Liquidity Jump, Liquidity Diffusion, and Treatment on Wash Trading of Crypto Assets ArXiv ID: 2404.07222 “View on arXiv” Authors: Unknown Abstract We propose that the liquidity of an asset includes two components: liquidity jump and liquidity diffusion. We show that liquidity diffusion has a higher correlation with crypto wash trading than liquidity jump and demonstrate that treatment on wash trading significantly reduces the level of liquidity diffusion, but only marginally reduces that of liquidity jump. We confirm that the autoregressive models are highly effective in modeling the liquidity-adjusted return with and without the treatment on wash trading. We argue that treatment on wash trading is unnecessary in modeling established crypto assets that trade in unregulated but mainstream exchanges. ...

Rank-Dependent Predictable Forward Performance Processes ArXiv ID: 2403.16228 “View on arXiv” Authors: Unknown Abstract Predictable forward performance processes (PFPPs) are stochastic optimal control frameworks for an agent who controls a randomly evolving system but can only prescribe the system dynamics for a short period ahead. This is a common scenario in which a controlling agent frequently re-calibrates her model. We introduce a new class of PFPPs based on rank-dependent utility, generalizing existing models that are based on expected utility theory (EUT). We establish existence of rank-dependent PFPPs under a conditionally complete market and exogenous probability distortion functions which are updated periodically. We show that their construction reduces to solving an integral equation that generalizes the integral equation obtained under EUT in previous studies. We then propose a new approach for solving the integral equation via theory of Volterra equations. We illustrate our result in the special case of conditionally complete Black-Scholes model. ...

Workplace sustainability or financial resilience? Composite-financial resilience index ArXiv ID: 2403.16296 “View on arXiv” Authors: Unknown Abstract Due to the variety of corporate risks in turmoil markets and the consequent financial distress especially in COVID-19 time, this paper investigates corporate resilience and compares different types of resilience that can be potential sources of heterogeneity in firms’ implied rate of return. Specifically, the novelty is not only to quantify firms’ financial resilience but also to compare it with workplace resilience which matters more in the COVID-19 era. The study prepares several pieces of evidence of the necessity and insufficiency of these two main types of resilience by comparing earnings expectations and implied discount rates of high- and low-resilience firms. Particularly, results present evidence of the possible amplification of workplace resilience by the financial status of firms in the COVID-19 era. The paper proposes a novel composite-financial resilience index as a potential measure for disaster risk that significantly and persistently reveals low-resilience characteristics of firms and resilience-heterogeneity in implied discount rates. ...

Anticipatory Gains and Event-Driven Losses in Blockchain-Based Fan Tokens: Evidence from the FIFA World Cup ArXiv ID: 2403.15810 “View on arXiv” Authors: Unknown Abstract National football teams increasingly issue tradeable blockchain-based fan tokens to strategically enhance fan engagement. This study investigates the impact of 2022 World Cup matches on the dynamic performance of each team’s fan token. The event study uncovers fan token returns surged six months before the World Cup, driven by positive anticipation effects. However, intraday analysis reveals a reversal of fan token returns consistently declining and trading volumes rising as matches unfold. To explain findings, we uncover asymmetries whereby defeats in high-stake matches caused a plunge in fan token returns, compared to low-stake matches, intensifying in magnitude for knockout matches. Contrarily, victories enhance trading volumes, reflecting increased market activity without a corresponding positive effect on returns. We align findings with the classic market adage “buy the rumor, sell the news,” unveiling cognitive biases and nuances in investor sentiment, cautioning the dichotomy of pre-event optimism and subsequent performance declines. ...

Investigating Similarities Across Decentralized Financial (DeFi) Services ArXiv ID: 2404.00034 “View on arXiv” Authors: Unknown Abstract We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols. ...

Construction of a Japanese Financial Benchmark for Large Language Models ArXiv ID: 2403.15062 “View on arXiv” Authors: Unknown Abstract With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity. There is also a growing need for benchmarks to evaluate the performance of current LLMs in each domain. Therefore, in this study, we constructed a benchmark comprising multiple tasks specific to the Japanese and financial domains and performed benchmark measurements on some models. Consequently, we confirmed that GPT-4 is currently outstanding, and that the constructed benchmarks function effectively. According to our analysis, our benchmark can differentiate benchmark scores among models in all performance ranges by combining tasks with different difficulties. ...

