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on Computational Economics |
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Issue of 2024‒01‒08
34 papers chosen by |
| By: | Matteo Gambara; Giulia Livieri; Andrea Pallavicini |
| Abstract: | In the present work, we introduce and compare state-of-the-art algorithms, that are now classified under the name of machine learning, to price Asian and look-back products with early-termination features. These include randomized feed-forward neural networks, randomized recurrent neural networks, and a novel method based on signatures of the underlying price process. Additionally, we explore potential applications on callable certificates. Furthermore, we present an innovative approach for calculating sensitivities, specifically Delta and Gamma, leveraging Chebyshev interpolation techniques. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.16762&r=cmp |
| By: | Philippe Goulet Coulombe; Mikael Frenette; Karin Klieber |
| Abstract: | We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network which accommodates for various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that breaks mean/variance indeterminacy in this class of overparametrized nonlinear models. Third, we conduct a blocked out-of-bag reality check to curb overfitting in both conditional moments. Fourth, the algorithm utilizes standard deep learning software and thus handles large data sets - both computationally and statistically. Ergo, our Hemisphere Neural Network (HNN) provides proactive volatility forecasts based on leading indicators when it can, and reactive volatility based on the magnitude of previous prediction errors when it must. We evaluate point and density forecasts with an extensive out-of-sample experiment and benchmark against a suite of models ranging from classics to more modern machine learning-based offerings. In all cases, HNN fares well by consistently providing accurate mean/variance forecasts for all targets and horizons. Studying the resulting volatility paths reveals its versatility, while probabilistic forecasting evaluation metrics showcase its enviable reliability. Finally, we also demonstrate how this machinery can be merged with other structured deep learning models by revisiting Goulet Coulombe (2022)'s Neural Phillips Curve. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.16333&r=cmp |
| By: | Vincent Gurgul; Stefan Lessmann; Wolfgang Karl H\"ardle |
| Abstract: | This paper explores the application of Machine Learning (ML) and Natural Language Processing (NLP) techniques in cryptocurrency price forecasting, specifically Bitcoin (BTC) and Ethereum (ETH). Focusing on news and social media data, primarily from Twitter and Reddit, we analyse the influence of public sentiment on cryptocurrency valuations using advanced deep learning NLP methods. Alongside conventional price regression, we treat cryptocurrency price forecasting as a classification problem. This includes both the prediction of price movements (up or down) and the identification of local extrema. We compare the performance of various ML models, both with and without NLP data integration. Our findings reveal that incorporating NLP data significantly enhances the forecasting performance of our models. We discover that pre-trained models, such as Twitter-RoBERTa and BART MNLI, are highly effective in capturing market sentiment, and that fine-tuning Large Language Models (LLMs) also yields substantial forecasting improvements. Notably, the BART MNLI zero-shot classification model shows considerable proficiency in extracting bullish and bearish signals from textual data. All of our models consistently generate profit across different validation scenarios, with no observed decline in profits or reduction in the impact of NLP data over time. The study highlights the potential of text analysis in improving financial forecasts and demonstrates the effectiveness of various NLP techniques in capturing nuanced market sentiment. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14759&r=cmp |
| By: | Robert Stok; Paul Bilokon |
| Abstract: | Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06256&r=cmp |
| By: | Ruslan Tepelyan; Achintya Gopal |
| Abstract: | The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. The core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this work, we explore the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities. The main problem we work to address is modeling the joint distribution of all the members of the S&P 500, or, in other words, learning a 500-dimensional joint distribution. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14735&r=cmp |
| By: | Colin M. Van Oort; Ethan Ratliff-Crain; Brian F. Tivnan; Safwan Wshah |
