|
on Computational Economics |
|
Issue of 2024‒04‒01
thirty-two papers chosen by |
| By: | Vikranth Lokeshwar Dhandapani; Shashi Jain |
| Abstract: | In this paper, we present an artificial neural network framework for portfolio compression of a large portfolio of European options with varying maturities (target portfolio) by a significantly smaller portfolio of European options with shorter or same maturity (compressed portfolio), which also represents a self-replicating static hedge portfolio of the target portfolio. For the proposed machine learning architecture, which is consummately interpretable by choice of design, we also define the algorithm to learn model parameters by providing a parameter initialisation technique and leveraging the optimisation methodology proposed in Lokeshwar and Jain (2024), which was initially introduced to price Bermudan options. We demonstrate the convergence of errors and the iterative evolution of neural network parameters over the course of optimization process, using selected target portfolio samples for illustration. We demonstrate through numerical examples that the Exposure distributions and Exposure profiles (Expected Exposure and Potential Future Exposure) of the target portfolio and compressed portfolio align closely across future risk horizons under risk-neutral and real-world scenarios. Additionally, we benchmark the target portfolio's Financial Greeks (Delta, Gamma, and Vega) against the compressed portfolio at future time horizons across different market scenarios generated by Monte-Carlo simulations. Finally, we compare the regulatory capital requirement under the standardised approach for counterparty credit risk of the target portfolio against the compressed portfolio and highlight that the capital requirement for the compact portfolio substantially reduces. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17941&r=cmp |
| By: | Victor Chernozhukov; Christian Hansen; Nathan Kallus; Martin Spindler; Vasilis Syrgkanis |
| Abstract: | An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.02467&r=cmp |
| By: | Ruoyu Sun (Xi'an Jiaotong-Liverpool University, School of Mathematics and Physics, Department of Financial and Actuarial Mathematics); Angelos Stefanidis (Xi'an Jiaotong-Liverpool University Entrepreneur College); Zhengyong Jiang (Xi'an Jiaotong-Liverpool University Entrepreneur College); Jionglong Su (Xi'an Jiaotong-Liverpool University Entrepreneur College) |
| Abstract: | As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. In recent years, DRL algorithms have been widely applied by scholars for portfolio optimization in consecutive trading periods, since the DRL agent can dynamically adapt to market changes and does not rely on the specification of the joint dynamics across the assets. However, typical DRL agents for portfolio optimization cannot learn a policy that is aware of the dynamic correlation between portfolio asset returns. Since the dynamic correlations among portfolio assets are crucial in optimizing the portfolio, the lack of such knowledge makes it difficult for the DRL agent to maximize the return per unit of risk, especially when the target market permits short selling (i.e., the US stock market). In this research, we propose a hybrid portfolio optimization model combining the DRL agent and the Black-Litterman (BL) model to enable the DRL agent to learn the dynamic correlation between the portfolio asset returns and implement an efficacious long/short strategy based on the correlation. Essentially, the DRL agent is trained to learn the policy to apply the BL model to determine the target portfolio weights. To test our DRL agent, we construct the portfolio based on all the Dow Jones Industrial Average constitute stocks. Empirical results of the experiments conducted on real-world United States stock market data demonstrate that our DRL agent significantly outperforms various comparison portfolio choice strategies and alternative DRL frameworks by at least 42% in terms of accumulated return. In terms of the return per unit of risk, our DRL agent significantly outperforms various comparative portfolio choice strategies and alternative strategies based on other machine learning frameworks. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.16609&r=cmp |
| By: | Vikranth Lokeshwar Dhandapani; Shashi Jain |
| Abstract: | This paper presents a Monte-Carlo-based artificial neural network framework for pricing Bermudan options, offering several notable advantages. These advantages encompass the efficient static hedging of the target Bermudan option and the effective generation of exposure profiles for risk management. We also introduce a novel optimisation algorithm designed to expedite the convergence of the neural network framework proposed by Lokeshwar et al. (2022) supported by a comprehensive error convergence analysis. We conduct an extensive comparative analysis of the Present Value (PV) distribution under Markovian and no-arbitrage assumptions. We compare the proposed neural network model in conjunction with the approach initially introduced by Longstaff and Schwartz (2001) and benchmark it against the COS model, the pricing model pioneered by Fang and Oosterlee (2009), across all Bermudan exercise time points. Additionally, we evaluate exposure profiles, including Expected Exposure and Potential Future Exposure, generated by our proposed model and the Longstaff-Schwartz model, comparing them against the COS model. We also derive exposure profiles at finer non-standard grid points or risk horizons using the proposed approach, juxtaposed with the Longstaff Schwartz method with linear interpolation and benchmark against the COS method. In addition, we explore the effectiveness of various interpolation schemes within the context of the Longstaff-Schwartz method for generating exposures at finer grid horizons. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.15936&r=cmp |
