On the Hull-White model with volatility smile for Valuation Adjustments ArXiv ID: 2403.14841 “View on arXiv” Authors: Unknown Abstract Affine Diffusion dynamics are frequently used for Valuation Adjustments (xVA) calculations due to their analytic tractability. However, these models cannot capture the market-implied skew and smile, which are relevant when computing xVA metrics. Hence, additional degrees of freedom are required to capture these market features. In this paper, we address this through an SDE with state-dependent coefficients. The SDE is consistent with the convex combination of a finite number of different AD dynamics. We combine Hull-White one-factor models where one model parameter is varied. We use the Randomized AD (RAnD) technique to parameterize the combination of dynamics. We refer to our SDE with state-dependent coefficients and the RAnD parametrization of the original models as the rHW model. The rHW model allows for efficient semi-analytic calibration to European swaptions through the analytic tractability of the Hull-White dynamics. We use a regression-based Monte-Carlo simulation to calculate exposures. In this setting, we demonstrate the significant effect of skew and smile on exposures and xVAs of linear and early-exercise interest rate derivatives. ...

Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research ArXiv ID: 2403.14483 “View on arXiv” Authors: Unknown Abstract Mobile Internet user credit assessment is an important way for communication operators to establish decisions and formulate measures, and it is also a guarantee for operators to obtain expected benefits. However, credit evaluation methods have long been monopolized by financial industries such as banks and credit. As supporters and providers of platform network technology and network resources, communication operators are also builders and maintainers of communication networks. Internet data improves the user’s credit evaluation strategy. This paper uses the massive data provided by communication operators to carry out research on the operator’s user credit evaluation model based on the fusion LightGBM algorithm. First, for the massive data related to user evaluation provided by operators, key features are extracted by data preprocessing and feature engineering methods, and a multi-dimensional feature set with statistical significance is constructed; then, linear regression, decision tree, LightGBM, and other machine learning algorithms build multiple basic models to find the best basic model; finally, integrates Averaging, Voting, Blending, Stacking and other integrated algorithms to refine multiple fusion models, and finally establish the most suitable fusion model for operator user evaluation. ...

Detecting and Triaging Spoofing using Temporal Convolutional Networks ArXiv ID: 2403.13429 “View on arXiv” Authors: Unknown Abstract As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large datasets and the continually changing tricks of the trade make it difficult to adapt to new market conditions and detect bad actors. To that end, we propose a framework that can be adapted easily to various problems in the space of detecting market manipulation. Our approach entails initially employing a labelling algorithm which we use to create a training set to learn a weakly supervised model to identify potentially suspicious sequences of order book states. The main goal here is to learn a representation of the order book that can be used to easily compare future events. Subsequently, we posit the incorporation of expert assessment to scrutinize specific flagged order book states. In the event of an expert’s unavailability, recourse is taken to the application of a more complex algorithm on the identified suspicious order book states. We then conduct a similarity search between any new representation of the order book against the expert labelled representations to rank the results of the weak learner. We show some preliminary results that are promising to explore further in this direction ...

Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing ArXiv ID: 2403.13625 “View on arXiv” Authors: Unknown Abstract Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiring deep domain and technical knowledge. These characteristics often limit the engagement of law enforcement and end-users in these technologies that, despite their potential, remain misunderstood. For this reason, in this study, we describe our experience in combining learning and training methods and the potential benefits of gamification to enhance technology transfer and increase adult learning. In fact, in this case, participants are experienced practitioners in professions/industries that are exposed to terrorism financing (such as Law Enforcement Officers, Financial Investigation Officers, private investigators, etc.) We define training activities on different levels for increasing the exchange of information about new trends and criminal modus operandi among and within law enforcement agencies, intensifying cross-border cooperation and supporting efforts to combat and prevent terrorism funding activities. On the other hand, a game (hackathon) is designed to address realistic challenges related to the dark net, crypto assets, new payment systems and dark web marketplaces that could be used for terrorist activities. The entire methodology was evaluated using quizzes, contest results, and engagement metrics. In particular, training events show about 60% of participants complete the 11-week training course, while the Hackathon results, gathered in two pilot studies (Madrid and The Hague), show increasing expertise among the participants (progression in the achieved points on average). At the same time, more than 70% of participants positively evaluate the use of the gamification approach, and more than 85% of them consider the implemented Use Cases suitable for their investigations. ...

