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on Artificial Intelligence |
| By: | Brian Jabarian |
| Abstract: | In this article, we explore the transformative potential of integrating generative AI, particularly Large Language Models (LLMs), into behavioral and experimental economics to enhance internal validity. By leveraging AI tools, researchers can improve adherence to key exclusion restrictions and in particular ensure the internal validity measures of mental models, which often require human intervention in the incentive mechanism. We present a case study demonstrating how LLMs can enhance experimental design, participant engagement, and the validity of measuring mental models. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.12032 |
| By: | Villarino, Resti Tito (Cebu Technological University); Villarino, Maureen Lorence |
| Abstract: | Background: In social sciences, an appreciable degree of instrument validation is intended to maintain scientific rigor and research quality. Objectives: To develop and evaluate an AI chatbot and website for instrument validation, assess their impact on instrument validity improvement, and analyze user perceptions. Methods: Adopting a quantitative design, the study was anchored on the developed RIVF of Villarino (2024). Moreover, it was evaluated through users' perceptions (n=100) by administering an online survey, whereby the employment of pairing t-tests used contrasting instrument validity-pre-vs post-RIVF scores, and one-way ANOVA was used to determine if a relationship existed between users' perceptions and total improvement in validity. A G*Power analysis showed that there was enough statistical power for the analyses: for paired t-tests, it was 99.73% (n = 100, dz = 0.5, α = 0.05), and for one-way ANOVA, 80.95% (n = 100, f = 0.25, α=0.05, four groups). All data were analyzed using IBM SPSS version 26. Results: Post-RIVF use, all the validity domains showed significant improvements (p-value greater than 0.001), but the maximum significant gain was seen in construct validity [Mean difference=1.20±0.60, t(49)=14.14]. Subjects perceived the AI chatbot as more useful [4.30±0.70 vs. 3.80±0.80, p-value greater than 0.001]. Conclusion: This AI-powered ecosystem indicates a potential for increasing the validity of research instruments in RIVF, while an AI chatbot is efficient in incrementing the construct validity. These results would infer that the use of AI technologies along with traditional validation methods will improve the quality of research instruments in the social sciences. Keywords: artificial intelligence (AI), instrument validation, research methodology, social sciences |
| Date: | 2024–07–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:rjyzg |
| By: | Zachary Wojtowicz |
| Abstract: | As generative foundation models improve, they also tend to become more persuasive, raising concerns that AI automation will enable governments, firms, and other actors to manipulate beliefs with unprecedented scale and effectiveness at virtually no cost. The full economic and social ramifications of this trend have been difficult to foresee, however, given that we currently lack a complete theoretical understanding of why persuasion is costly for human labor to produce in the first place. This paper places human and AI agents on a common conceptual footing by formalizing informational persuasion as a mathematical decision problem and characterizing its computational complexity. A novel proof establishes that persuasive messages are challenging to discover (NP-Hard) but easy to adopt if supplied by others (NP). This asymmetry helps explain why people are susceptible to persuasion, even in contexts where all relevant information is publicly available. The result also illuminates why litigation, strategic communication, and other persuasion-oriented activities have historically been so human capital intensive, and it provides a new theoretical basis for studying how AI will impact various industries. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.07923 |
| By: | Teng Ye; Jingnan Zheng; Junhui Jin; Jingyi Qiu; Wei Ai; Qiaozhu Mei |
| Abstract: | While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas. We utilize the latest advancements in AI technology to identify crucial factors that influence the success of crowdfunding campaigns and to improve their fundraising outcomes by strategically optimizing these factors. Our best-performing machine learning model accurately predicts the fundraising outcomes of 81.0% of campaigns, primarily based on their textual descriptions. Interpreting the machine learning model allows us to provide actionable suggestions on improving the textual description before launching a campaign. We demonstrate that by augmenting just three aspects of the narrative using a large language model, a campaign becomes more preferable to 83% human evaluators, and its likelihood of securing financial support increases by 11.9%. Our research uncovers the effective strategies for crafting descriptions for small business fundraising campaigns and opens up a new realm in integrating large language models into crowdfunding methodologies. |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.09480 |
| By: | Yao Lu; Gordon M. Phillips; Jia Yang |
| Abstract: | We examine the rise of cloud computing and AI in China and their impacts on industry dynamics after the shock to the cost of Internet-based computing power and services. We find that cloud computing is associated with an increase in firm entry, exit and the likelihood of M&A in industries that depend more on cloud infrastructure. Conversely, AI adoption has no impact on entry but reduces the likelihood of exit and M&A. Firm size plays a crucial role in these dynamics: cloud computing increases exit rates across all firms, while larger firms benefit from AI, experiencing reduced exit rates. Cloud computing decreases industry concentration but AI increases concentration. On the financing side, firms exposed to cloud computing increase equity and venture capital financing, while only large firms increase equity financing when exposed to AI. |