| By: | Ziyuan Ma; Conor Ryan; Jim Buckley; Muslim Chochlov |
| Abstract: | Sentiment analysis can be used for stock market prediction. However, existing research has not studied the impact of a user's financial background on sentiment-based forecasting of the stock market using artificial neural networks. In this work, a novel combination of neural networks is used for the assessment of sentiment-based stock market prediction, based on the financial background of the population that generated the sentiment. The state-of-the-art language processing model Bidirectional Encoder Representations from Transformers (BERT) is used to classify the sentiment and a Long-Short Term Memory (LSTM) model is used for time-series based stock market prediction. For evaluation, the Weibo social networking platform is used as a sentiment data collection source. Weibo users (and their comments respectively) are divided into Authorized Financial Advisor (AFA) and Unauthorized Financial Advisor (UFA) groups according to their background information, as collected by Weibo. The Hong Kong Hang Seng index is used to extract historical stock market change data. The results indicate that stock market prediction learned from the AFA group users is 39.67% more precise than that learned from the UFA group users and shows the highest accuracy (87%) when compared to existing approaches. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.00772&r=cmp |
| By: | Pengfei Zhao; Haoren Zhu; Wilfred Siu Hung NG; Dik Lun Lee |
| Abstract: | Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities such as risk management. The Econometrics and Machine Learning communities have developed two distinct approaches for financial volatility forecasting: the stochastic approach and the neural network (NN) approach. Despite their individual strengths, these methodologies have conventionally evolved in separate research trajectories with little interaction between them. This study endeavors to bridge this gap by establishing an equivalence relationship between models of the GARCH family and their corresponding NN counterparts. With the equivalence relationship established, we introduce an innovative approach, named GARCH-NN, for constructing NN-based volatility models. It obtains the NN counterparts of GARCH models and integrates them as components into an established NN architecture, thereby seamlessly infusing volatility stylized facts (SFs) inherent in the GARCH models into the neural network. We develop the GARCH-LSTM model to showcase the power of the GARCH-NN approach. Experiment results validate that amalgamating the NN counterparts of the GARCH family models into established NN models leads to enhanced outcomes compared to employing the stochastic and NN models in isolation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06642&r=cmp |
| By: | Hanshuang Tong; Jun Li; Ning Wu; Ming Gong; Dongmei Zhang; Qi Zhang |
| Abstract: | Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. There are two main challenges for typical deep learning-based methods for quantitative finance. First, they struggle to fuse textual and numerical information flexibly for stock movement prediction. Second, traditional methods lack clarity and interpretability, which impedes their application in scenarios where the justification for predictions is essential. To solve the above challenges, we propose Ploutos, a novel financial LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen contains multiple primary experts that can analyze different modal data, such as text and numbers, and provide quantitative strategies from different perspectives. Then PloutosGPT combines their insights and predictions and generates interpretable rationales. To generate accurate and faithful rationales, the training strategy of PloutosGPT leverage rearview-mirror prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token weighting mechanism to finetune LLM by increasing key tokens weight. Extensive experiments show our framework outperforms the state-of-the-art methods on both prediction accuracy and interpretability. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.00782&r=cmp |
| By: | Wentao Zhang; Lingxuan Zhao; Haochong Xia; Shuo Sun; Jiaze Sun; Molei Qin; Xinyi Li; Yuqing Zhao; Yilei Zhao; Xinyu Cai; Longtao Zheng; Xinrun Wang; Bo An |
| Abstract: | Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 9 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.18485&r=cmp |
| By: | Daniele Ballinari |
| Abstract: | Machine learning methods, particularly the double machine learning (DML) estimator (Chernozhukov et al., 2018), are increasingly popular for the estimation of the average treatment effect (ATE). However, datasets often exhibit unbalanced treatment assignments where only a few observations are treated, leading to unstable propensity score estimations. We propose a simple extension of the DML estimator which undersamples data for propensity score modeling and calibrates scores to match the original distribution. The paper provides theoretical results showing that the estimator retains the DML estimator's asymptotic properties. A simulation study illustrates the finite sample performance of the estimator. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.01585&r=cmp |
