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on Artificial Intelligence |
| By: | Van Pham; Scott Cunningham |
| Abstract: | This study investigates whether OpenAI's ChatGPT-3.5 and ChatGPT-4 can accurately forecast future events using two distinct prompting strategies. To evaluate the accuracy of the predictions, we take advantage of the fact that the training data at the time of experiment stopped at September 2021, and ask about events that happened in 2022 using ChatGPT-3.5 and ChatGPT-4. We employed two prompting strategies: direct prediction and what we call future narratives which ask ChatGPT to tell fictional stories set in the future with characters that share events that have happened to them, but after ChatGPT's training data had been collected. Concentrating on events in 2022, we prompted ChatGPT to engage in storytelling, particularly within economic contexts. After analyzing 100 prompts, we discovered that future narrative prompts significantly enhanced ChatGPT-4's forecasting accuracy. This was especially evident in its predictions of major Academy Award winners as well as economic trends, the latter inferred from scenarios where the model impersonated public figures like the Federal Reserve Chair, Jerome Powell. These findings indicate that narrative prompts leverage the models' capacity for hallucinatory narrative construction, facilitating more effective data synthesis and extrapolation than straightforward predictions. Our research reveals new aspects of LLMs' predictive capabilities and suggests potential future applications in analytical contexts. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.07396&r=ain |
| By: | Sara Fish; Yannai A. Gonczarowski; Ran I. Shorrer |
| Abstract: | The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions ("prompts") may increase collusion. These results extend to auction settings. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.00806&r=ain |
| By: | Elias Fernández Domingos; Inês Terrucha; Rémi Suchon (ETHICS EA 7446 - Experience ; Technology & Human Interactions ; Care & Society : - ICL - Institut Catholique de Lille - UCL - Université catholique de Lille, UCL - Université catholique de Lille); Jelena Grujić; Juan Burguillo; Francisco Santos; Tom Lenaerts |
| Abstract: | Home assistant chat-bots, self-driving cars, drones or automated negotiation systems are some of the several examples of autonomous (artificial) agents that have pervaded our society. These agents enable the automation of multiple tasks, saving time and (human) effort. However, their presence in social settings raises the need for a better understanding of their effect on social interactions and how they may be used to enhance cooperation towards the public good, instead of hindering it. To this end, we present an experimental study of human delegation to autonomous agents and hybrid human-agent interactions centered on a non-linear public goods dilemma with uncertain returns in which participants face a collective risk. Our aim is to understand experimentally whether the presence of autonomous agents has a positive or negative impact on social behaviour, equality and cooperation in such a dilemma. Our results show that cooperation and group success increases when participants delegate their actions to an artificial agent that plays on their behalf. Yet, this positive effect is less pronounced when humans interact in hybrid human-agent groups, where we mostly observe that humans in successful hybrid groups make higher contributions earlier in the game. Also, we show that participants wrongly believe that artificial agents will contribute less to the collective effort. In general, our results suggest that delegation to autonomous agents has the potential to work as commitment devices, which prevent both the temptation to deviate to an alternate (less collectively good) course of action, as well as limiting responses based on betrayal aversion. |
| Date: | 2022–05–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04296038&r=ain |
| By: | Siting Lu |
| Abstract: | The growing adoption of large language models (LLMs) presents substantial potential for deeper understanding of human behaviours within game theory frameworks through simulations. Leveraging on the diverse pool of LLM types and addressing the gap in research on competitive games, this paper examines the strategic interactions among multiple types of LLM-based agents in a classical game of beauty contest. Drawing parallels to experiments involving human subjects, LLM-based agents are assessed similarly in terms of strategic levels. They demonstrate varying depth of reasoning that falls within a range of level-0 and 1, and show convergence in actions in repeated settings. Furthermore, I also explore how variations in group composition of agent types influence strategic behaviours, where I found higher proportion of fixed-strategy opponents enhances convergence for LLM-based agents, and having a mixed environment with agents of differing relative strategic levels accelerates convergence for all agents. There could also be higher average payoffs for the more intelligent agents, albeit at the expense of the less intelligent agents. These results not only provide insights into outcomes for simulated agents under specified scenarios, it also offer valuable implications for understanding strategic interactions between algorithms. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08492&r=ain |
| By: | Annie Liang; Jay Lu |