| Abstract: | We present our Agent-Based Market Microstructure Simulation (ABMMS), an Agent-Based Financial Market (ABFM) that captures much of the complexity present in the US National Market System for equities (NMS). Agent-Based models are a natural choice for understanding financial markets. Financial markets feature a constrained action space that should simplify model creation, produce a wealth of data that should aid model validation, and a successful ABFM could strongly impact system design and policy development processes. Despite these advantages, ABFMs have largely remained an academic novelty. We hypothesize that two factors limit the usefulness of ABFMs. First, many ABFMs fail to capture relevant microstructure mechanisms, leading to differences in the mechanics of trading. Second, the simple agents that commonly populate ABFMs do not display the breadth of behaviors observed in human traders or the trading systems that they create. We investigate these issues through the development of ABMMS, which features a fragmented market structure, communication infrastructure with propagation delays, realistic auction mechanisms, and more. As a baseline, we populate ABMMS with simple trading agents and investigate properties of the generated data. We then compare the baseline with experimental conditions that explore the impacts of market topology or meta-reinforcement learning agents. The combination of detailed market mechanisms and adaptive agents leads to models whose generated data more accurately reproduce stylized facts observed in actual markets. These improvements increase the utility of ABFMs as tools to inform design and policy decisions. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15974&r=cmp |
| By: | Salas-Rojo, Pedro; Rodríguez, Juan Gabriel |
| Abstract: | This paper explores the relationship between received inheritances and the distribution of wealth (financial, non-financial and total) in four developed countries: the United States, Canada, Italy and Spain. We follow the inequality of opportunity (IOp) literature and − considering inheritances as the only circumstance− we show that traditional IOp approaches can lead to non-robust and arbitrary measures of IOp depending on discretionary cut-off choices of a continuous circumstance such as inheritances. To overcome this limitation, we apply Machine Learning methods (‘random forest’ algorithm) to optimize the choice of cut-offs and we find that IOp explains over 60% of wealth inequality in the US and Spain (using the Gini coefficient), and more than 40% in Italy and Canada. Including parental education as an additional circumstance −available for the US and Italy− we find that inheritances are still the main contributor. Finally, using the S-Gini index with different parameters to weight different parts of the distribution, we find that the effect of inheritances is more prominent at the middle of the wealth distribution, while parental education is more important for the asset-poor. |
| Keywords: | C60; D31; D63; G51; inequality of opportunity; inheritances; machine learning; parental education; wealth inequality |
| JEL: | J1 |
| Date: | 2022–03–10 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120916&r=cmp |
| By: | kilani, bochra hadj |
| Abstract: | In years there has been an increase, in the interest surrounding the utilization of unsupervised machine learning methods, particularly the application of K means clustering algorithms within urban studies. These techniques have demonstrated their usefulness, in examining and comprehending facets of planning including land usage patterns, transportation systems and population distribution. The objective of this article is to offer an overview of how K means clustering algorithm are employed in urban studies. The review examines the different methodologies and approaches employed in utilizing K-means clustering for urban analysis, highlighting its advantages and limitations. Additionally, the article discusses the specific challenges and considerations that arise when applying K-means clustering in urban studies, including data preprocessing, feature selection, and interpretation of the cluster results. The findings of this review demonstrate the wide range of applications of K-means clustering in urban studies, from identifying distinct land use categories to understanding the spatial distribution of social amenities. Furthermore, it is revealed that the use of K-means clustering in urban studies allows for the identification and characterization of hidden patterns and similarities among urban areas that might not be immediately apparent through traditional analysis methods. Overall, the use of K-means clustering algorithms provides a valuable tool for urban planners and researchers in gaining insights and making informed decisions in urban design. |
| Date: | 2023–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:bs6wy&r=cmp |
| By: | Keegan Harris; Nicole Immorlica; Brendan Lucier; Aleksandrs Slivkins |
| Abstract: | How can an informed sender persuade a receiver, having only limited information about the receiver's beliefs? Motivated by research showing generative AI can simulate economic agents, we initiate the study of information design with an oracle. We assume the sender can learn more about the receiver by querying this oracle, e.g., by simulating the receiver's behavior. Aside from AI motivations such as general-purpose Large Language Models (LLMs) and problem-specific machine learning models, alternate motivations include customer surveys and querying a small pool of live users. Specifically, we study Bayesian Persuasion where the sender has a second-order prior over the receiver's beliefs. After a fixed number of queries to an oracle to refine this prior, the sender commits to an information structure. Upon receiving the message, the receiver takes a payoff-relevant action maximizing her expected utility given her posterior beliefs. We design polynomial-time querying algorithms that optimize the sender's expected utility in this Bayesian Persuasion game. As a technical contribution, we show that queries form partitions of the space of receiver beliefs that can be used to quantify the sender's knowledge. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.18138&r=cmp |