| By: | Qishuo Cheng; Le Yang; Jiajian Zheng; Miao Tian; Duan Xin |
| Abstract: | Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading expert is trained by a model, and the trading decision is solely based on the prediction results of the model. Secondly, the data source used by the model is relatively simple, and only considers the data of the stock itself, ignoring the impact of the whole market risk on the stock. In this paper, the DQN algorithm is introduced into asset management portfolios in a novel and straightforward way, and the performance greatly exceeds the benchmark, which fully proves the effectiveness of the DRL algorithm in portfolio management. This also inspires us to consider the complexity of financial problems, and the use of algorithms should be fully combined with the problems to adapt. Finally, in this paper, the strategy is implemented by selecting the assets and actions with the largest Q value. Since different assets are trained separately as environments, there may be a phenomenon of Q value drift among different assets (different assets have different Q value distribution areas), which may easily lead to incorrect asset selection. Consider adding constraints so that the Q values of different assets share a Q value distribution to improve results. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.15994&r=cmp |
| By: | Sylvain Barthélémy; Virginie Gautier; Fabien Rondeau (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Currency crises, recurrent events in the economic history of developing, emerging, and developed countries, have disastrous economic consequences. This paper proposes an early warning system for currency crises using sophisticated recurrent neural networks, such as long short‐term memory (LSTM) and gated recurrent unit (GRU). These models were initially used in language processing, where they performed well. Such models are increasingly being used in forecasting financial asset prices, including exchange rates, but they have not yet been applied to the prediction of currency crises. As for all recurrent neural networks, they allow for taking into account nonlinear interactions between variables and the influence of past data in a dynamic form. For a set of 68 countries including developed, emerging, and developing economies over the period of 1995–2020, LSTM and GRU outperformed our benchmark models. LSTM and GRU correctly sent continuous signals within a 2‐year warning window to alert for 91% of the crises. For the LSTM, false signals represent only 14% of the emitted signals compared with 23% for logistic regression, making it an efficient early warning system for policymakers. |
| Keywords: | currency crises, early warning system, gated recurrent unit, long short-term memory, neural network |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04470367&r=cmp |
| By: | Bryan Kelly; Boris Kuznetsov; Semyon Malamud; Teng Andrea Xu |
| Abstract: | We open up the black box behind Deep Learning for portfolio optimization and prove that a sufficiently wide and arbitrarily deep neural network (DNN) trained to maximize the Sharpe ratio of the Stochastic Discount Factor (SDF) is equivalent to a large factor model (LFM): A linear factor pricing model that uses many non-linear characteristics. The nature of these characteristics depends on the architecture of the DNN in an explicit, tractable fashion. This makes it possible to derive end-to-end trained DNN-based SDFs in closed form for the first time. We evaluate LFMs empirically and show how various architectural choices impact SDF performance. We document the virtue of depth complexity: With enough data, the out-of-sample performance of DNN-SDF is increasing in the NN depth, saturating at huge depths of around 100 hidden layers. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06635&r=cmp |
| By: | Pierre Brugiere; Gabriel Turinici |
| Abstract: | The transformer models have been extensively used with good results in a wide area of machine learning applications including Large Language Models and image generation. Here, we inquire on the applicability of this approach to financial time series. We first describe the dataset construction for two prototypical situations: a mean reverting synthetic Ornstein-Uhlenbeck process on one hand and real S&P500 data on the other hand. Then, we present in detail the proposed Transformer architecture and finally we discuss some encouraging results. For the synthetic data we predict rather accurately the next move, and for the S&P500 we get some interesting results related to quadratic variation and volatility prediction. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.02523&r=cmp |
| By: | Andrei Iakovlev; Annie Liang |