Six Levels of Privacy: A Framework for Financial Synthetic Data ArXiv ID: 2403.14724 “View on arXiv” Authors: Unknown Abstract Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well. Such data may arise from client information, business information, or other proprietary sources that must be protected. Even though the process by which Synthetic Data is generated serves to obscure the original data to some degree, the extent to which privacy is preserved is hard to assess. Accordingly, we introduce a hierarchy of levels'' of privacy that are useful for categorizing Synthetic Data generation methods and the progressively improved protections they offer. While the six levels were devised in the context of financial applications, they may also be appropriate for other industries as well. Our paper includes: A brief overview of Financial Synthetic Data, how it can be used, how its value can be assessed, privacy risks, and privacy attacks. We close with details of the Six Levels’’ that include defenses against those attacks. ...

Modeling stock price dynamics on the Ghana Stock Exchange: A Geometric Brownian Motion approach ArXiv ID: 2403.13192 “View on arXiv” Authors: Unknown Abstract Modeling financial data often relies on assumptions that may prove insufficient or unrealistic in practice. The Geometric Brownian Motion (GBM) model is frequently employed to represent stock price processes. This study investigates whether the behavior of weekly and monthly returns of selected equities listed on the Ghana Stock Exchange conforms to the GBM model. Parameters of the GBM model were estimated for five equities, and forecasts were generated for three months. Evaluation of estimation accuracy was conducted using mean square error (MSE). Results indicate that the expected prices from the modeled equities closely align with actual stock prices observed on the Exchange. Furthermore, while some deviations were observed, the actual prices consistently fell within the estimated confidence intervals. ...

A path-dependent PDE solver based on signature kernels ArXiv ID: 2403.11738 “View on arXiv” Authors: Unknown Abstract We develop a provably convergent kernel-based solver for path-dependent PDEs (PPDEs). Our numerical scheme leverages signature kernels, a recently introduced class of kernels on path-space. Specifically, we solve an optimal recovery problem by approximating the solution of a PPDE with an element of minimal norm in the signature reproducing kernel Hilbert space (RKHS) constrained to satisfy the PPDE at a finite collection of collocation paths. In the linear case, we show that the optimisation has a unique closed-form solution expressed in terms of signature kernel evaluations at the collocation paths. We prove consistency of the proposed scheme, guaranteeing convergence to the PPDE solution as the number of collocation points increases. Finally, several numerical examples are presented, in particular in the context of option pricing under rough volatility. Our numerical scheme constitutes a valid alternative to the ubiquitous Monte Carlo methods. ...

Advanced Statistical Arbitrage with Reinforcement Learning ArXiv ID: 2403.12180 “View on arXiv” Authors: Unknown Abstract Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spread of paired stocks. Studies on this strategy often rely heavily on model assumption. In this study, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading. ...

Asset management with an ESG mandate ArXiv ID: 2403.11622 “View on arXiv” Authors: Unknown Abstract We investigate the portfolio frontier and risk premia in equilibrium when institutional investors aim to minimize the tracking error variance under an ESG score mandate. If a negative ESG premium is priced in the market, this mandate can reduce portfolio inefficiency when the return over-performance target is limited. In equilibrium, with asset managers endowed with an ESG mandate and mean-variance investors, a negative ESG premium arises. A result that is supported by empirical data. The negative ESG premium is due to the ESG constraint imposed on institutional investors and is not associated with a risk factor. ...

FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications ArXiv ID: 2403.12285 “View on arXiv” Authors: Unknown Abstract There are multiple sources of financial news online which influence market movements and trader’s decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading techniques, to arrive at better informed trading decisions. Standard lexicon based sentiment approaches have demonstrated their power in aiding financial decisions. However, they are known to suffer from issues related to context sensitivity and word ordering. Large Language Models (LLMs) can also be used in this context, but they are not finance-specific and tend to require significant computational resources. To facilitate a finance specific LLM framework, we introduce a novel approach based on the Llama 2 7B foundational model, in order to benefit from its generative nature and comprehensive language manipulation. This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data, so as to jointly handle the complexities of financial lexicon and context, and further equipping it with a neural network based decision mechanism. Such a generator-classifier scheme, referred to as FinLlama, is trained not only to classify the sentiment valence but also quantify its strength, thus offering traders a nuanced insight into financial news articles. Complementing this, the implementation of parameter-efficient fine-tuning through LoRA optimises trainable parameters, thus minimising computational and memory requirements, without sacrificing accuracy. Simulation results demonstrate the ability of the proposed FinLlama to provide a framework for enhanced portfolio management decisions and increased market returns. These results underpin the ability of FinLlama to construct high-return portfolios which exhibit enhanced resilience, even during volatile periods and unpredictable market events. ...