| By: | Yangyang Yu; Haohang Li; Zhi Chen; Yuechen Jiang; Yang Li; Denghui Zhang; Rong Liu; Jordan W. Suchow; Khaldoun Khashanah |
| Abstract: | Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce \textsc{FinMe}, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, to outline the agent's characteristics; Memory, with layered processing, to aid the agent in assimilating realistic hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{FinMe}'s memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare \textsc{FinMe} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks and funds. We then fine-tuned the agent's perceptual spans to achieve a significant trading performance. Collectively, \textsc{FinMe} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.13743&r=cmp |
| By: | Boyang Yu |
| Abstract: | The impact of non-deterministic outputs from Large Language Models (LLMs) is not well examined for financial text understanding tasks. Through a compelling case study on investing in the US equity market via news sentiment analysis, we uncover substantial variability in sentence-level sentiment classification results, underscoring the innate volatility of LLM outputs. These uncertainties cascade downstream, leading to more significant variations in portfolio construction and return. While tweaking the temperature parameter in the language model decoder presents a potential remedy, it comes at the expense of stifled creativity. Similarly, while ensembling multiple outputs mitigates the effect of volatile outputs, it demands a notable computational investment. This work furnishes practitioners with invaluable insights for adeptly navigating uncertainty in the integration of LLMs into financial decision-making, particularly in scenarios dictated by non-deterministic information. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15180&r=cmp |
| By: | Shalini Sharma; Angshul Majumdar; Emilie Chouzenoux; Victor Elvira |
| Abstract: | Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological side, a new dynamical modeling approach is proposed. Our approach keeps the probabilistic formulation of the state-space model, which provides uncertainty quantification on the estimates, and the function approximation ability of deep neural networks. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14731&r=cmp |
| By: | Cantone, Giulio Giacomo |
| Abstract: | The article “The psychometric house-of-mirrors: the effect of measurement distortions on agent-based models’ predictions” claims that in general ordinal scales are biased measures of latent constructs and parametrisation of Agent-Based Models (ABMs) after parametric estimates fit on measurements on ordinal scales do not generally converge into coherent inferences. I argue that the assumptions of their results do not generalise and I use to argument to claim that the parametric fit of ordinal scales can be relaxed under less restrictive methodological assumptions, conversely that the sample of respondents is unbiased and that items are semantically well-designed. My logic is the following: i. differently by the authors, I notice that ordinal scales differ substantially by rankings in their probabilistic structure; ii. it follows that, without assuming strong ontological axioms, Uniformity is not ideal for ordinal scales, hence the simulation of the authors holds only a partial validity; iii. I conclude that well-designed surveys are relatively immune from the kind of distortions modeled by authors because with a good survey design idiosyncratic distortions annihilate into regular noise. In the Conclusions I suggest adopting an approach for the parametrisation of Agent-Based Models based on mixture models. |
| Date: | 2023–12–08 |
| URL: | http://d.repec.org/n?u=RePEc:osf:metaar:5kzt4&r=cmp |
| By: | Konstantins Benkovskis (Latvijas Banka); Dzintars Jaunzems (Latvijas Banka); Olegs Matvejevs (Latvijas Banka) |
| Abstract: | We propose a novel method for modelling energy substitution in CGE models using energy processes defined according to the purposes of energy use. The purpose-based approach is superior for modelling the green transition because it closely mimics firms’ decisions regarding switching energy sources and is more parsimonious, relying on fewer industry-specific elasticities in the production structure. Latvia’s Computable General Equilibrium (CGE) model is an integral part of the joint CGE-EUROMOD modelling system used for policy simulations at Latvijas Banka. We improve this model by 1) incorporating endogenous substitution of energy resources by enterprises through the proposed purpose-based approach, 2) including the accounting of greenhouse gas (GHG) emissions generated by all public and private sector entities, and 3) introducing explicit modelling of expenses related to these emissions both due to state-level levies and participation in the EU Emissions Trading Scheme (EU ETS). To illustrate the advantages of the augmented model, we simulate a scenario in which Latvia follows a linear path to achieve GHG emissions reduction consistent with its European Green Deal objectives by 2030 achieved solely through carbon pricing. The analysis of this scenario suggests that over a three-year horizon ending in 2025, the resulting cumulative welfare losses would exceed 2% in the case of an uncompensated carbon tax (resulting in a budget balance improvement of 2.6% of GDP) or amount to 0.3% if government consumption is increased to keep the budget balance constant. If instead the size of the public sector is maintained and the higher carbon tax is compensated by a VAT rate cut, economic activity expands by 1% but GHG emissions fall by 40% less. |