| Abstract: | Evaluations once solely within the domain of human experts (e.g., medical diagnosis by doctors) can now also be carried out by machine learning algorithms. This raises a new conceptual question: what is the difference between being evaluated by humans and algorithms, and when should an individual prefer one form of evaluation over the other? We propose a theoretical framework that formalizes one key distinction between the two forms of evaluation: Machine learning algorithms are standardized, fixing a common set of covariates by which to assess all individuals, while human evaluators customize which covariates are acquired to each individual. Our framework defines and analyzes the advantage of this customization -- the value of context -- in environments with very high-dimensional data. We show that unless the agent has precise knowledge about the joint distribution of covariates, the value of more covariates exceeds the value of context. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.11157&r=cmp |
| By: | Richard Schnorrenberger; Aishameriane Schmidt; Guilherme Valle Moura |
| Abstract: | We investigate the predictive ability of machine learning methods to produce weekly inflation nowcasts using high-frequency macro-financial indicators and a survey of professional forecasters. Within an unrestricted mixed-frequency ML framework, we provide clear guidelines to improve inflation nowcasts upon forecasts made by specialists. First, we find that variable selection performed via the LASSO is fundamental for crafting an effective ML model for inflation nowcasting. Second, we underscore the relevance of timely data on price indicators and SPF expectations to better discipline our model-based nowcasts, especially during the inflationary surge following the COVID-19 crisis. Third, we show that predictive accuracy substantially increases when the model specification is free of ragged edges and guided by the real-time data release of price indicators. Finally, incorporating the most recent high-frequency signal is already sufficient for real-time updates of the nowcast, eliminating the need to account for lagged high-frequency information. |
| Keywords: | inflation nowcasting; machine learning; mixed-frequency data; survey of professional forecasters; |
| JEL: | E31 E37 C53 C55 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:806&r=cmp |
| By: | Adele Ravagnani; Fabrizio Lillo; Paola Deriu; Piero Mazzarisi; Francesca Medda; Antonio Russo |
| Abstract: | Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveillance aimed at identifying potential insider trading activities. This method lies in the reconstruction-based paradigm and employs principal component analysis and autoencoders as dimensionality reduction techniques. The only input of this method is the trading position of each investor active on the asset for which we have a price sensitive event (PSE). After determining reconstruction errors related to the trading profiles, several conditions are imposed in order to identify investors whose behavior could be suspicious of insider trading related to the PSE. As a case study, we apply our method to investor resolved data of Italian stocks around takeover bids. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.00707&r=cmp |
| By: | Giraldo, Carlos (Latin American Reserve Fund); Giraldo, Iader (Latin American Reserve Fund); Gomez-Gonzalez, Jose E. (City University of New York – Lehman College); Uribe, Jorge M. (Universitat Oberta de Catalunya) |
| Abstract: | This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95%, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability. |
| Keywords: | Credit growth; Machine learning methodology; Excessive credit creation; Financial stability |
| JEL: | C45 E44 G21 |
| Date: | 2024–03–10 |
| URL: | http://d.repec.org/n?u=RePEc:col:000566:021077&r=cmp |
| By: | Jiajian Zheng; Duan Xin; Qishuo Cheng; Miao Tian; Le Yang |
| Abstract: | The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure, which can interfere with the macro level development of the national economy. The prediction of stock price trends is a popular research topic in academia. Predicting the three trends of stock pricesrising, sideways, and falling can assist investors in making informed decisions about buying, holding, or selling stocks. Establishing an effective forecasting model for predicting these trends is of substantial practical importance. This paper evaluates the predictive performance of random forest models combined with artificial intelligence on a test set of four stocks using optimal parameters. The evaluation considers both predictive accuracy and time efficiency. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17194&r=cmp |
| By: | John D. Huber; Laura Mayoral |
| Abstract: | We develop a novel methodology that uses machine learning to produce accurate estimates of consumption per capita and poverty in 10x10km cells in sub-Saharan Africa over time. Using the new data, we revisit two prominent papers that examine the effect of institutions on economic development, both of which use “nightlights” as a proxy for development. The conclusions from these papers are reversed when we substitute the new consumption data for nightlights. We argue that the different conclusions about institutions are due to a previously unrecognized problem that is endemic when nightlights are used as a proxy for spatial economic well-being: nightlights suffer from nonclassical measurement error. This error will typically lead to biased estimates in standard statistical models that use nightlights as a spatially disaggregated measure of economic development. The bias can be either positive or negative, and it can appear when nightlights are used as either a dependent or an independent variable. Our research therefore underscores an important limitation in the use of nightlights, which has become the standard measure of spatial economic well-being for studies focusing on developing parts of the world. It also demonstrates how machine learning models can generate a useful alternative to nightlights, with important implications for the conclusions we draw from the analyses in which such data are employed. |
