Laboratory Seminars

The seminar reviewed a comparative study of order flow toxicity on two structurally different platforms: the Binance cryptocurrency exchange and the MOEX regulated stock exchange. High-frequency data was used, and classical toxicity metrics and their first differences were analysed to identify dynamic asymmetries. The prediction of price slippage relative to VWAP using the XGBoost machine learning model was discussed as a proxy indicator of toxicity.

The seminar examined the dynamics of the toxic flow of applications in financial markets and the role of high-frequency trading (HFT) in the formation of adverse selection. Special attention is paid to how fast automated strategies affect the ratio of informed and uninformed market participants, as well as the stability of intraday liquidity.

This project seminar is devoted to the study of methods for personalisation of diffusion models with a limited number of images. The problem of retraining for particular details is considered and the approach of temporary redistribution of diffusion steps—time-step reweighting—is presented. The method uses an uneven distribution of timesteps, focusing on the areas with the most informative features, and adjusts the training weights to maintain unbiasedness. This principle makes it possible to enhance the assimilation of the key characteristics of an object and increase the stability of personalisation without changing the architecture of the model or increasing computational costs.

The seminar examined how multi-agent systems are used to study complex processes in economics and finance. It was shown why historical data alone is not enough to test many hypotheses, and how agent-based simulators can reproduce market dynamics and a system's response to external influences.

The key elements of such models are analysed in detail—the concepts of agent, shock, and the limitations that arise during their construction. Special emphasis is placed on examples of practical application: modelling various behavioural strategies in trading systems, research on automated market makers (AMM), and alternative market mechanisms. The role of liquidity pools and their difference from traditional order book systems is considered separately.

The project seminar is devoted to an in-depth analysis of modern methods of managing neural network generative models. We have reviewed key precision control mechanisms such as IP Adapter, LoRA, and ControlNet, which allow for customisation of the content, style, and pose of the generated image. Special attention is paid to the transition from static images to video through the Animate Anyone and UniAnimate architectures. Computational optimisation methods that are critical for practical application are also touched upon. All this is considered in the context of the applied task of virtualising clothing fitting in e-commerce, where accuracy and realism are key factors.

During the seminar, methods for evaluating hallucinations in the generation of diffusion models were considered. The main focus was on the deterministic method, the essence of which is the mapping of images into a one–dimensional space and clustering in the plane of Complexity-Entropy characteristics for the time series obtained from the images, which makes it possible to assess the degree of confidence in the belonging of the generation to the class of hallucinations.

The study, which was reviewed during the project seminar, analyses bifurcation points in financial models with coloured noise, as well as examines market dynamics involving traders using various strategies. The main focus is on the Vasicek stochastic model, which numerically studies the effect of coloured noise on change-points using machine learning (LSTM) methods. In parallel, a simulation of a single-security market is being considered, where traders adapt their strategies depending on market conditions.

The research results combine the theoretical analysis of stochastic systems with practical modelling of market dynamics, which can be useful for developing sustainable trading algorithms and risk management.

Using an agent-based simulation model, we study how the choice of a liquidity strategy depends on the learning algorithm used by traders. In previous studies on this topic, for dealer-type markets, the result was obtained on the irrelevance of the algorithm used. We find the only non-cooperative Nash equilibrium in our model and show that for two of the three algorithms tested, traders converge to significantly underestimate the liquidity provided compared to the non-cooperative equilibrium strategy.

Fictitious trading (wash trading) remains a serious problem on decentralised exchanges, especially in highly speculative segments such as memecoins, where artificial trading activity distorts liquidity and pricing indicators. Unlike traditional financial markets and centralised exchanges with limit orders, automated market makers (AMM) are based on special mechanisms that require specialised methods for detecting manipulation.

This study suggests a comprehensive approach to detecting fictitious trading on Uniswap v2 using graph analysis, which allows for the adaptation of existing detection methods to the AMM environment. We have collected a unique dataset with historical memecoin trading market data and developed several methods to identify fictitious trades: (1) a heuristic method based on approaches from traditional finance, and (2) two graph methods that model trader interaction in the form of directed graphs to identify fictitious trading cycles. Key graph features such as transaction cycling and address clustering are used to improve detection accuracy.