Nonlinear shifts and dislocations in financial market structure and composition ArXiv ID: 2403.15163 “View on arXiv” Authors: Unknown Abstract This paper develops new mathematical techniques to identify temporal shifts among a collection of US equities partitioned into a new and more detailed set of market sectors. Although conceptually related, our three analyses reveal distinct insights about financial markets, with meaningful implications for investment managers. First, we explore a variety of methods to identify nonlinear shifts in market sector structure and describe the mathematical connection between the measure used and the captured phenomena. Second, we study network structure with respect to our new market sectors and identify meaningfully connected sector-to-sector mappings. Finally, we conduct a series of sampling experiments over different sample spaces and contrast the distribution of Sharpe ratios produced by long-only, long-short and short-only investment portfolios. In addition, we examine the sector composition of the top-performing portfolios for each of these portfolio styles. In practice, the methods proposed in this paper could be used to identify regime shifts, optimally structured portfolios, and better communities of equities. ...

Robust Utility Optimization via a GAN Approach ArXiv ID: 2403.15243 “View on arXiv” Authors: Unknown Abstract Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve robust utility optimization problems in general and realistic settings. In particular, we model both the investor and the market by neural networks (NN) and train them in a mini-max zero-sum game. This approach is applicable for any continuous utility function and in realistic market settings with trading costs, where only observable information of the market can be used. A large empirical study shows the versatile usability of our method. Whenever an optimal reference strategy is available, our method performs on par with it and in the (many) settings without known optimal strategy, our method outperforms all other reference strategies. Moreover, we can conclude from our study that the trained path-dependent strategies do not outperform Markovian ones. Lastly, we uncover that our generative approach for learning optimal, (non-) robust investments under trading costs generates universally applicable alternatives to well known asymptotic strategies of idealized settings. ...

Anti-correlation network among China A-shares ArXiv ID: 2404.00028 “View on arXiv” Authors: Unknown Abstract The correlation-based financial networks are studied intensively. However, previous studies ignored the importance of the anti-correlation. This paper is the first to consider the anti-correlation and positive correlation separately, and accordingly construct the weighted temporal anti-correlation and positive correlation networks among stocks listed in the Shanghai and Shenzhen stock exchanges. For both types of networks during the first 24 years of this century, fundamental topological measurements are analyzed systematically. This paper unveils some essential differences in these topological measurements between the anti-correlation and positive correlation networks. It also observes an asymmetry effect between the stock market decline and rise. The methodology proposed in this paper has the potential to reveal significant differences in the topological structure and dynamics of a complex financial system, stock behavior, investment portfolios, and risk management, offering insights that are not visible when all correlations are considered together. More importantly, this paper proposes a new direction for studying complex systems: the anti-correlation network. It is well worth reexamining previous relevant studies using this new methodology. ...

DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models ArXiv ID: 2403.14063 “View on arXiv” Authors: Unknown Abstract In this work, we propose an approach to generalize denoising diffusion probabilistic models for stock market predictions and portfolio management. Present works have demonstrated the efficacy of modeling interstock relations for market time-series forecasting and utilized Graph-based learning models for value prediction and portfolio management. Though convincing, these deterministic approaches still fall short of handling uncertainties i.e., due to the low signal-to-noise ratio of the financial data, it is quite challenging to learn effective deterministic models. Since the probabilistic methods have shown to effectively emulate higher uncertainties for time-series predictions. To this end, we showcase effective utilisation of Denoising Diffusion Probabilistic Models (DDPM), to develop an architecture for providing better market predictions conditioned on the historical financial indicators and inter-stock relations. Additionally, we also provide a novel deterministic architecture MaTCHS which uses Masked Relational Transformer(MRT) to exploit inter-stock relations along with historical stock features. We demonstrate that our model achieves SOTA performance for movement predication and Portfolio management. ...