| Keywords: | economic develpment, poverty, institutions, nightlights, nonclassical measurement error, machine learning |
| JEL: | C01 P46 P48 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1433&r=cmp |
| By: | Antonis Papapantoleon; Jasper Rou |
| Abstract: | We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness, and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed method is assessed in a series of numerical examples, with particular focus in the lifted Heston model. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.00746&r=cmp |
| By: | Zhuangwei Shi |
| Abstract: | The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to their robust nonlinear generalization capabilities. Recently, Mamba, a structured state space sequence model with a selection mechanism and scan module (S6), has emerged as a powerful tool in sequence modeling tasks. Leveraging this framework, this paper proposes a novel Mamba-based model for stock price prediction, named MambaStock. The proposed MambaStock model effectively mines historical stock market data to predict future stock prices without handcrafted features or extensive preprocessing procedures. Empirical studies on several stocks indicate that the MambaStock model outperforms previous methods, delivering highly accurate predictions. This enhanced accuracy can assist investors and institutions in making informed decisions, aiming to maximize returns while minimizing risks. This work underscores the value of Mamba in time-series forecasting. Source code is available at https://github.com/zshicode/MambaStock. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.18959&r=cmp |
| By: | Mourelatos, Evangelos; Zervas, Panagiotis; Lagios, Dimitris; Tzimas, Giannis |
| Abstract: | This paper employs an online real-effort experiment to investigate gender disparities in the selection of individuals into competitive working environments when assisted by artificial intelligence (AI). In contrast to previous research suggesting greater competitiveness among men, our findings reveal that both genders are equally likely to compete in the presence of AI assistance. Surprisingly, the introduction of AI eliminates an 11-percentage-point gender gap, between men and women in our competitive scenario. We also discuss how the gender gap in tournament entry appears to be contingent on ChatGPT selection rather than being omnipresent. Notably, 47% of female participants independently chose to utilize ChatGPT, while 55% of males did the same. However, when ChatGPT was offered by the experimenter-employer, more than 53% of female participants opted for AI assistance, compared to 57% of males, in a gender-neutral online task. This shift prompts a reevaluation of gender gap trends in competition entry rates, particularly as women increasingly embrace generative AI tools, resulting in a boost in their confidence. We rule out differences in risk aversion. The discussion suggests that these behavioral patterns may have significant policy implications, as the introduction of generative AI tools in the workplace can be leveraged to rectify gender disparities. |
| Keywords: | Gender differences, ChatGPT, Competition, Economic experiments |
| JEL: | C90 J16 J71 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1404&r=cmp |
| By: | ZHANG Meilian; YIN Ting; USUI Emiko; OSHIO Takashi; ZHANG Yi |
| Abstract: | Overemployment and underemployment being widely existing phenomena, much less is known about their determinants for older workers. We innovatively employ machine learning methods to determine the important factors driving overemployment and underemployment among older workers in Japan. Those with better economic conditions, worse health, less family support, and unfavorable job characteristics are more likely to report overemployment, whereas increasing age, less disposable income, shorter current work hours, holding a job with a temporary nature, and low job and pay satisfaction are predictive to underemployment. Cluster analysis further shows that reasons for having work hour mismatches can be highly heterogeneous within both overemployed and underemployed groups. Subgroup analyses suggest room for pro-work policies among 65+ workers facing financial stress and lacking family support, female workers with unstable jobs and low spousal income, and salaried workers working insufficient hours. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:24034&r=cmp |
| By: | Deepeka Garg; Benjamin Patrick Evans; Leo Ardon; Annapoorani Lakshmi Narayanan; Jared Vann; Udari Madhushani; Makada Henry-Nickie; Sumitra Ganesh |
