while tail thickness rises.
We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simula…
Study no-arbitrage conditions in 1D diffusion markets with interest rates.
problem Determining no-arbitrage conditions in 1D diffusion markets with interest rates.
method Established deterministic criteria for no-arbitrage notions in terms of scale function and speed measure.
result Revealed various effects, e.g., NIP not excluded by reflecting boundaries.
Nearly one-half of all trades in financial markets are executed by high-speed, autonomous computer programs -- a type of trading often called high-frequency trading (HFT). Although evidence suggests that HFT increases the efficiency of markets, it is unclear how or why it produces this outcome. Here we create a simple …
In this study, we extend the optimal execution problem with convex market impact function studied in Kato (2014) to the case where the market impact function is S-shaped, that is, concave on and convex on for some . We study the corresponding Hamilton-Jacobi-…
We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then st…
Consider the problem of pricing options on forwards in energy markets, when spot prices follow a geometric multi-factor model in which several rates of mean reversion appear. In this paper we investigate the role played by slow mean reversion when pricing and hedging options. In particular, we determine both upper and …
Investigates optimal strategies for market makers using internal liquidity.
problem Optimizing strategies for market makers with internal liquidity.
method Investigates optimal multi-objective strategy for market makers with internal liquidity.
result Draws important qualitative insights for real-world trading.
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithmq-fin.CP
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a PGA and visualise the results using disjoint minimal spanning trees (MSTs). We demonstrate that our GP…
Develops a stochastic approach to financial market delays.
problem Modeling delays in financial markets with multiple assets.
method Introduces a general stochastic framework for information and order execution delays.
result Delayed markets maintain fundamental asset pricing theorems and no asymptotic free lunch condition.
Trading with the Crowdq-fin.TR
Game theory models how agents trade in a risky asset considering price impact and a common signal.
problem Modeling how financial agents liquidate assets in a risky market with price impact and a common signal.
method Formulated and solved a multi-player stochastic differential game and mean field game.
result Equilibrium strategies reveal how agents adjust the predictive trading signal to price impact.
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
Quantum computing speeds up interest rate derivative pricing using LMM.
problem Challenges in pricing interest rate derivatives, especially caps.
method Hybrid classical-quantum approach using quantum amplitude estimation.
result Quantum computing improves convergence in pricing interest rate derivatives.
Study increasing profits in a flexible financial market model.
problem Characterize increasing profits in a 1D diffusion market with interest rates.
method Characterize increasing profits using an auxiliary deterministic signed measure and a canonical trading strategy.
result Existence and characterization of increasing profits in terms of and .
Analyzes financial return distributions over various time scales.
problem Understanding the changing nature of financial return distributions over time.
method Modeling return distributions using power-law, stretched exponential, and q-Gaussian functions.
result The 'inverse-cubic power-law' is still a good fit for short-term returns, but market dynamics are more complex.
Study shows frequent 'stock' mentions on Twitter correlate with stock market declines.
problem Understanding how social media impacts financial markets during pandemics.
method Collected over 26 million tweets about COVID-19, analyzed using natural language processing.
result More frequent use of 'stock' in tweets predicts substantial declines in stock market indices.
High-frequency trading strategy boosts battery storage profits.
problem Maximizing revenue for battery energy storage systems in intraday markets.
method Adapted dynamic programming for continuous intraday markets, considering limit order book dynamics.
result Dynamic programming strategy outperforms standard re-optimization methods, increasing profits by 58% and 14% respectively.
A recently introduced Importance Sampling strategy based on a least squares optimization is applied to the Monte Carlo simulation of Libor Market Models. Such Least Squares Importance Sampling (LSIS) allows the automatic optimization of the sampling distribution within a trial class by means of a quick presimulation al…
Financial exchanges provide incentives for limit order book (LOB) liquidity provision to certain market participants, termed designated market makers or designated sponsors. While quoting requirements typically enforce the activity of these participants for a certain portion of the day, we argue that liquidity demand t…
FinRL-Meta creates diverse market environments for DRL in finance.
problem Inaccurate financial data and diverse market environments challenge DRL in finance.
method Open-source data processing tools, hundreds of market environments, and multiprocessing.
result FinRL-Meta improves DRL accuracy and speed in financial simulations.
