Quantitative Finance
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Showing new listings for Friday, 22 November 2024
- [1] arXiv:2411.13555 [pdf, html, other]
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Title: Deep Learning in Long-Short Stock Portfolio Allocation: An Empirical StudySubjects: Statistical Finance (q-fin.ST)
This paper provides an empirical study exploring the application of deep learning algorithms -- Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer -- in constructing long-short stock portfolios. Two datasets comprising randomly selected stocks from the S\&P 500 and NASDAQ indices, each spanning a decade of daily data, are utilized. The models predict daily stock returns based on historical features such as past returns, Relative Strength Index (RSI), trading volume, and volatility. Portfolios are dynamically adjusted by longing stocks with positive predicted returns and shorting those with negative predictions, with asset weights optimized using Mean-Variance Optimization. Performance is evaluated over a two-year testing period, focusing on return, Sharpe ratio, and maximum drawdown metrics. The results demonstrate the efficacy of deep learning models in enhancing long-short stock portfolio performance.
- [2] arXiv:2411.13558 [pdf, html, other]
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Title: Finding the nonnegative minimal solutions of Cauchy PDEs in a volatility-stabilized marketSubjects: Computational Finance (q-fin.CP); Probability (math.PR); Mathematical Finance (q-fin.MF)
The relative arbitrage problem in Stochastic Portfolio Theory seeks to generate an investment strategy that almost surely outperforms a benchmark portfolio at the end of a certain time horizon. The highest relative return in relative arbitrage opportunities is related to the smallest nonnegative continuous solution of a Cauchy PDE. However, solving the PDE poses analytical and numerical challenges, due to the high dimensionality and its non-unique solutions. In this paper, we discuss numerical methods to address the relative arbitrage problem and the associated PDE in a volatility-stabilized market using time-changed Bessel bridges. We present a practical algorithm and demonstrate numerical results through an example in volatility-stabilized markets.
- [3] arXiv:2411.13559 [pdf, other]
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Title: Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic TradingSubjects: Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence them. This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. This paper will introduce the development of a novel trading system methodology by proposing a two-layer Composing Ensembles architecture, optimized through grid search, to predict whether the price will rise or fall the next day. This strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark, representing a standard investment strategy.
- [4] arXiv:2411.13562 [pdf, other]
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Title: The Role of AI in Financial Forecasting: ChatGPT's Potential and ChallengesComments: 7 pages, 4 figures, 3 tablesSubjects: Statistical Finance (q-fin.ST); Artificial Intelligence (cs.AI)
The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also highlight the continued improvement in data processing capabilities. Explore how AI is reshaping financial services with precisely tAIlored services that can more precisely meet the diverse needs of individual investors. The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection. Analyze the limitations of current AI technology in financial forecasting and its potential impact on the future financial industry landscape, including changes in the job market, the emergence of new financial institutions, and user interface innovations. Emphasizing the importance of increasing investor understanding and awareness of AI and looking ahead to future trends in AI tools for user experience to drive wider adoption of AI in financial decision making. The huge potential, challenges, and future directions of AI in the financial sector highlight the critical role of AI technology in driving transformation and innovation in the financial sector
- [5] arXiv:2411.13564 [pdf, html, other]
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Title: A Random Forest approach to detect and identify Unlawful Insider TradingSubjects: Statistical Finance (q-fin.ST); Machine Learning (cs.LG); Risk Management (q-fin.RM); Trading and Market Microstructure (q-fin.TR)
According to The Exchange Act, 1934 unlawful insider trading is the abuse of access to privileged corporate information. While a blurred line between "routine" the "opportunistic" insider trading exists, detection of strategies that insiders mold to maneuver fair market prices to their advantage is an uphill battle for hand-engineered approaches. In the context of detailed high-dimensional financial and trade data that are structurally built by multiple covariates, in this study, we explore, implement and provide detailed comparison to the existing study (Deng et al. (2019)) and independently implement automated end-to-end state-of-art methods by integrating principal component analysis to the random forest (PCA-RF) followed by a standalone random forest (RF) with 320 and 3984 randomly selected, semi-manually labeled and normalized transactions from multiple industry. The settings successfully uncover latent structures and detect unlawful insider trading. Among the multiple scenarios, our best-performing model accurately classified 96.43 percent of transactions. Among all transactions the models find 95.47 lawful as lawful and $98.00$ unlawful as unlawful percent. Besides, the model makes very few mistakes in classifying lawful as unlawful by missing only 2.00 percent. In addition to the classification task, model generated Gini Impurity based features ranking, our analysis show ownership and governance related features based on permutation values play important roles. In summary, a simple yet powerful automated end-to-end method relieves labor-intensive activities to redirect resources to enhance rule-making and tracking the uncaptured unlawful insider trading transactions. We emphasize that developed financial and trading features are capable of uncovering fraudulent behaviors.
