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on Big Data |
| By: | Saber Talazadeh; Dragan Perakovic |
| Abstract: | Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach to stock market prediction by integrating sentiment analysis using FinGPT generative AI model with the traditional Random Forest model. The proposed technique aims to optimize the accuracy of stock price forecasts by leveraging the nuanced understanding of financial sentiments provided by FinGPT. We present a new methodology called "Sentiment-Augmented Random Forest" (SARF), which in-corporates sentiment features into the Random Forest framework. Our experiments demonstrate that SARF outperforms conventional Random Forest and LSTM models with an average accuracy improvement of 9.23% and lower prediction errors in pre-dicting stock market movements. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07143 |
| By: | Arya Chakraborty; Auhona Basu |
| Abstract: | The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual data. This paper proposes a novel Two-Level Conv-LSTM Neural Network integrated with a Large Language Model (LLM) for comprehensive stock advising. The model harnesses the strengths of Conv-LSTM for analyzing time-series data and LLM for processing and understanding textual information from financial news, social media, and reports. In the first level, convolutional layers are employed to identify local patterns in historical stock prices and technical indicators, followed by LSTM layers to capture the temporal dynamics. The second level integrates the output with an LLM that analyzes sentiment and contextual information from textual data, providing a holistic view of market conditions. The combined approach aims to improve prediction accuracy and provide contextually rich stock advising. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.12807 |
| By: | Caterina Giannetti; Maria Saveria Mavillonio |
| Abstract: | Using a unique dataset of equity offerings from crowdfunding platforms, we explore the synergy between human insights and algorithmic analysis in evaluating campaign success through business plan assessments. Human evaluators (students) used a predefined grid to assess each proposal in a Business Plan competition. We then developed a classifier with advanced textual representations and compared prediction errors between human evaluators, a machine learning model, and their combination. Our goal is to identify the drivers of discrepancies in their evaluations. While AI models outperform humans in overall accuracy, human evaluations offer valuable insights, especially in areas requiring subtle judgment. Combining human and AI predictions leads to improved performance, highlighting the complementary strengths of human intuition and AI's computational power. |
| Keywords: | Crowdfunding, Natural Language Processing, Human Evaluation |
| JEL: | C45 C53 G2 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:pie:dsedps:2024/315 |
| By: | Yi Zheng; Zehao Li; Peng Jiang; Yijie Peng |
| Abstract: | We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and the parameter within the Poisson distribution as a function of price introduce complexity into analyzing the problem property. We demonstrate the concavity of the single-period profit function with respect to product price and inventory within their respective domains. The demand model is enhanced by integrating a decision tree-based machine learning approach, trained on comprehensive market data. Employing a two-timescale stochastic approximation scheme, we address the discrepancies in decision frequencies between pricing and replenishment, ensuring convergence to local optimum. We further refine our methodology by incorporating deep reinforcement learning (DRL) techniques and propose a fast-slow dual-agent DRL algorithm. In this approach, two agents handle pricing and inventory and are updated on different scales. Numerical results from both single and multiple products scenarios validate the effectiveness of our methods. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21109 |
| By: | Sotiris Tsolacos; Tatiana Franus |
| Abstract: | In this paper we study the performance of a range of methodologies to forecast real estate prices. We compare the forecast accuracy of econometric and time series models to machine learning algorithms. The target series is the yield impact a metric which is based on changes in yields (cap rates) and a prime determinant of capital value or price changes (appreciation returns). We focus on the main sectors – offices, retail and industrials – in the UK and we perform the analysis with monthly data taken from MSCI. Using monthly MSCI data results in a sample that begins in 1987. The econometric and time series models include ARMA, ARMAX, stepwise and Lasso regressions. Machine learning methods include random forest, XGBoost, and support vector machines. We use a large set of economic, financial and survey data to predict movements in yield impact. We assess the forecast performance of the selected methodologies over different time horizons, one, three, six, and twelve months. The forecast evaluation follows conventional forecast evaluation metrics. This includes basic measures such as the mean error, mean absolute error and RMSE and more sophisticated measures such Diebold-Mariano tests. We are particularly interested in forecasting gains arising from the combination of forecasts from different methods.The results have significant practical value. The forecast assessment can pick up directional changes and be used for price discovery. Real estate data in the private market are produced with a lag (even monthly data) and early information about changes in prices are valuable to real estate investors and lenders. The study aims to identify the methods or the combination of methods with the best predictive ability and focus investor attention to these methods. |
| Keywords: | econometric models; forecasting assessment; Machine Learning; Property pricing |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-251 |
| By: | Jesús Fernández-Villaverde; Galo Nuño; Jesse Perla |