Evaluation of the proposed methods shows that graph approaches are superior to classical techniques in detecting fictitious trading on AMM exchanges. Our analysis allows us to quantify the volume of fictitious trading in pools and demonstrate its impact on liquidity and price formation. This research contributes to the development of on-chain financial manipulation analytics by offering a scalable, data-driven methodology adapted to manipulation in the AMM environment. The results of the work are valuable for regulators, developers, and users of decentralised exchanges.

The workshop examined white, red, and green noise in financial time series and their impact on the detection of transition points. Stochastic models, including the Ornstein–Uhlenbeck process, and numerical analysis methods such as the Krylov–Bogolyubov averaging were also discussed. Special attention was paid to green noise and its role in irreversible transitions between system states.

In addition, we reviewed transition point detection algorithms (PELT) and machine learning (LSTM, CatBoost) for classifying time series states, and emphasised the importance of choosing a noise model to improve the effectiveness of forecasting and risk management.

The article 'Market Efficiency, Informational Asymmetry and Pseudo-Collusion of Adaptively Learning Agents' was presented and discussed at the seminar. Abstract: We examine the dynamics of informational efficiency in a market with asymmetrically informed, boundedly rational traders who adaptively learn optimal strategies using simple multiarmed bandit (MAB) algorithms. The strategies available to the traders have two dimensions: on the one hand, the traders must endogenously choose whether to acquire a costly information signal, on the other, they must determine how aggressively they trade by choosing the share of their wealth to be invested in the risky asset. Our study contributes to two strands of literature: the literature comparing the effects of competitive and strategic behaviour on asset price efficiency under costly information as well as the actively growing literature on algorithmic pseudo-collusion in financial markets. We find that for certain market environments our results contradict the predictions of Kyle [1989] in that a market with strategically acting traders can be more efficient than a purely competitive one. Furthermore, we obtain novel results on the ability of independently learning traders to coordinate on a pseudo-collusive behaviour, leading to non-competitive pricing. Contrary to some recent contributions (see eg [Cartea et al. 2022]), we find that the pseudo-collusive behaviour in our model is robust to a large number of agents, demonstrating that even in the setting of financial markets with a large number of independently learning traders, non-competitive pricing and pseudo-collusive behaviour can frequently arise. 

Financial markets are often subject to various forms of manipulation, including pump-and-dump schemes, which lead to significant price volatility and financial losses for investors. Previous research on cryptocurrency markets has used machine learning methods to identify manipulation targets early. However, these methods had serious limitations due to imbalances in the historical data. To solve these problems, we propose a new framework for detecting manipulation targets, including two-step data normalisation, specific algorithm optimisation, and new microstructured features. The workshop reviewed several approaches based on gradient boosting, including hyperparameter optimisation based on class imbalance and ranking algorithms. The results of the proposed algorithms surpass existing methods, achieving an accuracy of 5% in the top 1 and 45% accuracy in the top 10 in a test sample with a strong class imbalance (class imbalance is approximately 270).

In the classic work, Grossman and Stiglitz [1980]* show that financial asset prices inevitably contain a certain degree of inefficiency at non-zero information costs. However, this result was obtained in the paradigm of rational expectations, which place unrealistic demands on the rationality of agents, and postulates only the impossibility of effective equilibrium, without affecting the dynamics of market efficiency. Simulation multi-agent models with an element of market selection allow us to study the dynamics of market efficiency in conditions when agents are limited rational and adaptive. We model investor behavior using the so-called multi-armed bandit algorithm and discover a U-shaped ratio of market efficiency to information costs.

* Grossman, Sanford J., and Joseph E. Stiglitz. "On the impossibility of informationally efficient markets." The American economic review 70, no. 3 (1980): 393-408.

A research paper was presented at the seminar, the purpose of which was to analyse the break points in stochastic interest rate models under the influence of coloured noise. The effect of coloured noise on the number of breakpoints and their frequency, as well as the possibility of detecting them using a neural network approach, was investigated. The object of the study was the Vasicek stochastic model, which is used to model interest rates. The research methodology included approximation of numerical solutions of the model by the Euler-Maruyama method, calibration of model parameters, as well as adaptation of the integration step. Methods for detecting breakpoints and their application to the generated data were considered separately. As a result of the study, the results of deep learning models for detecting breakpoints in data generated under the influence of noise are presented.