| Keywords: | CGE model, Latvia, GHG emissions, Emissions Trading Scheme, carbon tax, energy substitution, green transformation, energy transition, European Green Deal, EUROMOD |
| JEL: | C68 Q58 Q48 Q54 Q41 |
| Date: | 2023–12–18 |
| URL: | http://d.repec.org/n?u=RePEc:ltv:wpaper:202307&r=cmp |
| By: | Joao Guerreiro; Sergio Rebelo; Pedro Teles |
| Abstract: | We consider an environment in which there is substantial uncertainty about the potential adverse external effects of AI algorithms. We find that subjecting algorithm implementation to regulatory approval or mandating testing is insufficient to implement the social optimum. When testing costs are low, a combination of mandatory testing for external effects and making developers liable for the adverse external effects of their algorithms comes close to implementing the social optimum even when developers have limited liability. |
| JEL: | H21 O33 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31921&r=cmp |
| By: | Jen-Yin Yeh; Hsin-Yu Chiu; Jhih-Huei Huang |
| Abstract: | This study employs machine learning models to predict the failure of Peer-to-Peer (P2P) lending platforms, specifically in China. By employing the filter method and wrapper method with forward selection and backward elimination, we establish a rigorous and practical procedure that ensures the robustness and importance of variables in predicting platform failures. The research identifies a set of robust variables that consistently appear in the feature subsets across different selection methods and models, suggesting their reliability and relevance in predicting platform failures. The study highlights that reducing the number of variables in the feature subset leads to an increase in the false acceptance rate while the performance metrics remain stable, with an AUC value of approximately 0.96 and an F1 score of around 0.88. The findings of this research provide significant practical implications for regulatory authorities and investors operating in the Chinese P2P lending industry. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14577&r=cmp |
| By: | Ummara Mumtaz; Summaya Mumtaz |
| Abstract: | The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across different areas, our study investigates ChatGPT's capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data on platforms like Twitter to offer insightful predictions about stock trends. We focus on determining if a tweet has a positive, negative, or neutral effect on two big tech giants Microsoft and Google's stock value. Our findings highlight a positive link between ChatGPT's evaluations and the following days stock results for both tech companies. This research enriches our view on ChatGPT's adaptability and emphasizes the growing importance of AI in shaping financial market forecasts. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06273&r=cmp |
| By: | Haoqiang Kang; Xiao-Yang Liu |
| Abstract: | The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Despite the growing concerns, there has been a lack of empirical investigation. In this paper, we provide an empirical examination of LLMs' hallucination behaviors in financial tasks. First, we empirically investigate LLM model's ability of explaining financial concepts and terminologies. Second, we assess LLM models' capacity of querying historical stock prices. Third, to alleviate the hallucination issue, we evaluate the efficacy of four practical methods, including few-shot learning, Decoding by Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method and the prompt-based tool learning method for a function to generate a query command. Finally, our major finding is that off-the-shelf LLMs experience serious hallucination behaviors in financial tasks. Therefore, there is an urgent need to call for research efforts in mitigating LLMs' hallucination. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15548&r=cmp |
| By: | Ruiqi Sun; Daniel Trefler |
| Abstract: | The rise of artificial intelligence (AI) and of cross-border restrictions on data flows has created a host of new questions and related policy dilemmas. This paper addresses two questions: How is digital service trade shaped by (1) AI algorithms and (2) by the interplay between AI algorithms and cross-border restrictions on data flows? Answers lie in the palm of your hand: From London to Lagos, mobile app users trigger international transactions when they open AI-powered foreign apps. We have 2015-2020 usage data for the most popular 35, 575 mobile apps and, to quantify the AI deployed in each of these apps, we use a large language model (LLM) to link each app to each of the app developer's AI patents. (This linkage of specific products to specific patents is a methodological innovation.) Armed with data on app usage by country, with AI deployed in each app, and with an instrument for AI (a Heckscher-Ohlin cost-shifter), we answer our two questions. (1) On average, AI causally raises an app's number of foreign users by 2.67 log points or by more than 10-fold. (2) The impact of AI on foreign users is halved if the foreign users are in a country with strong restrictions on cross-border data flows. These countries are usually autocracies. We also provide a new way of measuring AI knowledge spillovers across firms and find large spillovers. Finally, our work suggests numerous ways in which LLMs such as ChatGPT can be used in other applications. |