| JEL: | D25 G3 G34 L20 L23 L25 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32811 |
| By: | Bastani, Spencer (Uppsala University); Waldenström, Daniel (Research Institute of Industrial Economics, Stockholm) |
| Abstract: | This paper examines the implications of Artificial Intelligence (AI) and automation for the taxation of labor and capital in advanced economies. It synthesizes empirical evidence on worker displacement, productivity, and income inequality, as well as theoretical frameworks for optimal taxation. Implications for tax policy are discussed, focusing on the level of capital taxes and the progressivity of labor taxes. While there may be a need to adjust the level of capital taxes and the structure of labor income taxation, there are potential drawbacks of overly progressive taxation and universal basic income schemes that could undermine work incentives, economic growth, and long-term household welfare. Some of the challenges posed by AI and automation may also be better addressed through regulatory measures rather than tax policy. |
| Keywords: | AI, automation, inequality, labor share, optimal taxation, tax progressivity |
| JEL: | H21 H30 O33 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izapps:pp212 |
| By: | Jon Danielsson; Andreas Uthemann |
| Abstract: | The rapid adoption of artificial intelligence (AI) is transforming the financial industry. AI will either increase systemic financial risk or act to stabilise the system, depending on endogenous responses, strategic complementarities, the severity of events it faces and the objectives it is given. AI's ability to master complexity and respond rapidly to shocks means future crises will likely be more intense than those we have seen so far. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.17048 |
| By: | Haowei Ni; Shuchen Meng; Xupeng Chen; Ziqing Zhao; Andi Chen; Panfeng Li; Shiyao Zhang; Qifu Yin; Yuanqing Wang; Yuxi Chan |
| Abstract: | Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a 'Hold' option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.06634 |
| By: | Colliard, Jean-Edouard (HEC Paris); Foucault, Thierry (HEC Paris); Lovo, Stefano (HEC Paris) |
| Abstract: | We let ``Algorithmic Market Makers'' (AMs), using Q-learning algorithms, determine prices for a risky asset in a standard market making game with adverse selection and compare these prices to the Nash equilibrium of the game. We observe that AMs effectively adapt to adverse selection, adjusting prices post-trade as anticipated. However, AMs charge a markup over the competitive price and this markup increases when adverse selection costs decrease, in contrast to the predictions of the Nash equilibrium. We attribute this unexpected pattern to the diminished learning capacity of AMs when faced with increased profit variance. |
| Keywords: | Algorithmic pricing; Market Making; Adverse Selection; Market Power; Reinforcement learning |
| JEL: | D43 G10 G14 |
| Date: | 2022–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:ebg:heccah:1459 |
| By: | Wee Ling Tan; Stephen Roberts; Stefan Zohren |
| Abstract: | We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.21791 |
| By: | Andras Komaromi; Xiaomin Wu; Ran Pan; Yang Liu; Pablo Cisneros; Anchal Manocha; Hiba El Oirghi |
| Abstract: | The International Monetary Fund (IMF) has expanded its online learning program, offering over 100 Massive Open Online Courses (MOOCs) to support economic and financial policymaking worldwide. This paper explores the application of Artificial Intelligence (AI), specifically Large Language Models (LLMs), to analyze qualitative feedback from participants in these courses. By fine-tuning a pre-trained LLM on expert-annotated text data, we develop models that efficiently classify open-ended survey responses with accuracy comparable to human coders. The models’ robust performance across multiple languages, including English, French, and Spanish, demonstrates its versatility. Key insights from the analysis include a preference for shorter, modular content, with variations across genders, and the significant impact of language barriers on learning outcomes. These and other findings from unstructured learner feedback inform the continuous improvement of the IMF's online courses, aligning with its capacity development goals to enhance economic and financial expertise globally. |
| Date: | 2024–08–02 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/166 |
| By: | Gregory Yampolsky; Dhruv Desai; Mingshu Li; Stefano Pasquali; Dhagash Mehta |
| Abstract: | The explainability of black-box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated industrial applications due to regulatory requirements and the need for transparency in business practices. Among the various paradigms of XAI, Explainable Case-Based Reasoning (XCBR) stands out as a pragmatic approach that elucidates the output of a model by referencing actual examples from the data used to train or test the model. Despite its potential, XCBR has been relatively underexplored for many algorithms such as tree-based models until recently. We start by observing that most XCBR methods are defined based on the distance metric learned by the algorithm. By utilizing a recently proposed technique to extract the distance metric learned by Random Forests (RFs), which is both geometry- and accuracy-preserving, we investigate various XCBR methods. These methods amount to identify special points from the training datasets, such as prototypes, critics, counter-factuals, and semi-factuals, to explain the predictions for a given query of the RF. We evaluate these special points using various evaluation metrics to assess their explanatory power and effectiveness. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.06679 |