| Abstract: | We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents' decisions and the aggregate consistency conditions required by equilibrium. Following this, we introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a survey of neural network applications in quantitative economics and offer reasons for cautious optimism. |
| JEL: | C61 C63 E27 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33117 |
| By: | Kei Nakagawa; Masanori Hirano; Yugo Fujimoto |
| Abstract: | This study aims to evaluate the sentiment of financial texts using large language models~(LLMs) and to empirically determine whether LLMs exhibit company-specific biases in sentiment analysis. Specifically, we examine the impact of general knowledge about firms on the sentiment measurement of texts by LLMs. Firstly, we compare the sentiment scores of financial texts by LLMs when the company name is explicitly included in the prompt versus when it is not. We define and quantify company-specific bias as the difference between these scores. Next, we construct an economic model to theoretically evaluate the impact of sentiment bias on investor behavior. This model helps us understand how biased LLM investments, when widespread, can distort stock prices. This implies the potential impact on stock prices if investments driven by biased LLMs become dominant in the future. Finally, we conduct an empirical analysis using Japanese financial text data to examine the relationship between firm-specific sentiment bias, corporate characteristics, and stock performance. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00420 |
| By: | Miguel Camacho-Ruiz; Ram\'on Alberto Carrasco; Gema Fern\'andez-Avil\'es; Antonio LaTorre |
| Abstract: | Identifying client needs to provide optimal services is crucial in tourist destination management. The events held in tourist destinations may help to meet those needs and thus contribute to tourist satisfaction. As with product management, the creation of hierarchical catalogs to classify those events can aid event management. The events that can be found on the internet are listed in dispersed, heterogeneous sources, which makes direct classification a difficult, time-consuming task. The main aim of this work is to create a novel process for automatically classifying an eclectic variety of tourist events using a hierarchical taxonomy, which can be applied to support tourist destination management. Leveraging data science methods such as CRISP-DM, supervised machine learning, and natural language processing techniques, the automatic classification process proposed here allows the creation of a normalized catalog across very different geographical regions. Therefore, we can build catalogs with consistent filters, allowing users to find events regardless of the event categories assigned at source, if any. This is very valuable for companies that offer this kind of information across multiple regions, such as airlines, travel agencies or hotel chains. Ultimately, this tool has the potential to revolutionize the way companies and end users interact with tourist events information. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.19741 |
| By: | Midha, Joshua |
| Abstract: | This paper introduces a novel transaction-function model for valuing emerging markets, integrating machine learning, agent-based modeling, and multi-method valuation techniques. Traditional valuation models often rely on aggregated economic indicators such as GDP growth and inflation, which lack the granularity needed to capture the transactional dynamics and unique risk factors inherent to emerging markets. In contrast, the proposed model treats each market as a multi-dimensional function of individual transactions, analyzing these interactions through a multi-method framework that includes Discounted Cash Flow (DCF), comparables, precedent transaction, and multiples analysis. By incorporating machine learning algorithms, the model iteratively improves predictive accuracy, dynamically adjusting to new data in volatile and data-sparse environments. Additionally, agent-based simulations provide insights into behavioral responses to policy changes, regulatory shifts, and other market-specific conditions, offering a behavioral layer often missing from traditional approaches. Validation of this model demonstrates a marked improvement in predictive accuracy and adaptability compared to conventional models. This transaction-function approach provides investors and policymakers with a granular, data-driven tool for assessing the true growth potential of emerging markets, paving the way for more informed, context-sensitive investment decisions. |
| Date: | 2024–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:d8jkt |
| By: | Mahdi Goldani |
| Abstract: | The GCC region includes Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, and Oman, which are of critical geopolitical and economic importance, being rich in oil and positioned along vital maritime routes. However, the region faces complex security challenges, ranging from traditional threats like interstate conflicts to nontraditional risks such as cyber-attacks, piracy, and environmental concerns. This study investigates the safety and security index for six GCC countries using machine learning techniques, specifically XGBoost, to forecast security trends for the next five years. Data from the Global Peace Index and World Bank development indicators were employed to construct the model. Key indicators related to economic, political, and environmental factors were selected using the Edit Distance on Real Sequence feature selection method. The model demonstrated high accuracy, with a mean absolute percentage error of less than 10% across all countries. The results indicate that Bahrain and Saudi Arabia are likely to experience improvements in their safety and security indexes. At the same time, Kuwait and Oman may face challenges in maintaining their current levels of security. The findings suggest that economic diversification, environmental sustainability, and social stability are critical for ensuring long-term security in the region. This study provides valuable insights for policymakers in designing proactive strategies to address emerging security threats. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21511 |