- [6] arXiv:2411.13565 [pdf, html, other]
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Title: Intergenerational cross-subsidies in UK collective defined contribution (CDC) fundsSubjects: General Finance (q-fin.GN)
We evaluate the performance of and level of intergenerational cross subsidy in single-employer and multi-employer collective defined contribution (CDC) schemes which have been designed to be compatible with UK legislation. The single-employer scheme captures the essential features of the Royal Mail CDC scheme, which is currently the only UK CDC scheme. We find that the schemes can be successful in smoothing pension outcomes while outperforming a DC + annuity scheme, but that this outperformance is not guaranteed in a single-employer scheme. There are significant intergenerational cross-subsidies in the single-employer scheme. These qualitatively mirror the cross-subsidies seen in existing defined benefit schemes, but we find the magnitude of the cross-subsidies is much larger in single employer CDC schemes. The multi-employer scheme is intended to minimize such cross-subsidies, but we find that such subsidies still arise due to the approximate pricing methodology implicit in the scheme design. These cross-subsidies tend to cancel out over time, but in any given year they can be large, implying that it is important to use a rigorous pricing methodology when valuing collective pension investments.
- [7] arXiv:2411.13576 [pdf, other]
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Title: The Role of Central Banks in Advancing Sustainable FinanceSubjects: General Finance (q-fin.GN); General Economics (econ.GN)
This paper examines the pivotal role central banks play in advancing sustainable finance, a crucial component in addressing global environmental and social challenges. As supervisors of financial stability and economic growth, central banks have dominance over the financial system to influence how a country moves towards sustainable economy. The chapter explores how central banks integrate sustainability into their monetary policies, regulatory frameworks, and financial market operations. It highlights the ways in which central banks can promote green finance through sustainable investment principles, climate risk assessments, and green bond markets. Additionally, the chapter examines the collaborative efforts between central banks, governments, and international institutions to align financial systems with sustainability goals. By investigating case studies and best practices, the chapter provides a comprehensive understanding of the strategies central banks employ to foster a resilient and sustainable financial landscape. The findings underscore the imperative for central banks to balance traditional mandates with the emerging necessity to support sustainable development, ultimately contributing to the broader agenda of achieving global sustainability targets.
- [8] arXiv:2411.13579 [pdf, other]
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Title: Optimal portfolio under ratio-type periodic evaluation in stochastic factor models under convex trading constraintsComments: Keywords: Periodic evaluation, relative portfolio performance, incomplete market, stochastic factor model, convex trading constraints, convex duality approach. This manuscript combines two previous preprints arXiv:2311.12517 and arXiv:2401.14672 into one paper with more general and improved resultsSubjects: Mathematical Finance (q-fin.MF); Optimization and Control (math.OC); Portfolio Management (q-fin.PM)
This paper studies a type of periodic utility maximization problems for portfolio management in incomplete stochastic factor models with convex trading constraints. The portfolio performance is periodically evaluated on the relative ratio of two adjacent wealth levels over an infinite horizon, featuring the dynamic adjustments in portfolio decision according to past achievements. Under power utility, we transform the original infinite horizon optimal control problem into an auxiliary terminal wealth optimization problem under a modified utility function. To cope with the convex trading constraints, we further introduce an auxiliary unconstrained optimization problem in a modified market model and develop the martingale duality approach to establish the existence of the dual minimizer such that the optimal unconstrained wealth process can be obtained using the dual representation. With the help of the duality results in the auxiliary problems, the relationship between the constrained and unconstrained models as well as some fixed point arguments, we finally derive and verify the optimal constrained portfolio process in a periodic manner for the original problem over an infinite horizon.