| Abstract: | Algorithms are increasingly used to guide high-stakes decisions about individuals. Consequently, substantial interest has developed around defining and measuring the ``fairness'' of these algorithms. These definitions of fair algorithms share two features: First, they prioritize the role of a pre-defined group identity (e.g., race or gender) by focusing on how the algorithm's impact differs systematically across groups. Second, they are statistical in nature; for example, comparing false positive rates, or assessing whether group identity is independent of the decision (where both are viewed as random variables). These notions are facially distinct from a social welfare approach to fairness, in particular one based on ``veil of ignorance'' thought experiments in which individuals choose how to structure society prior to the realization of their social identity. In this paper, we seek to understand and organize the relationship between these different approaches to fairness. Can the optimization criteria proposed in the algorithmic fairness literature also be motivated as the choices of someone from behind the veil of ignorance? If not, what properties distinguish either approach to fairness? |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.04424&r=ain |
| By: | Knoth, Nils; Decker, Marie; Laupichler, Matthias Carl; Pinski, Marc; Buchholtz, Nils; Bata, Katharina; Schultz, Ben |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:144414&r=ain |
| By: | Pelin Ozgul; Marie-Christine Fregin; Michael Stops; Simon Janssen; Mark Levels |
| Abstract: | Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI. Moreover, we observe that wage growth premiums for the lowest and the highest required skill level appear unrelated to AI susceptibility and that workers in occupations with many routine tasks saw higher wage growth if their work was more strongly susceptible to AI. Our findings imply that AI has the potential to affect human workers differently than canonical economic theories about the impact of technology on work these theories predict. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.06472&r=ain |
| By: | Pinksi, Marc; Hofmann, Thomas; Benlian, Alexander |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:144321&r=ain |
| By: | Iñaki Aldasoro; Sebastian Doerr; Leonardo Gambacorta; Daniel Rees |
| Abstract: | This paper studies the effects of artificial intelligence (AI) on sectoral and aggregate employment, output and inflation in both the short and long run. We construct an index of industry exposure to AI to calibrate a macroeconomic multi-sector model. Building on studies that find significant increases in workers' output from AI, we model AI as a permanent increase in productivity that differs by sector. We find that AI significantly raises output, consumption and investment in the short and long run. The inflation response depends crucially on households' and firms' anticipation of the impact of AI. If they do not anticipate higher future productivity, AI adoption is initially disinflationary. Over time, general equilibrium forces lead to moderate inflation through demand effects. In contrast, when households and firms anticipate higher future productivity, inflation rises immediately. Inspecting individual sectors and performing counterfactual exercises we find that a sector's initial exposure to AI has little correlation with its long-term increase in output. However, output grows by twice as much for the same increase in aggregate productivity when AI affects sectors producing consumption rather than investment goods, thanks to second round effects through sectoral linkages. We discuss how public policy should foster AI adoption and implications for central banks. |
| Keywords: | artificial intelligence, generative AI, inflation, output, productivity, monetary policy |
| JEL: | E31 J24 O33 O40 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1179&r=ain |
| By: | Thorsten Hens (Department of Finance, University of Zurich, Department of Finance, Norwegian School of Economics, NHH, Institute of Economic Research, Kyoto University); Trine Nordlie (Department of Finance, Norwegian School of Economics, NHH, Bergen) |
| Abstract: | This study compares OpenAI's ChatGPT-4 and Google's Bard with bank experts in determining investors'risk profiles. We find that for half of the client cases used, there are no statistically significant differences in the risk profiles. Moreover, the economic relevance of the differences is small. |
| Keywords: | Large Language Models, ChatGPT, Bard, Risk Profiling |
| JEL: | D8 D14 D81 G51 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:kyo:wpaper:1103&r=ain |
| By: | Kirtac, Kemal; Germano, Guido |
| Abstract: | We analyse the performance of the large language models (LLMs) OPT, BERT, and FinBERT, alongside the traditional Loughran-McDonald dictionary, in the sentiment analysis of 965, 375 U.S. financial news articles from 2010 to 2023. Our findings reveal that the GPT-3-based OPT model significantly outperforms the others, predicting stock market returns with an accuracy of 74.4%. A long-short strategy based on OPT, accounting for 10 basis points (bps) in transaction costs, yields an exceptional Sharpe ratio of 3.05. From August 2021 to July 2023, this strategy produces an impressive 355% gain, outperforming other strategies and traditional market portfolios. This underscores the transformative potential of LLMs in financial market prediction and portfolio management and the necessity of employing sophisticated language models to develop effective investment strategies based on news sentiment. |
| Keywords: | artificial intelligence investment strategies; generative pre-trained transformer (GPT); large language models; machine learning in stock return prediction; natural language processing (NLP) |
| JEL: | C53 G10 G11 G12 G14 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122592&r=ain |
| By: | Yupeng Cao; Zhi Chen; Qingyun Pei; Fabrizio Dimino; Lorenzo Ausiello; Prashant Kumar; K. P. Subbalakshmi; Papa Momar Ndiaye |
