Looking into informal currency markets as Limit Order Books: impact of market makers ArXiv ID: 2503.03858 “View on arXiv” Authors: Unknown Abstract This study pioneers the application of the market microstructure framework to an informal financial market. By scraping data from websites and social media about the Cuban informal currency market, we model the dynamics of bid/ask intentions using a Limit Order Book (LOB). This approach enables us to study key characteristics such as liquidity, stability and volume profiles. We continue exploiting the Avellaneda-Stoikov model to explore the impact of introducing a Market Maker (MM) into this informal setting, assessing its influence on the market structure and the bid/ask dynamics. We show that the Market Maker improves the quality of the market. Beyond their academic significance, we believe that our findings are relevant for policymakers seeking to intervene informal markets with limited resources. ...

Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market ArXiv ID: 2503.08696 “View on arXiv” Authors: Unknown Abstract Classical asset price forecasting methods primarily rely on numerical data, such as price time series, trading volumes, limit order book data, and technical analysis indicators. However, the news flow plays a significant role in price formation, making the development of multimodal approaches that combine textual and numerical data for improved prediction accuracy highly relevant. This paper addresses the problem of forecasting financial asset prices using the multimodal approach that combines candlestick time series and textual news flow data. A unique dataset was collected for the study, which includes time series for 176 Russian stocks traded on the Moscow Exchange and 79,555 financial news articles in Russian. For processing textual data, pre-trained models RuBERT and Vikhr-Qwen2.5-0.5b-Instruct (a large language model) were used, while time series and vectorized text data were processed using an LSTM recurrent neural network. The experiments compared models based on a single modality (time series only) and two modalities, as well as various methods for aggregating text vector representations. Prediction quality was estimated using two key metrics: Accuracy (direction of price movement prediction: up or down) and Mean Absolute Percentage Error (MAPE), which measures the deviation of the predicted price from the true price. The experiments showed that incorporating textual modality reduced the MAPE value by 55%. The resulting multimodal dataset holds value for the further adaptation of language models in the financial sector. Future research directions include optimizing textual modality parameters, such as the time window, sentiment, and chronological order of news messages. ...

Bayesian Estimation of Corporate Default Spreads ArXiv ID: 2503.02991 “View on arXiv” Authors: Unknown Abstract Risk-averse investors often wish to exclude stocks from their portfolios that bear high credit risk, which is a measure of a firm’s likelihood of bankruptcy. This risk is commonly estimated by constructing signals from quarterly accounting items, such as debt and income volatility. While such information may provide a rich description of a firm’s credit risk, the low-frequency with which the data is released implies that investors may be operating with outdated information. In this paper we circumvent this problem by developing a high-frequency credit risk proxy via corporate default spreads which are estimated from daily bond price data. We accomplish this by adapting classic yield curve estimation methods to a corporate bond setting, leveraging advances in Bayesian estimation to ensure higher model stability when working with small sample data which also allows us to directly model the uncertainty of our predictions. ...

Complex discontinuities of the square root of Fredholm determinants in the Volterra Stein-Stein model ArXiv ID: 2503.02965 “View on arXiv” Authors: Unknown Abstract Fourier-based methods are central to option pricing and hedging when the Fourier-Laplace transform of the log-price and integrated variance is available semi-explicitly. This is the case for the Volterra Stein-Stein stochastic volatility model, where the characteristic function is known analytically. However, naive evaluation of this formula can produce discontinuities due to the complex square root of a Fredholm determinant, particularly when the determinant crosses the negative real axis, leading to severe numerical instabilities. We analyze this phenomenon by characterizing the determinant’s crossing behavior for the joint Fourier-Laplace transform of integrated variance and log-price. We then derive an expression for the transform to account for such crossings and develop efficient algorithms to detect and handle them. Applied to Fourier-based pricing in the rough Stein-Stein model, our approach significantly improves accuracy while drastically reducing computational cost relative to existing methods. ...