- [9] arXiv:2411.13586 [pdf, html, other]
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Title: Advance Detection Of Bull And Bear Phases In Cryptocurrency MarketsSubjects: Statistical Finance (q-fin.ST); Artificial Intelligence (cs.AI)
Cryptocurrencies are highly volatile financial instruments with more and more new retail investors joining the scene with each passing day. Bitcoin has always proved to determine in which way the rest of the cryptocurrency market is headed towards. As of today Bitcoin has a market dominance of close to 50 percent. Bull and bear phases in cryptocurrencies are determined based on the performance of Bitcoin over the 50 Day and 200 Day Moving Averages. The aim of this paper is to foretell the performance of bitcoin in the near future by employing predictive algorithms. This predicted data will then be used to calculate the 50 Day and 200 Day Moving Averages and subsequently plotted to establish the potential bull and bear phases.
- [10] arXiv:2411.13594 [pdf, html, other]
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Title: High resolution microprice estimates from limit orderbook data using hyperdimensional vector Tsetlin MachinesSubjects: Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
We propose an error-correcting model for the microprice, a high-frequency estimator of future prices given higher order information of imbalances in the orderbook. The model takes into account a current microprice estimate given the spread and best bid to ask imbalance, and adjusts the microprice based on recent dynamics of higher price rank imbalances. We introduce a computationally fast estimator using a recently proposed hyperdimensional vector Tsetlin machine framework and demonstrate empirically that this estimator can provide a robust estimate of future prices in the orderbook.
- [11] arXiv:2411.13599 [pdf, html, other]
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Title: Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold InvestmentSubjects: Statistical Finance (q-fin.ST); Artificial Intelligence (cs.AI)
Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of LLMs. Despite the remarkable success of zero-shot Chain-of-Thoughts (CoT) in solving general reasoning tasks, the potential of these methods still remains paid limited attention in the financial reasoning this http URL address this issue, we explore multiple prompt strategies and incorporated semantic news information to improve LLMs' performance on financial reasoning this http URL the best of our knowledge, we are the first to explore this important issue by applying ChatGPT to the gold this http URL this work, our aim is to investigate the financial reasoning capabilities of LLMs and their capacity to generate logical and persuasive investment opinions. We will use ChatGPT, one of the most powerful LLMs recently, and prompt engineering to achieve this goal. Our research will focus on understanding the ability of LLMs in sophisticated analysis and reasoning within the context of investment decision-making. Our study finds that ChatGPT with CoT prompt can provide more explainable predictions and overcome behavioral biases, which is crucial in finance-related tasks and can achieve higher investment returns.
- [12] arXiv:2411.13603 [pdf, html, other]
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Title: A Full-History Network Dataset for BTC Asset Decentralization ProfilingComments: IEEE BigData 2024Subjects: Statistical Finance (q-fin.ST); Artificial Intelligence (cs.AI)
Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.
In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design several decentralization degrees for quantification. Through extensive experiments, we emphasize the significant role of network properties and our network-based decentralization degree in enhancing Bitcoin analysis. Our findings demonstrate the importance of our comprehensive dataset and analysis in advancing research on Bitcoin's transaction dynamics and decentralization, providing valuable insights into the network's structure and its implications. - [13] arXiv:2411.13615 [pdf, other]
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Title: A Deep Learning Approach to Predict the Fall [of Price] of Cryptocurrency Long Before its Actual FallComments: 22 pages, 3 figuresSubjects: Statistical Finance (q-fin.ST); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin.
- [14] arXiv:2411.13762 [pdf, html, other]
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Title: Assessing Stablecoin Credit RisksSubjects: Risk Management (q-fin.RM); General Finance (q-fin.GN)
This paper delves into the spectrum of credit risks associated with decentralized stablecoin issuance, ranging from overcollateralized lending to business-to-business credit. It examines the mechanisms, risks, and mitigation strategies at each layer, highlighting the potential for scaling decentralized stablecoins while ensuring systemic health.