| Abstract: | The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$\&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely combines different types of financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. Our approach involves a multi-stage process: initially extracting and analyzing ECC data using LLMs, followed by gathering and processing time-series data before the ECC dates to model and understand risk over different timeframes. Using multimodal fusion techniques, RiskLabs amalgamates these varied data features for comprehensive multi-task financial risk prediction. Empirical experiment results demonstrate RiskLab's effectiveness in forecasting both volatility and variance in financial markets. Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context. Our findings not only contribute to the AI in finance application but also open new avenues for applying LLMs in financial risk assessment. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.07452&r=ain |
| By: | Dat Mai |
| Abstract: | This paper introduces StockGPT, an autoregressive "number" model pretrained directly on the history of daily U.S. stock returns. Treating each return series as a sequence of tokens, the model excels at understanding and predicting the highly intricate stock return dynamics. Instead of relying on handcrafted trading patterns using historical stock prices, StockGPT automatically learns the hidden representations predictive of future returns via its attention mechanism. On a held-out test sample from 2001 to 2023, a daily rebalanced long-short portfolio formed from StockGPT predictions earns an annual return of 119% with a Sharpe ratio of 6.5. The StockGPT-based portfolio completely explains away momentum and long-/short-term reversals, eliminating the need for manually crafted price-based strategies and also encompasses most leading stock market factors. This highlights the immense promise of generative AI in surpassing human in making complex financial investment decisions and illustrates the efficacy of the attention mechanism of large language models when applied to a completely different domain. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.05101&r=ain |
| By: | Armand Mihai Cismaru |
| Abstract: | In this paper, we introduce DeepTraderX (DTX), a simple Deep Learning-based trader, and present results that demonstrate its performance in a multi-threaded market simulation. In a total of about 500 simulated market days, DTX has learned solely by watching the prices that other strategies produce. By doing this, it has successfully created a mapping from market data to quotes, either bid or ask orders, to place for an asset. Trained on historical Level-2 market data, i.e., the Limit Order Book (LOB) for specific tradable assets, DTX processes the market state $S$ at each timestep $T$ to determine a price $P$ for market orders. The market data used in both training and testing was generated from unique market schedules based on real historic stock market data. DTX was tested extensively against the best strategies in the literature, with its results validated by statistical analysis. Our findings underscore DTX's capability to rival, and in many instances, surpass, the performance of public-domain traders, including those that outclass human traders, emphasising the efficiency of simple models, as this is required to succeed in intricate multi-threaded simulations. This highlights the potential of leveraging "black-box" Deep Learning systems to create more efficient financial markets. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.18831&r=ain |
| By: | Kapil Panda |
| Abstract: | Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of international finances, necessitating a nuanced understanding of the correlations between international and national markets to help investors make informed investment decisions. Therefore, by leveraging the capabilities of artificial intelligence, this study utilizes neural networks to depict the correlations between US and International Public Finances and predict the changes in international public finances based on the changes in US public finances. With the neural network model achieving a commendable Mean Squared Error (MSE) value of 2.79, it is able to affirm a discernible correlation and also plot the effect of US market volatility on international markets. To further test the accuracy and significance of the model, an economic analysis was conducted that aimed to correlate the changes seen by the results of the model with historical stock market changes. This model demonstrates significant potential for investors to predict changes in international public finances based on signals from US markets, marking a significant stride in comprehending the intricacies of global public finances and the role of artificial intelligence in decoding its multifaceted patterns for practical forecasting. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.18823&r=ain |
| By: | KOUAKOU, Dorgyles C.M.; SZEGO, Eva |
| Abstract: | This paper employs graph theory to assess the extent of integration of artificial intelligence (AI) technologies within defense activities and investigates how the performance of the national innovation system (NIS) influences this integration. The analysis utilizes data from 33 countries with defense industries, observed from 1990 to 2020. Empirical findings indicate that the United States (U.S.) leads globally, with a significant gap between the U.S. and other countries. NIS performance increases the level of integration of AI technologies in defense activities, suggesting that policies aimed at strengthening NIS performance should have positive externalities on defense activities in terms of integrating AI technologies. Technological diversification, knowledge localization, and originality are key dimensions of NIS performance that significantly enhance the integration of AI technologies within defense activities. They exhibit similar average marginal effects, suggesting comparable impacts. The cycle time of technologies has an inverted-U shaped relationship with the level of integration. |
| Keywords: | Integration of AI technologies; Defense activities; National innovation system |
| JEL: | L64 O31 O34 O38 |
| Date: | 2024–04–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120617&r=ain |