We investigate the possibility of statistical evaluation of the market completeness for discrete time stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one. The paper shows that market inc…
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48 results for “market modeling”
The possibility of statistical evaluation of the market completeness and incompleteness is investigated for continuous time diffusion stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one.…
Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratioq-fin.ST
Research predicts money market volume based on capital market and bank rates ratio.
problem Understanding the influence of capital market and bank rates on money market instruments.
method Correlation matrix and time series model to predict money market volume.
result Predictive model for money market instrument volume based on historical data.
Predicts stock market crashes using rational bubble model.
problem Financial market crashes prediction.
method White box model based on rational bubble theory.
result Successfully predicts major crashes in Dow Jones and Bitcoin markets.
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
Simulating and analyzing a sparse order book: an application to intraday electricity marketsq-fin.TR
Model simulates sparse order books in illiquid markets.
problem Inaccurate LOB models in illiquid markets.
method Inhomogeneous Poisson process for order arrivals and cancellations.
result Enhanced understanding of LOB dynamics in illiquid markets.
Interest rate market models, like the LIBOR market model, have the advantage that the basic model quantities are directly observable in financial markets. Inflation market models extend this approach to inflation markets, where zero-coupon and year-on-year inflation-indexed swaps are the basic observable products. For …
As demonstrated during the recent financial crisis, regulators require additional analytical tools to assess systemic risk in the financial sector. This paper describes one such tool; namely a novel market modeling and analysis capability. Our model builds upon two leading market models: one which emphasizes market mic…
The paper proposes a new algorithm for dealer markets that incorporates hedging and market impact.
problem How to manage risk and quote prices in dealer markets with limited internalization.
method Develops a mathematical model that allows dealers to hedge part of their inventory and adjust quotes based on inventory size.
result Dealers can internalize risk within a certain inventory range and externalize it outside of that range, optimizing their quoting strategy.
This study presents an agent-based computational cross-market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index futures to simulate this structure. This model allows heterogeneous investors to make investment decisions …
Modeling market makers' quoting strategies to understand price impact.
problem Understanding how price impact arises from market makers' quoting strategies.
method Modeling market making as a dynamic auction using Stochastic Differential Games and finding Nash Equilibrium.
result The price impact function derived from market makers' strategies matches the Almgren-Chriss model.
PRIME models cryptocurrency exchange market impact.
problem Understanding and predicting market impact in cryptocurrency exchanges.
method Developed a multi-agent simulation to model market impact.
result Allows better estimation of market slippage and knock-on consequences.
A q-spin Potts model of markets: Gain-loss asymmetry in stock indices as an emergent phenomenonphysics.soc-ph
Study shows gain-loss asymmetry in stock indices using a q-spin Potts model.
problem Understanding the dynamics of stock indices in complex markets.
method Developed a q-spin Potts model to represent stock market dynamics.
result Observed a self-organized gain-loss asymmetry in stock indices.
Evaluating the Financial Market Function in Prewar Japan using a Time-Varying Parameter Modelq-fin.ST
This study evaluates prewar Japanese financial market efficiency using time-varying models.
problem Determining when prewar Japanese financial market lost its price formation function.
method Time-varying parameter model, generalized least squares-based time-varying vector autoregressive model.
result The prewar Japanese financial market lost its price formation function in 1932.
We present a simple model of a non-equilibrium self-organizing market where asset prices are partially driven by investment decisions of a bounded-rational agent. The agent acts in a stochastic market environment driven by various exogenous "alpha" signals, agent's own actions (via market impact), and noise. Unlike tra…
In most OTC markets, a small number of market makers provide liquidity to other market participants. More precisely, for a list of assets, they set prices at which they agree to buy and sell. Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money while …
We attempt to explain stock market dynamics in terms of the interaction among three variables: market price, investor opinion and information flow. We propose a framework for such interaction and apply it to build a model of stock market dynamics which we study both empirically and theoretically. We demonstrate that th…
Performative Market Makingq-fin.TR
Financial models shape markets through performativity, creating self-fulfilling prophecies.
problem Lack of mathematical formulation for performativity in financial markets.
method Embedding the model in the market process, creating a closed feedback loop.
result Performative market makers can reverse engineer dominant strategies and arbitrage them.
Optimized execution model using interbank and internal liquidity.
problem Minimizing market impact in trading.
method Integrates interbank limit and market orders with internal market-making liquidity.
result Reduces market impact and improves execution efficiency.
Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecastingcs.CE
Improved crypto market forecasting using historical price reactions to tweets.
problem Challenges in inferring market impact from human sentiment labels.
method Market-derived labeling approach to assign tweet sentiment labels based on historical price trends. Fine-tuned language model with context-aware prompt-tuning.
result 89.6% accuracy on Bitcoin news events, outperforming traditional fusion models.
This study models AI traders' impact on financial markets using a multi-agent framework.
problem Lack of a comprehensive model to assess AI traders' effects on market price formation and volatility.
method Developed a multi-agent market model with microfoundations of the GARCH model.
result Validated the model through simulations and analyzed AI traders' impact.
Study applies market microstructure to Cuban informal currency market, finding market makers improve liquidity.
problem Understanding dynamics of informal currency markets.
method Modeling bid/ask intentions using Limit Order Book, applying Avellaneda-Stoikov model with Market Maker.
result Market Maker improves market quality and bid/ask dynamics.
Designing a financial market that works well is very important for developing and maintaining an advanced economy, but is not easy because changing detailed rules, even ones that seem trivial, sometimes causes unexpected large impacts and side effects. A computer simulation using an agent-based model can directly treat…
Evology models US equity mutual funds interactions for investment strategies.
problem Understanding complex interactions in financial markets.
method Agent-based model (ABM) of US stock market participants and their strategies.
result Trading strategies interact with other market participants and conditions.
Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Daysq-fin.GN
Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
ABM simulates OTC government bond market dynamics, enhancing liquidity and stability.
problem Understanding and ensuring market stability and liquidity in OTC government bond markets.
method Developed a bespoke ABM to simulate market-maker interactions and test hypotheses.
result Greater agent diversity enhances market liquidity and reducing market-making costs improves stability.
Efficiently calibrates SABR/LIBOR models to real market caplets and swaptions data.
problem Calibration of stochastic volatility models to real market data.
method Proposes a parallelized simulated annealing algorithm for multi-GPUs.
result Numerical results show advantages of using multi-GPUs for SABR/LIBOR model calibration.
Optimal energy trading strategy for intraday markets using Hawkes processes.
problem Optimal execution in intraday energy markets with specific trading patterns.
method Calibrated Hawkes process model with transient price impact.
result Substantial cost reductions in TWAP and VWAP benchmarks.
The paper develops models for asset returns based on market conditions and uses them to construct a trading policy.
problem Developing a robust trading strategy based on market conditions.
method The authors create stratified models of asset return mean and covariance, fit these models using Laplacian regularization, and combine them with a Markowitz optimization method.
result The trading policy performs well out of sample and can be scaled to larger problems.
We present a new model for prediction markets, in which we use risk measures to model agents and introduce a market maker to describe the trading process. This specific choice on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objective, despi…
Study predicts electricity prices using LSTM models with feature selection, considering market coupling.
problem Accurate day-ahead electricity price forecasting in coupled markets.
method Hybrid LSTM-based deep learning models with feature selection algorithms.
result Proposed models achieve considerably accurate results in Nordic market.
Market-maker optimizes quotes based on strategic market-takers' behavior.
problem Optimizing market-making strategies in a strategic trading environment.
method Mean-field game approach to model strategic market-takers and derive optimal controls.
result Derivation of optimal controls for market-maker and market-taker.
TRADES generates realistic market simulations for financial modeling.
problem Generating realistic and responsive market simulations for financial tasks.
method TRADES uses a transformer-based denoising diffusion probabilistic engine to generate time series order flows conditioned on market state.
result TRADES improves market simulation metrics by 3.27-3.48 over state-of-the-art (SoTA) methods.
Study models crypto markets using multi-agent reinforcement learning.
problem Emulating crypto market dynamics and behaviors.
method Multi-agent reinforcement learning (MARL) with RL techniques.
result Model accurately emulates crypto market microstructure and behaviors.
Paper uses AI to predict tail risks in US financial markets.
problem Predicting extreme risks in US financial markets.
method Multivariate multilevel CAViaR model optimized by gradient descent and genetic algorithm.
result Credit market's spillover effect on stock market is greater and longer-lasting.
We introduce solvable stochastic dealer models, which can reproduce basic empirical laws of financial markets such as the power law of price change. Starting from the simplest model that is almost equivalent to a Poisson random noise generator, the model becomes fairly realistic by adding only two effects, the self-mod…
Financial Wind Tunnel generates versatile market data for model testing.
problem Inconsistent market dynamics across different scales and sources.
method Retrieval-augmented diffusion-based simulator integrating macro and micro patterns.
result Enhanced performance and adaptability of downstream models in complex markets.
Model financial markets with social media influences using hierarchical networks.
problem Understanding social media's impact on financial markets.
method Agent-based model with hierarchical influence network.
result Model accurately simulates real-world financial market behaviors.
New method identifies informed traders in prediction markets.
problem How information is incorporated into market prices is unknown.
method Kyle model applied to field experiment prediction market data.
result Traders with significant price impact are identified as informed.
Study shows marketable order routing to wholesalers benefits all traders, leading to lower market depth and price volatility.
problem Determining the preference of retail traders for marketable order routing.
method Two models: one for market makers competing for retail order flow (Bertrand model) and another for price-taking competitive liquidity providers (open exchange model).
result Routing marketable orders to wholesalers is preferred by all traders, leading to mean reverting inventories and lower market depth.
There are two schools of thought regarding market impact modeling. On the one hand, seminal papers by Almgren and Chriss introduced a decomposition between a permanent market impact and a temporary (or instantaneous) market impact. This decomposition is used by most practitioners in execution models. On the other hand,…
This study examines the adaptive market hypothesis (AMH) in Japanese stock markets (TOPIX and TSE2). In particular, we measure the degree of market efficiency by using a time-varying model approach. The empirical results show that (1) the degree of market efficiency changes over time in the two markets, (2) the level o…
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Eventscs.AI
We develop a model of how information flows into a market, and derive algorithms for automatically detecting and explaining relevant events. We analyze data from twenty-two "political stock markets" (i.e., betting markets on political outcomes) on the Iowa Electronic Market (IEM). We prove that, under certain efficienc…
Micro-economic Analysis of the Physical Constrained Markets: Game Theory Application to Competitive Electricity Marketsphysics.soc-ph
Competition has been introduced in the electricity markets with the goal of reducing prices and improving efficiency. The basic idea which stays behind this choice is that, in competitive markets, a greater quantity of the good is exchanged at a lower and a lower price, leading to higher market efficiency. Electricity …
Financial market dynamics compared to thermodynamics.
problem Understanding the dynamics of financial markets through thermodynamic principles.
method Analogy with Szilárd information engine to derive market temperature and information extraction.
result Informed traders' gains are bounded by market temperature and information.
Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
Modeling financial chaos with market makers' risk appetite.
problem Unpredictable price changes in financial markets.
method Using Hamiltonian approach with anharmonic oscillators and nonlinear coupling.
result Market makers' risk appetite determines chaotic dynamics in financial markets.