- [15] arXiv:2411.13783 [pdf, html, other]
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Title: Process and Policy Insights from Intercomparing Electricity System Capacity Expansion ModelsGreg Schivley, Michael Blackhurst, Patricia Hidalgo-Gonzalez, Jesse Jenkins, Oleg Lugovoy, Qian Luo, Michael J. Roberts, Rangrang Zheng, Cameron Wade, Matthias FrippSubjects: General Economics (econ.GN); Optimization and Control (math.OC)
This study undertakes a detailed intercomparison of four open-source electricity system capacity expansion models--Temoa, Switch, GenX, and USENSYS--to examine their suitability for guiding U.S. power sector decarbonization policies. We isolate the effects of model-specific differences on policy outcomes and investment decisions by harmonizing empirical inputs via PowerGenome and systematically defining "scenarios" (policy conditions) and "configurations" (model setup choices). Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, thus distinguishing results that arise from data inputs or configuration versus inherent model structure. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under each current policies and net-zero configuration, with less than 1 percent difference in system costs for most configurations. This agreement across models allows us to examine the impact of configuration choices. For example, configurations that assume unit commitment constraints or economic retirement of generators reveal the difference in investment decisions and system costs that arise from these modeling choices, underscoring the need for clear scenario and configuration definitions in policy guidance. Through this study, we identify critical structural assumptions that influence model outcomes and demonstrate the advantages of a standardized approach when using capacity expansion models. This work offers a valuable benchmark and identifies a few key modeling choices for policymakers, which ultimately will enhance transparency and reliability in modeling efforts to inform the clean energy transition for clean energy planning.
- [16] arXiv:2411.13792 [pdf, html, other]
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Title: Multiscale MarkowitzSubjects: Portfolio Management (q-fin.PM); Chaotic Dynamics (nlin.CD); Mathematical Finance (q-fin.MF)
Traditional Markowitz portfolio optimization constrains daily portfolio variance to a target value, optimising returns, Sharpe or variance within this constraint. However, this approach overlooks the relationship between variance at different time scales, typically described by $\sigma(\Delta t) \propto (\Delta t)^{H}$ where $H$ is the Hurst exponent, most of the time assumed to be \(\frac{1}{2}\). This paper introduces a multifrequency optimization framework that allows investors to specify target portfolio variance across a range of frequencies, characterized by a target Hurst exponent $H_{target}$, or optimize the portfolio at multiple time scales. By incorporating this scaling behavior, we enable a more nuanced and comprehensive risk management strategy that aligns with investor preferences at various time scales. This approach effectively manages portfolio risk across multiple frequencies and adapts to different market conditions, providing a robust tool for dynamic asset allocation. This overcomes some of the traditional limitations of Markowitz, when it comes to dealing with crashes, regime changes, volatility clustering or multifractality in markets. We illustrate this concept with a toy example and discuss the practical implementation for assets with varying scaling behaviors.
- [17] arXiv:2411.13813 [pdf, html, other]
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Title: The Value of Information from Sell-side AnalystsSubjects: General Finance (q-fin.GN)
I examine the value of information from sell-side analysts by analyzing a large corpus of their written reports. Using embeddings from state-of-the-art large language models, I show that textual information in analyst reports explains 10.19% of contemporaneous stock returns out-of-sample, a value that is economically more significant than quantitative forecasts. I then perform a Shapley value decomposition to assess how much each topic within the reports contributes to explaining stock returns. The results show that analysts' income statement analyses account for more than half of the reports' explanatory power. Expressing these findings in economic terms, I estimate that early acquisition of analysts' reports can yield significant profits. Analysts' information value peeks in the first week following earnings announcements, highlighting their vital role in interpreting new financial data.