| Abstract: | Mortgages account for the largest portion of household debt in the United States, totaling around \$12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage servicing industry plays a vital role in offering this assistance, yet there has been limited research modelling the complex relationship between households and servicers. To bridge this gap, we developed an agent-based model that explores household behavior and the effectiveness of relief measures during financial distress. Our model represents households as adaptive learning agents with realistic financial attributes. These households experience exogenous income shocks, which may influence their ability to make mortgage payments. Mortgage servicers provide relief options to these households, who then choose the most suitable relief based on their unique financial circumstances and individual preferences. We analyze the impact of various external shocks and the success of different mortgage relief strategies on specific borrower subgroups. Through this analysis, we show that our model can not only replicate real-world mortgage studies but also act as a tool for conducting a broad range of what-if scenario analyses. Our approach offers fine-grained insights that can inform the development of more effective and inclusive mortgage relief solutions. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17932&r=cmp |
| By: | Reinking, Ernst; Becker, Marco |
| Abstract: | Retrieval-Augmented Generation (RAG) synergistically combines the intrinsic knowledge of LLMs with the huge, dynamic databases of companies. Referencing the basic concept of a RAG ("Naive RAG"), this working paper identifies the critical factors of this cutting-edge architecture and gives hints for improvement. Finally, future paths for research and development are outlined. |
| Abstract: | Retrieval-Augmented Generation (RAG) verbindet auf synergetische Weise das intrinsische Wissen von LLMs mit den riesigen, dynamischen Datenbeständen von Unternehmen. Aufbauend auf dem Grundkonzept einer RAG („Naive RAG“) identifiziert dieses Working Paper kritische Faktoren dieser hochaktuellen Architektur und gibt Hinweise zur Verbesserung. Abschließend werden zukünftige Wege für Forschung und Entwicklung aufgezeigt. |
| Keywords: | AI, RAG, artificial intelligence, Retrieval-Augmented Generation |
| JEL: | M15 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:285307&r=cmp |
| By: | Lijia Ma; Xingchen Xu; Yong Tan |
| Abstract: | In the domain of digital information dissemination, search engines act as pivotal conduits linking information seekers with providers. The advent of chat-based search engines utilizing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), exemplified by Bing Chat, marks an evolutionary leap in the search ecosystem. They demonstrate metacognitive abilities in interpreting web information and crafting responses with human-like understanding and creativity. Nonetheless, the intricate nature of LLMs renders their "cognitive" processes opaque, challenging even their designers' understanding. This research aims to dissect the mechanisms through which an LLM-powered chat-based search engine, specifically Bing Chat, selects information sources for its responses. To this end, an extensive dataset has been compiled through engagements with New Bing, documenting the websites it cites alongside those listed by the conventional search engine. Employing natural language processing (NLP) techniques, the research reveals that Bing Chat exhibits a preference for content that is not only readable and formally structured, but also demonstrates lower perplexity levels, indicating a unique inclination towards text that is predictable by the underlying LLM. Further enriching our analysis, we procure an additional dataset through interactions with the GPT-4 based knowledge retrieval API, unveiling a congruent text preference between the RAG API and Bing Chat. This consensus suggests that these text preferences intrinsically emerge from the underlying language models, rather than being explicitly crafted by Bing Chat's developers. Moreover, our investigation documents a greater similarity among websites cited by RAG technologies compared to those ranked highest by conventional search engines. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.19421&r=cmp |
| By: | Alexander Cuntz; Carsten Fink; Hansueli Stamm |
| Abstract: | The emergence of Artificial Intelligence (AI) has profound implications for intellectual property (IP) frameworks. While much of the discussion so far has focused on the legal implications, we focus on the economic dimension. We dissect AI's role as both a facilitator and disruptor of innovation and creativity. Recalling economic principles and reviewing relevant literature, we explore the evolving landscape of AI innovation incentives and the challenges it poses to existing IP frameworks. From patentability dilemmas to copyright conundrums, we find that there is a delicate balance between fostering innovation and safeguarding societal interests amidst rapid technological progress. We also point to areas where future economic research could offer valuable insights to policymakers. |
| Keywords: | Artificial Intelligence, Intellectual Property, Patents, Copyright |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:wip:wpaper:77&r=cmp |
| By: | Emiliano Alvarez (Universidad de la República); Volker Grimm (Helmholtz Centre for Environmental Research-UFZ)) |