Consumption-portfolio choice with preferences for liquid assets ArXiv ID: 2503.02697 “View on arXiv” Authors: Unknown Abstract This paper investigates an infinite horizon, discounted, consumption-portfolio problem in a market with one bond, one liquid risky asset, and one illiquid risky asset with proportional transaction costs. We consider an agent with liquidity preference, modeled by a Cobb-Douglas utility function that includes the liquid wealth. We analyze the properties of the value function and divide the solvency region into three regions: the buying region, the no-trading region, and the selling region, and prove that all three regions are non-empty. We mathematically characterize and numerically solve the optimal policy and prove its optimality. Our numerical analysis sheds light on the impact of various parameters on the optimal policy, and some intuition and economic insights behind it are also analyzed. We find that liquidity preference encourages agents to retain more liquid wealth and inhibits consumption, and may even result in a negative allocation to the illiquid asset. The liquid risky asset not only affects the location of the three regions but also has an impact on consumption. However, whether this impact on consumption is promoted or inhibited depends on the degree of risk aversion of agents. ...

Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market ArXiv ID: 2503.02518 “View on arXiv” Authors: Unknown Abstract Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 hours. We assess it using two 5-year long test periods from the German and Spanish power markets, covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine. Considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy range from 3% to 15% in terms of the root mean squared error and exceed 1% in terms of profits from a realistic trading strategy involving day-ahead bidding and battery storage. ...

N-player and mean field games among fund managers considering excess logarithmic returns ArXiv ID: 2503.02722 “View on arXiv” Authors: Unknown Abstract This paper studies the competition among multiple fund managers with relative performance over the excess logarithmic return. Fund managers compete with each other and have expected utility or mean-variance criteria for excess logarithmic return. Each fund manager possesses a unique risky asset, and all fund managers can also invest in a public risk-free asset and a public risk asset. We construct both an $n$-player game and a mean field game (MFG) to address the competition problem under these two criteria. We explicitly define and rigorously solve the equilibrium and mean field equilibrium (MFE) for each criteria. In the four models, the excess logarithmic return as the evaluation criterion of the fund leads to the {" allocation fractions"} being constant. The introduction of the public risky asset yields different outcomes, with competition primarily affecting the investment in public assets, particularly evident in the MFG. We demonstrate that the MFE of the MFG represents the limit of the $n$-player game’s equilibrium as the competitive scale $n$ approaches infinity. Finally, the sensitivity analyses of the equilibrium are given. ...

To Hedge or Not to Hedge: Optimal Strategies for Stochastic Trade Flow Management ArXiv ID: 2503.02496 “View on arXiv” Authors: Unknown Abstract This paper addresses the trade-off between internalisation and externalisation in the management of stochastic trade flows. We consider agents who must absorb flows and manage risk by deciding whether to warehouse it or hedge in the market, thereby incurring transaction costs and market impact. Unlike market makers, these agents cannot skew their quotes to attract offsetting flows and deter risk-increasing ones, leading to a fundamentally different problem. Within the Almgren-Chriss framework, we derive almost-closed-form solutions in the case of quadratic execution costs, while more general cases require numerical methods. In particular, we discuss the challenges posed by artificial boundary conditions when using classical grid-based numerical PDE techniques and propose reinforcement learning methods as an alternative. ...

VWAP Execution with Signature-Enhanced Transformers: A Multi-Asset Learning Approach ArXiv ID: 2503.02680 “View on arXiv” Authors: Unknown Abstract In this paper I propose a novel approach to Volume Weighted Average Price (VWAP) execution that addresses two key practical challenges: the need for asset-specific model training and the capture of complex temporal dependencies. Building upon my recent work in dynamic VWAP execution arXiv:2502.18177, I demonstrate that a single neural network trained across multiple assets can achieve performance comparable to or better than traditional asset-specific models. The proposed architecture combines a transformer-based design inspired by arXiv:2406.02486 with path signatures for capturing geometric features of price-volume trajectories, as in arXiv:2406.17890. The empirical analysis, conducted on hourly cryptocurrency trading data from 80 trading pairs, shows that the globally-fitted model with signature features (GFT-Sig) achieves superior performance in both absolute and quadratic VWAP loss metrics compared to asset-specific approaches. Notably, these improvements persist for out-of-sample assets, demonstrating the model’s ability to generalize across different market conditions. The results suggest that combining global parameter sharing with signature-based feature extraction provides a scalable and robust approach to VWAP execution, offering significant practical advantages over traditional asset-specific implementations. ...

A Dynamic Model of Private Asset Allocation ArXiv ID: 2503.01099 “View on arXiv” Authors: Unknown Abstract We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors’ portfolio choices. We use cutting-edge machine learning methods to quantify the optimal investment policies over the life cycle of a fund. Moreover, our model offers regulators a tool for precisely quantifying the trade-offs when setting risk-based capital charges. ...