- [18] arXiv:2411.13937 [pdf, html, other]
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Title: Analytical Formula for Fractional-Order Conditional Moments of Nonlinear Drift CEV Process with Regime Switching: Hybrid Approach with ApplicationsSubjects: Mathematical Finance (q-fin.MF)
This paper introduces an analytical formula for the fractional-order conditional moments of nonlinear drift constant elasticity of variance (NLD-CEV) processes under regime switching, governed by continuous-time finite-state irreducible Markov chains. By employing a hybrid system approach, we derive exact closed-form expressions for these moments across arbitrary fractional orders and regime states, thereby enhancing the analytical tractability of NLD-CEV models under stochastic regimes. Our methodology hinges on formulating and solving a complex system of interconnected partial differential equations derived from the Feynman--Kac formula for switching diffusions. To illustrate the practical relevance of our approach, Monte Carlo simulations for process with Markovian switching are applied to validate the accuracy and computational efficiency of the analytical formulas. Furthermore, we apply our findings for the valuation of financial derivatives within a dynamic nonlinear mean-reverting regime-switching framework, which demonstrates significant improvements over traditional methods. This work offers substantial contributions to financial modeling and derivative pricing by providing a robust tool for practitioners and researchers who are dealing with complex stochastic environments.
- [19] arXiv:2411.13965 [pdf, html, other]
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Title: Does the square-root price impact law belong to the strict universal scalings?: quantitative support by a complete survey of the Tokyo stock exchange marketComments: 28 pages, 16 figuresSubjects: Trading and Market Microstructure (q-fin.TR); Statistical Mechanics (cond-mat.stat-mech); General Economics (econ.GN); Portfolio Management (q-fin.PM); Risk Management (q-fin.RM)
Universal power laws have been scrutinised in physics and beyond, and a long-standing debate exists in econophysics regarding the strict universality of the nonlinear price impact, commonly referred to as the square-root law (SRL). The SRL posits that the average price impact $I$ follows a power law with respect to transaction volume $Q$, such that $I(Q) \propto Q^{\delta}$ with $\delta \approx 1/2$. Some researchers argue that the exponent $\delta$ should be system-specific, without universality. Conversely, others contend that $\delta$ should be exactly $1/2$ for all stocks across all countries, implying universality. However, resolving this debate requires high-precision measurements of $\delta$ with errors of around $0.1$ across hundreds of stocks, which has been extremely challenging due to the scarcity of large microscopic datasets -- those that enable tracking the trading behaviour of all individual accounts. Here we conclusively support the universality hypothesis of the SRL by a complete survey of all trading accounts for all liquid stocks on the Tokyo Stock Exchange (TSE) over eight years. Using this comprehensive microscopic dataset, we show that the exponent $\delta$ is equal to $1/2$ within statistical errors at both the individual stock level and the individual trader level. Additionally, we rejected two prominent models supporting the nonuniversality hypothesis: the Gabaix-Gopikrishnan-Plerou-Stanley and the Farmer-Gerig-Lillo-Waelbroeck models. Our work provides exceptionally high-precision evidence for the universality hypothesis in social science and could prove useful in evaluating the price impact by large investors -- an important topic even among practitioners.
- [20] arXiv:2411.14058 [pdf, html, other]
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Title: Wavelet Analysis of Cryptocurrencies -- Non-Linear Dynamics in High Frequency DomainsComments: 18 pagesSubjects: General Finance (q-fin.GN); General Economics (econ.GN); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)
In this study, we perform some analysis for the probability distributions in the space of frequency and time variables. However, in the domain of high frequencies, it behaves in such a way as the highly non-linear dynamics. The wavelet analysis is a powerful tool to perform such analysis in order to search for the characteristics of frequency variations over time for the prices of major cryptocurrencies. In fact, the wavelet analysis is found to be quite useful as it examine the validity of the efficient market hypothesis in the weak form, especially for the presence of the cyclical persistence at different frequencies. If we could find some cyclical persistence at different frequencies, that means that there exist some intrinsic causal relationship for some given investment horizons defined by some chosen sampling scales. This is one of the characteristic results of the wavelet analysis in the time-frequency domains.
- [21] arXiv:2411.14068 [pdf, html, other]
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Title: Calculating Profits and Losses for Algorithmic Trading Strategies: A Short GuideSubjects: Trading and Market Microstructure (q-fin.TR)
We present a series of equations that track the total realized and unrealized profits and losses at any time, incorporating the spread. The resulting formalism is ideally suited to evaluate the performance of trading model algorithms.