| JEL: | F12 F13 F14 F23 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31925&r=cmp |
| By: | Paul Labonne; Leif Anders Thorsrud |
| Abstract: | The nonlinear nexus between financial conditions indicators and the conditional distribution of GDP growth has recently been challenged. We show how one can use textual economic news combined with a shallow Neural Network to construct an alternative financial indicator based on word embeddings. By design the index associates growth-at-risk to news about credit, leverage and funding, and we document that the proposed indicator is particularly informative about the lower left tail of the GDP distribution and delivers significantly better out-of-sample density forecasts than commonly used alternatives. Speaking to theories on endogenous information choice and credit-market sentiment we further document that the news-based index likely carries information about beliefs rather than fundamentals. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0125&r=cmp |
| By: | von der Heyde, Leah (LMU Munich); Haensch, Anna-Carolina; Wenz, Alexander (University of Mannheim) |
| Abstract: | The rise of large language models (LLMs) like GPT-3 has sparked interest in their potential for creating synthetic datasets, particularly in the realm of privacy research. This study critically evaluates the use of LLMs in generating synthetic public opinion data, pointing out the biases inherent in the data generation process. While LLMs, trained on vast internet datasets, can mimic societal attitudes and behaviors, their application in synthesizing data poses significant privacy and accuracy challenges. We investigate these issues using the case of vote choice prediction in the 2017 German federal elections. Employing GPT-3, we construct synthetic personas based on the German Longitudinal Election Study, prompting the LLM to predict voting behavior. Our analysis compares these LLM-generated predictions with actual survey data, focusing on the implications of using such synthetic data and the biases it may contain. The results demonstrate GPT-3’s propensity to inaccurately predict voter choices, with biases favoring certain political groups and more predictable voter profiles. This outcome raises critical questions about the reliability and ethical use of LLMs in generating synthetic data. |
| Date: | 2023–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:97r8s&r=cmp |
| By: | Yuzhakov, Vladimir (Южаков, Владимир) (The Russian Presidential Academy of National Economy and Public Administration); Talapina, Elvira (Талапина, Эльвира) (The Russian Presidential Academy of National Economy and Public Administration); Chereshneva, Irina (Черешнева, Ирина) (The Russian Presidential Academy of National Economy and Public Administration) |
| Abstract: | Nowadays none country in the world has developed a holistic regulatory approach to regulation of artificial intelligence use. Meanwhile, in the digital landscape the risks of human rights violations are increasing, which is gained newfound relevance of the need to simultaneous development of legal protection of human rights in mastery of technology and artificial intelligence capabilities by public administration. In this regard the objectives of this paper are to analyze the current state of legal regulation of AI use in public administration and to formulate of proposals for legal groundwork for preventing and overcoming the risks of human rights violations due to the use of AI in public administration. As a part of the study scientific publications on this topic, international and national laws and regulations, including foreign countries have been analyzed. The main results of the preprint are the results of the analysis of the state of legal regulation of AI use in public administration; the results of the analysis of potential risks of human rights violations due to the use of AI in public administration; the author's systematization of the risks of human rights violations due to the use of AI in public administration, as varied the institutional system of rights protection; the proposals for legal means to prevent and overcome the risks of human rights violations due to the use of AI in public administration. The study allows for the conclusion about the lack of attention to the human rights aspect in forecasting, regulating and using of AI in public administration as well as the necessity to design a system of legal means to prevent and overcome the risks of human rights violations due to the use of AI in public administration. The lack of proper regulation of AI use in public administration, which involves the risks of human rights violations, determines the scientific novelty and potential practical significance of the study brought to attention of readers. The author's concept involves the promotion of a human-centered approach to the use of artificial intelligence technology in public administration. Its findings and results can be taken into account and used for the formulation and state policy implementation in ensuring human rights due to the use of AI in the Russian public administration. |
| Keywords: | Human rights, public administration, data, artificial intelligence, personal data, big data processing, algorithm |
| JEL: | H11 H83 K38 |
| Date: | 2022–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:wpaper:w20220285&r=cmp |
| By: | Gabriel Turinici; Pierre Brugiere |