The paper sets criteria for no arbitrage in complex financial models.
problem Determining conditions for the absence of arbitrage in financial markets.
method Established deterministic conditions for no arbitrage, NUPBR, and NFLVR in diffusion market models.
result Provided criteria in terms of scale function and speed measure.
A time-varying cointegration model for foreign exchange rates is presented. Unlike previous studies, we allow the loading matrix in the vector error correction (VEC) model to be varying over time. Because the loading matrix in the VEC model is associated with the speed at which deviations from the long-run relationship…
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
problem Cognitive load's effect on financial market information processing.
method Developed a theoretical framework and tested it with exogenous disclosure complexity variation.
result Cognitive load significantly impairs price discovery, particularly for less sophisticated investors.
Deep learning models predict stock prices with high accuracy and speed.
problem Precise prediction of stock prices in an efficient market.
method Design and training of ten deep learning regression models.
result Models achieve high accuracy in forecasting stock prices of an auto sector company.
We show that prices and shortfall risks of game (Israeli) barrier options in a sequence of binomial approximations of the Black--Scholes (BS) market converge to the corresponding quantities for similar game barrier options in the BS market with path dependent payoffs and the speed of convergence is estimated, as well. …
AI speeds up hydrogen fuel cell stack development time.
problem Reducing time to market for hydrogen fuel cell stacks.
method Generative Adversarial Networks for data augmentation.
result Reduced testing time from 1000 hours to 3 hours.
We consider a single security market based on a limit order book and two investors, with different speeds of trade execution. If the fast investor can front-run the slower investor, we show that this allows the fast trader to obtain risk free profits, but that these profits cannot be scaled. We derive the fast trader's…
New optimizers improve stock market forecasting accuracy.
problem Forecasting S&P 500 Index returns with MambaStock model.
method Evaluation of various optimizers (Adam, RMSProp, Lion, Roaree).
result Roaree optimizers combine faster training with reduced oscillations.
Estimates boundaries for acceptable bilateral gamma risk in financial markets.
problem Determining the compensation needed for risky future cash flows to be considered acceptable.
method Statistical inference from market prices and derivatives, using prospect theory.
result Upper and lower boundaries for bilateral gamma risk are estimated and tested against market data.
Escaping the Brownian stalkersq-fin.TR
We propose a simple model for the behaviour of longterm investors on a stock market, consisting of three particles, which represent the current price of the stock and the opinion of the buyers, respectively sellers, about the right trading price. As time evolves, both groups of traders update their opinions with respec…
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
problem Understanding responsive speeds of market participants in high-frequency trading.
method Multi-kernel Hawkes models with conditional Hessian analysis for optimization.
result Existence of multi-kernels (UHF, VHF, HF) in high-frequency price dynamics.
While historically, economists have been primarily occupied with analyzing the behaviour of the markets, electronic trading gave rise to a new class of unprecedented problems associated with market fairness, transparency and manipulation. These problems stem from technical shortcomings that are not accounted for in the…
FlowHFT learns adaptive trading strategies from multiple models for diverse market conditions.
problem Traditional HFT models are limited by specific market conditions and cannot adapt to dynamic markets.
method FlowHFT uses flow matching policy to learn from multiple expert models and adapt to various market scenarios.
result FlowHFT consistently outperforms individual expert models in multiple market conditions.
Newton Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilitiesq-fin.CP
A deep learning model speeds up computation of numerous implied volatilities.
problem Frequent computation of numerous implied volatilities using iteration methods like Newton-Raphson reaches processing speed limits.
method Emulated Newton-Raphson method using PyTorch and optimized with TensorRT.
result Up to 1,000 times faster than a benchmark implementation of Newton-Raphson.
The expected low market penetration of connected vehicles (CVs) in the near future could be a constraint in estimating traffic flow parameters, such as average travel speed of a roadway segment and average space headway between vehicles from the CV broadcasted data. This estimated traffic flow parameters from low penet…
Bayesian theory explains market impact of large trades.
problem Reduction of price impact from large trades.
method Bayesian approach incorporating all trade information.
result Recovery of market impact laws including square-root and linear regimes.
High-speed computerized trading, often called "high-frequency trading" (HFT), has increased dramatically in financial markets over the last decade. In the US and Europe, it now accounts for nearly one-half of all trades. Although evidence suggests that HFT contributes to the efficiency of markets, there are concerns it…