- [22] arXiv:2411.14230 [pdf, other]
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Title: Public sentiments on the fourth industrial revolution: An unsolicited public opinion poll from TwitterComments: 40 pages, 6 tablesSubjects: General Economics (econ.GN); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
This article explores public perceptions on the Fourth Industrial Revolution (4IR) through an analysis of social media discourse across six European countries. Using sentiment analysis and machine learning techniques on a dataset of tweets and media articles, we assess how the public reacts to the integration of technologies such as artificial intelligence, robotics, and blockchain into society. The results highlight a significant polarization of opinions, with a shift from neutral to more definitive stances either embracing or resisting technological impacts. Positive sentiments are often associated with technological enhancements in quality of life and economic opportunities, whereas concerns focus on issues of privacy, data security, and ethical implications. This polarization underscores the need for policymakers to engage proactively with the public to address fears and harness the benefits of 4IR technologies. The findings also advocate for digital literacy and public awareness programs to mitigate misinformation and foster an informed public discourse on future technological integration. This study contributes to the ongoing debate on aligning technological advances with societal values and needs, emphasizing the role of informed public opinion in shaping effective policy.
New submissions (showing 22 of 22 entries)
- [23] arXiv:2411.03502 (replaced) [pdf, html, other]
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Title: Adaptive Shock Compensation in the Multi-layer Network of Global Food Production and TradeSubjects: General Economics (econ.GN)
Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our findings indicate that these lead to spillover effects potentially exacerbating food inequality: an Indian rice shock resulted in a 5.8 % increase in rice losses in countries with a low Human Development Index (HDI) and a 14.2 % decrease in those with a high HDI. Considering multiple interacting shocks leads to super-additive losses of up to 12 % of the total available food volume across the global food production network. This framework allows us to identify combinations of shocks that pose substantial systemic risks and reduce the resilience of the global food supply.
- [24] arXiv:2312.01730 (replaced) [pdf, html, other]
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Title: Set-valued stochastic integrals for convoluted L\'{e}vy processesComments: 28 pagesSubjects: Probability (math.PR); Mathematical Finance (q-fin.MF)
In this paper we study set-valued Volterra-type stochastic integrals driven by Lévy processes. Upon extending the classical definitions of set-valued stochastic integral functionals to convoluted integrals with square-integrable kernels, set-valued convoluted stochastic integrals are defined by taking the closed decomposable hull of the integral functionals for generic time. We show that, aside from well-established results for set-valued Itô integrals, while set-valued stochastic integrals with respect to a finite-variation Poisson random measure are guaranteed to be integrably bounded for bounded integrands, this is not true when the random measure is of infinite variation. For indefinite integrals, we prove that it is a mutual effect of kernel singularity and jumps that the set-valued convoluted integrals are possibly explosive and take extended vector values. These results have some important implications on how set-valued fractional dynamical systems are to be constructed in general. Two classes of set-monotone processes are studied for practical interests in economic and financial modeling.
- [25] arXiv:2411.11853 (replaced) [pdf, html, other]
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Title: Chat Bankman-Fried: an Exploration of LLM Alignment in FinanceSubjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); General Finance (q-fin.GN)
Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental framework to assess whether LLMs adhere to ethical and legal standards in the relatively unexplored context of finance. We prompt nine LLMs to impersonate the CEO of a financial institution and test their willingness to misuse customer assets to repay outstanding corporate debt. Beginning with a baseline configuration, we adjust preferences, incentives and constraints, analyzing the impact of each adjustment with logistic regression. Our findings reveal significant heterogeneity in the baseline propensity for unethical behavior of LLMs. Factors such as risk aversion, profit expectations, and regulatory environment consistently influence misalignment in ways predicted by economic theory, although the magnitude of these effects varies across LLMs. This paper highlights both the benefits and limitations of simulation-based, ex post safety testing. While it can inform financial authorities and institutions aiming to ensure LLM safety, there is a clear trade-off between generality and cost.