| Abstract: | We introduce Onflow, a reinforcement learning technique that enables online optimization of portfolio allocation policies based on gradient flows. We devise dynamic allocations of an investment portfolio to maximize its expected log return while taking into account transaction fees. The portfolio allocation is parameterized through a softmax function, and at each time step, the gradient flow method leads to an ordinary differential equation whose solutions correspond to the updated allocations. This algorithm belongs to the large class of stochastic optimization procedures; we measure its efficiency by comparing our results to the mathematical theoretical values in a log-normal framework and to standard benchmarks from the 'old NYSE' dataset. For log-normal assets, the strategy learned by Onflow, with transaction costs at zero, mimics Markowitz's optimal portfolio and thus the best possible asset allocation strategy. Numerical experiments from the 'old NYSE' dataset show that Onflow leads to dynamic asset allocation strategies whose performances are: a) comparable to benchmark strategies such as Cover's Universal Portfolio or Helmbold et al. "multiplicative updates" approach when transaction costs are zero, and b) better than previous procedures when transaction costs are high. Onflow can even remain efficient in regimes where other dynamical allocation techniques do not work anymore. Therefore, as far as tested, Onflow appears to be a promising dynamic portfolio management strategy based on observed prices only and without any assumption on the laws of distributions of the underlying assets' returns. In particular it could avoid model risk when building a trading strategy. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.05169&r=cmp |
| By: | Nir Chemaya; Daniel Martin |
| Abstract: | The emergent abilities of Large Language Models (LLMs), which power tools like ChatGPT and Bard, have produced both excitement and worry about how AI will impact academic writing. In response to rising concerns about AI use, authors of academic publications may decide to voluntarily disclose any AI tools they use to revise their manuscripts, and journals and conferences could begin mandating disclosure and/or turn to using detection services, as many teachers have done with student writing in class settings. Given these looming possibilities, we investigate whether academics view it as necessary to report AI use in manuscript preparation and how detectors react to the use of AI in academic writing. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14720&r=cmp |
| By: | Lijun Bo; Yijie Huang; Xiang Yu |
| Abstract: | This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models. We consider the benchmark process modelled by a geometric Brownian motion with zero drift driven by some unhedgeable risk. The relaxed tracking formulation is adopted where the portfolio value compensated by the injected capital needs to outperform the benchmark process at any time, and the goal is to minimize the cost of the discounted total capital injection. In the first part, we solve the stochastic control problem when the market model is known, for which the equivalent auxiliary control problem with reflections and the associated HJB equation with a Neumann boundary condition are studied. In the second part, the market model is assumed to be unknown, for which we consider the exploratory formulation of the control problem with entropy regularizer and develop the continuous-time q-learning algorithm for the stochastic control problem with state reflections. In an illustrative example, we show the satisfactory performance of the q-learning algorithm. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14318&r=cmp |
| By: | Jingyi Tian; Jun Nagayasu |
| Abstract: | As an important domain of information technology development, artificial intelligence (AI) has garnered significant popularity in the financial sector. While AI offers numerous advantages, investigating potential risks associated with the widespread use of AI has become a critical point for researchers. We examine the impact of AI technologies on systemic risk within China’s financial industry. Our findings suggest that AI helps mitigate the increase of systemic risk. However, the impact of AI differs across different financial sectors and is more pronounced during crisis periods. Our study also suggests that AI can decrease systemic risk by enhancing the human capital of financial firms. Moreover, the theoretical framework presented in this paper provides insights into the notion that imprudent allocation of AI-related investment could potentially contribute to an increase in systemic risk. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:toh:tupdaa:44&r=cmp |
| By: | MODIS, THEODORE (CERN) |
| Abstract: | This is an update of a 22-year old study that attempted to quantify complexity (in arbitrary units) for the entire Universe in terms of 28 evolutionary milestones – breaks in historical perspective – and concluded that complexity will soon begin decreasing. AI is considered here as the latest such milestone. At the same time the data have been improved and the focus is now sharpened by studying only the recent 14 milestones, those relating to humans. The old conclusion is corroborated here with AI positioned at the beginning of the downward trend of the complexity cycle. The contribution of AI to complexity is expected to be somewhat smaller than that of the Internet. The next evolutionary milestone of comparable importance is expected around 2052 and should add less complexity than AI but more than nuclear energy/DNA/transistor. |