| Abstract: | Agent-based modeling (ABM) is a modeling tool that has increased its use in different sciences as well as in economics. Among othe reasons, this is due to the extension of the complex systems paradigm indifferent sciences and the increase in multidisciplinary work. This phenomenon manifests itself in the social sciences from the realiation that social organizations are interactive systems of multiple agents, with feedback, reflexivity, and non-linear effects on the rest of the system. The way in which information is structured is conditional on the paradigms applied and the problem addressed. Since economies are assumed to becomplex adaptive systems, theories and their representations must be consistent with this principle. Therefore, their modeling must allow for a faithful representation of the problem under analysis while being clearand allowing for analysis and subsequent replication. In this paper, we demonstrate how the ODD (Overview, Design concepts, and Details) Protocol fosters transparency and coherence for economic ABMs. To do so, three published ABMs from economics are taken, analyzing the structure and content of their descriptions, and rewritten according to ODD. It discusses in particular the added value of using ODD and how all this could help to overcome different obstacles to a wider use of Complex Systems and ODD in Economics identified in the preceding literature. |
| Keywords: | DD protocol, Economics, agent-based model, macroeconomics, Schelling model |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:aoz:wpaper:307&r=cmp |
| By: | Mi Zhou; Vibhanshu Abhishek; Timothy Derdenger; Jaymo Kim; Kannan Srinivasan |
| Abstract: | This study analyzed images generated by three popular generative artificial intelligence (AI) tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators. Our analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and (2) subtle biases in facial expressions and appearances. Firstly, we found that all three AI generators exhibited bias against women and African Americans. Moreover, we found that the evident gender and racial biases uncovered in our analysis were even more pronounced than the status quo when compared to labor force statistics or Google images, intensifying the harmful biases we are actively striving to rectify in our society. Secondly, our study uncovered more nuanced prejudices in the portrayal of emotions and appearances. For example, women were depicted as younger with more smiles and happiness, while men were depicted as older with more neutral expressions and anger, posing a risk that generative AI models may unintentionally depict women as more submissive and less competent than men. Such nuanced biases, by their less overt nature, might be more problematic as they can permeate perceptions unconsciously and may be more difficult to rectify. Although the extent of bias varied depending on the model, the direction of bias remained consistent in both commercial and open-source AI generators. As these tools become commonplace, our study highlights the urgency to identify and mitigate various biases in generative AI, reinforcing the commitment to ensuring that AI technologies benefit all of humanity for a more inclusive future. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.02726&r=cmp |
| By: | Nozomu Kobayashi; Yoshiyuki Suimon; Koichi Miyamoto |
| Abstract: | Finance, especially option pricing, is a promising industrial field that might benefit from quantum computing. While quantum algorithms for option pricing have been proposed, it is desired to devise more efficient implementations of costly operations in the algorithms, one of which is preparing a quantum state that encodes a probability distribution of the underlying asset price. In particular, in pricing a path-dependent option, we need to generate a state encoding a joint distribution of the underlying asset price at multiple time points, which is more demanding. To address these issues, we propose a novel approach using Matrix Product State (MPS) as a generative model for time series generation. To validate our approach, taking the Heston model as a target, we conduct numerical experiments to generate time series in the model. Our findings demonstrate the capability of the MPS model to generate paths in the Heston model, highlighting its potential for path-dependent option pricing on quantum computers. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17148&r=cmp |
| By: | S. Alex Yang; Angela Huyue Zhang |
| Abstract: | The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (use data for AI training without compensating the creator) benefits all parties when abundant training data exists, it can hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of the global regulatory environment. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17801&r=cmp |
| By: | Konark Jain; Nick Firoozye; Jonathan Kochems; Philip Treleaven |
| Abstract: | Limit Order Books (LOBs) serve as a mechanism for buyers and sellers to interact with each other in the financial markets. Modelling and simulating LOBs is quite often necessary} for calibrating and fine-tuning the automated trading strategies developed in algorithmic trading research. The recent AI revolution and availability of faster and cheaper compute power has enabled the modelling and simulations to grow richer and even use modern AI techniques. In this review we \highlight{examine} the various kinds of LOB simulation models present in the current state of the art. We provide a classification of the models on the basis of their methodology and provide an aggregate view of the popular stylized facts used in the literature to test the models. We additionally provide a focused study of price impact's presence in the models since it is one of the more crucial phenomena to model in algorithmic trading. Finally, we conduct a comparative analysis of various qualities of fits of these models and how they perform when tested against empirical data. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17359&r=cmp |