Benchmark Beating with the Increasing Convex Order ArXiv ID: 2311.01692 “View on arXiv” Authors: Unknown Abstract In this paper we model benchmark beating with the increasing convex order (ICX order). The mean constraint in the mean-variance theory of portfolio selection can be regarded as beating a constant. We then investigate the problem of minimizing the variance of a portfolio with ICX order constraints, based on which we also study the problem of beating-performance-variance efficient portfolios. The optimal and efficient portfolios are all worked out in closed form for complete markets. ...

Maximizing Portfolio Predictability with Machine Learning ArXiv ID: 2311.01985 “View on arXiv” Authors: Unknown Abstract We construct the maximally predictable portfolio (MPP) of stocks using machine learning. Solving for the optimal constrained weights in the multi-asset MPP gives portfolios with a high monthly coefficient of determination, given the sample covariance matrix of predicted return errors from a machine learning model. Various models for the covariance matrix are tested. The MPPs of S&P 500 index constituents with estimated returns from Elastic Net, Random Forest, and Support Vector Regression models can outperform or underperform the index depending on the time period. Portfolios that take advantage of the high predictability of the MPP’s returns and employ a Kelly criterion style strategy consistently outperform the benchmark. ...

Towards a data-driven debt collection strategy based on an advanced machine learning framework ArXiv ID: 2311.06292 “View on arXiv” Authors: Unknown Abstract The European debt purchase market as measured by the total book value of purchased debt approached 25bn euros in 2020 and it was growing at double-digit rates. This is an example of how big the debt collection and debt purchase industry has grown and the important impact it has in the financial sector. However, in order to ensure an adequate return during the debt collection process, a good estimation of the propensity to pay and/or the expected cashflow is crucial. These estimations can be employed, for instance, to create different strategies during the amicable collection to maximize quality standards and revenues. And not only that, but also to prioritize the cases in which a legal process is necessary when debtors are unreachable for an amicable negotiation. This work offers a solution for these estimations. Specifically, a new machine learning modelling pipeline is presented showing how outperforms current strategies employed in the sector. The solution contains a pre-processing pipeline and a model selector based on the best model calibration. Performance is validated with real historical data of the debt industry. ...

Non-linear non-zero-sum Dynkin games with Bermudan strategies ArXiv ID: 2311.01086 “View on arXiv” Authors: Unknown Abstract In this paper, we study a non-zero-sum game with two players, where each of the players plays what we call Bermudan strategies and optimizes a general non-linear assessment functional of the pay-off. By using a recursive construction, we show that the game has a Nash equilibrium point. Keywords: Non-Zero-Sum Game, Bermudan Strategies, Nash Equilibrium, Recursive Construction, Non-Linear Assessment Functional, Derivatives/Contingent Claims ...

On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications ArXiv ID: 2311.00964 “View on arXiv” Authors: Unknown Abstract Rules are widely used in Fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. In practice, a two-stage framework of fraud prevention decision rule set mining is usually employed in large Fintech institutions; Stage 1 generates a potentially large pool of rules and Stage 2 aims to produce a refined rule subset according to some criteria (typically based on precision and recall). This paper focuses on improving the flexibility and efficacy of this two-stage framework, and is concerned with finding high-quality rule subsets in a bi-objective space (such as precision and recall). To this end, we first introduce a novel algorithm called SpectralRules that directly generates a compact pool of rules in Stage 1 with high diversity. We empirically find such diversity improves the quality of the final rule subset. In addition, we introduce an intermediate stage between Stage 1 and 2 that adopts the concept of Pareto optimality and aims to find a set of non-dominated rule subsets, which constitutes a Pareto front. This intermediate stage greatly simplifies the selection criteria and increases the flexibility of Stage 2. For this intermediate stage, we propose a heuristic-based framework called PORS and we identify that the core of PORS is the problem of solution selection on the front (SSF). We provide a systematic categorization of the SSF problem and a thorough empirical evaluation of various SSF methods on both public and proprietary datasets. On two real application scenarios within Alipay, we demonstrate the advantages of our proposed methodology over existing work. ...

Quantum Computational Algorithms for Derivative Pricing and Credit Risk in a Regime Switching Economy ArXiv ID: 2311.00825 “View on arXiv” Authors: Unknown Abstract Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages. The type of models we study are based on a regime switching volatility model driven by a Markov chain with observable states. The basic model features a Geometric Brownian Motion with drift and volatility parameters determined by the finite states of a Markov chain. We study algorithms to estimate credit risk and option pricing on a gate-based quantum computer. These models bring us closer to realistic market settings, and therefore quantum computing closer the realm of practical applications. ...

Characteristics of price related fluctuations in Non-Fungible Token (NFT) market ArXiv ID: 2310.19747 “View on arXiv” Authors: Unknown Abstract A non-fungible token (NFT) market is a new trading invention based on the blockchain technology which parallels the cryptocurrency market. In the present work we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market - even though young and governed by a somewhat different mechanisms of trading - shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators. ...

Robust Estimation of Realized Correlation: New Insight about Intraday Fluctuations in Market Betas ArXiv ID: 2310.19992 “View on arXiv” Authors: Unknown Abstract Time-varying volatility is an inherent feature of most economic time-series, which causes standard correlation estimators to be inconsistent. The quadrant correlation estimator is consistent but very inefficient. We propose a novel subsampled quadrant estimator that improves efficiency while preserving consistency and robustness. This estimator is particularly well-suited for high-frequency financial data and we apply it to a large panel of US stocks. Our empirical analysis sheds new light on intra-day fluctuations in market betas by decomposing them into time-varying correlations and relative volatility changes. Our results show that intraday variation in betas is primarily driven by intraday variation in correlations. ...

Optimal fees in hedge funds with first-loss compensation ArXiv ID: 2310.19023 “View on arXiv” Authors: Unknown Abstract Hedge fund managers with the first-loss scheme charge a management fee, a performance fee and guarantee to cover a certain amount of investors’ potential losses. We study how parties can choose a mutually preferred first-loss scheme in a hedge fund with the manager’s first-loss deposit and investors’ assets segregated. For that, we solve the manager’s non-concave utility maximization problem, calculate Pareto optimal first-loss schemes and maximize a decision criterion on this set. The traditional 2% management and 20% performance fees are found to be not Pareto optimal, neither are common first-loss fee arrangements. The preferred first-loss coverage guarantee is increasing as the investor’s risk-aversion or the interest rate increases. It decreases as the manager’s risk-aversion or the market price of risk increases. The more risk averse the investor or the higher the interest rate, the larger is the preferred performance fee. The preferred fee schemes significantly decrease the fund’s volatility. ...

Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict ArXiv ID: 2310.18903 “View on arXiv” Authors: Unknown Abstract Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures markets: WTI, Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we examine both static and dynamic characteristics using daily and high-frequency data. We identified a clear power-law decay in most VG degree distributions and highlighted the pronounced clustering tendencies within crude oil futures VGs. Our results also confirm an inverse correlation between clustering coefficient and node degree and further reveal that all VGs not only adhere to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we found that WTI and Brent demonstrate aligned behavior, while the SC market, with its unique trading mechanics, deviates. The 5-minute VGs’ assortativity coefficient provides a deeper understanding of these markets’ reactions to the pandemic and geopolitical events. Furthermore, the differential responses during the COVID-19 and Russia-Ukraine conflict underline the unique sensitivities of each market to global disruptions. Overall, this research offers profound insights into the structure, dynamics, and adaptability of these essential commodities markets in the face of worldwide challenges. ...