| Date: | 2023–11–21 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:rtw9b&r=cmp |
| By: | Goller, Daniel (University of Bern); Gschwendt, Christian (University of Bern); Wolter, Stefan C. (University of Bern) |
| Abstract: | In this paper, we show the causal influence of the launch of generative AI in the form of ChatGPT on the search behavior of young people for apprenticeship vacancies. There is a strong and long-lasting decline in the intensity of searches for vacancies, which suggests great uncertainty among the affected cohort. Analyses based on the classification of occupations according to tasks, type of cognitive requirements, and the expected risk of automation to date show significant differences in the extent to which specific occupations are affected. Occupations with a high proportion of cognitive tasks, with high demands on language skills, and those whose automation risk had previously been assessed by experts as lower are significantly more affected by the decline. However, no differences can be found with regard to the proportion of routine vs. non-routine tasks. |
| Keywords: | artificial intelligence, occupational choice, labor supply, technological change |
| JEL: | J24 O33 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16638&r=cmp |
| By: | Yu, Chen |
| Abstract: | The advent of artificial intelligence (AI) has ushered in a new age of technological advancement, with profound implications for the workforce and societal structures. The article "AI Eat Men and the Awakening of the Middle Class" delves into the socioeconomic impact of AI on the middle class, a demographic that stands at the crossroads of potential displacement and opportunity. It explores the growing concern over job automation and the consequent call for a reevaluation of the social contract in an AI-driven world. The article discusses the proposed "AI Tax" as a means to address the displacement of human labor and to redistribute the benefits of increased productivity. It also highlights the proactive stance of the middle class in advocating for policies that promote ethical AI integration, ensuring that technology augments rather than replaces human work. The conclusion posits that the middle class's awakening is a crucial step toward a balanced future where AI enhances human potential, advocating for a society that values human contribution in the age of automation. |
| Date: | 2023–12–05 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:bj2p4&r=cmp |
| By: | Deborah Miori; Constantin Petrov |
| Abstract: | Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14419&r=cmp |
| By: | Mohammad Rasouli; Ravi Chiruvolu; Ali Risheh |
| Abstract: | With the investment landscape becoming more competitive, efficiently scaling deal sourcing and improving deal insights have become a dominant strategy for funds. While funds are already spending significant efforts on these two tasks, they cannot be scaled with traditional approaches; hence, there is a surge in automating them. Many third party software providers have emerged recently to address this need with productivity solutions, but they fail due to a lack of personalization for the fund, privacy constraints, and natural limits of software use cases. Therefore, most major funds and many smaller funds have started developing their in-house AI platforms: a game changer for the industry. These platforms grow smarter by direct interactions with the fund and can be used to provide personalized use cases. Recent developments in large language models, e.g. ChatGPT, have provided an opportunity for other funds to also develop their own AI platforms. While not having an AI platform now is not a competitive disadvantage, it will be in two years. Funds require a practical plan and corresponding risk assessments for such AI platforms. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06251&r=cmp |
| By: | GÓMEZ-GONZÁLEZ Emilio; GOMEZ Emilia (European Commission - JRC) |
| Abstract: | This report provides a state of the art of the current and near-future applications of Artificial Intelligence (AI) in medicine, healthcare and well-being, building on previous analyses, and framed in recent historical circumstances of the COVID-19 pandemic and the war in Ukraine. The present analysis includes software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. It identifies the particularities of AI systems, the opportunities and risks associated to them, as well as its social impact, considering their maturity, availability, controversy, and sustainability. From this review, the report identifies 100 relevant topics, and discusses lessons learnt in the area from the mentioned historical circumstances. In addition, the present study recognizes five key expanding areas with particular significance in terms of social impact: AI tools for mental health, AI-mediated gene editing, AI tools for epidemiology and health data monitoring, AI-mediated neuro-technologies and AI-mediated inclusion of neurodiversity, and describes them in detail considering the proposed social assessment scales. We conclude with some science for policy challenges in the field. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc134715&r=cmp |
| By: | Christophe Carugati |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bre:wpaper:node_9593&r=cmp |
| By: | L. Ingber |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:lei:ingber:23qf&r=cmp |