| By: | Krantz, Sebastian |
| Abstract: | Using rich geospatial data and causal machine learning (ML), this paper maps potential economic benefits from incremental investments in all major types of public and economic infrastructure across Africa. These 'infrastructure potential maps' cover all African populated areas at a spatial resolution of 9.7km (96km2). They show that the local returns to infrastructure are highly variable and context-specific. For example 'hard infrastructure' such as paved roads and communications is more beneficial in cities, whereas 'social infrastructure' such as education, health, public services and utilities is more critical in rural areas. Market access and agglomeration effects largely govern these returns. The open Africa Infrastructure Database built for this project provides granular data in 54 economic categories/sectors. It reveals that Africa's infrastructure is concentrated in urban areas, with cities exhibiting marked heterogeneity in infrastructure, public services, and economic activities. Spatial inefficiency is common. The findings are consistent with economic literature, highlighting causal ML and explainable AI's potential to generate insights from geospatial data and assist spatial planning. |
| Keywords: | Africa, infrastructure, investment potential, geospatial big data, causal ML, explainable AI |
| JEL: | O18 R11 R40 C14 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkwp:305261 |
| By: | Mohamed Bassi |
| Abstract: | Dans un monde de plus en plus digitalisé, la collecte et le traitement de la donnée numérique provenant du web et des objets connectés s’imposent comme une activité de première importance dans les centres de recherche et autres think tanks. Avec le langage Python nous avons développé un outil de veille économique qui permet d’analyser les publications des chercheurs en économie affiliés aux institutions africaines. Cet outil met en jeu des algorithmes de Machine Learning, en particulier des techniques de Traitement du Langage Naturel. Les jeux de données mis en jeu émanent de la plateforme Research Papers in Economics, et ce à travers le web scraping. |
| Date: | 2023–02 |
| URL: | https://d.repec.org/n?u=RePEc:ocp:rpaeco:pb_11_23 |
| By: | Zhiyuan Pei; Jianqi Yan; Jin Yan; Bailing Yang; Ziyuan Li; Lin Zhang; Xin Liu; Yang Zhang |
| Abstract: | Recently, deep learning in stock prediction has become an important branch. Image-based methods show potential by capturing complex visual patterns and spatial correlations, offering advantages in interpretability over time series models. However, image-based approaches are more prone to overfitting, hindering robust predictive performance. To improve accuracy, this paper proposes a novel method, named Sequence-based Multi-scale Fusion Regression Convolutional Neural Network (SMSFR-CNN), for predicting stock price movements in the China A-share market. By utilizing CNN to learn sequential features and combining them with image features, we improve the accuracy of stock trend prediction on the A-share market stock dataset. This approach reduces the search space for image features, stabilizes, and accelerates the training process. Extensive comparative experiments on 4, 454 A-share stocks show that the model achieves a 61.15% positive predictive value and a 63.37% negative predictive value for the next 5 days, resulting in a total profit of 165.09%. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.19291 |
| By: | Dragos Gorduza; Yaxuan Kong; Xiaowen Dong; Stefan Zohren |
| Abstract: | We investigate the effectiveness of a momentum trading signal based on the coverage network of financial analysts. This signal builds on the key information-brokerage role financial sell-side analysts play in modern stock markets. The baskets of stocks covered by each analyst can be used to construct a network between firms whose edge weights represent the number of analysts jointly covering both firms. Although the link between financial analysts coverage and co-movement of firms' stock prices has been investigated in the literature, little effort has been made to systematically learn the most effective combination of signals from firms covered jointly by analysts in order to benefit from any spillover effect. To fill this gap, we build a trading strategy which leverages the analyst coverage network using a graph attention network. More specifically, our model learns to aggregate information from individual firm features and signals from neighbouring firms in a node-level forecasting task. We develop a portfolio based on those predictions which we demonstrate to exhibit an annualized returns of 29.44% and a Sharpe ratio of 4.06 substantially outperforming market baselines and existing graph machine learning based frameworks. We further investigate the performance and robustness of this strategy through extensive empirical analysis. Our paper represents one of the first attempts in using graph machine learning to extract actionable knowledge from the analyst coverage network for practical financial applications. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.20597 |
| By: | Facundo Arga\~naraz; Juan Carlos Escanciano |
| Abstract: | Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE). Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012). We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.23785 |
| By: | Maria Saveria Mavillonio |
| Abstract: | In finance, Natural Language Processing (NLP) has become both a powerful and challenging tool, as extensive unstructured documents—such as business plans, financial reports, and regulatory filings—hold essential insights for strategic decision-making. This paper reviews the progression of NLP text representation methods, from foundational models to advanced Transformer architectures that greatly enhance semantic and contextual analysis. Yet, these models encounter limitations when applied to long financial documents, where computational efficiency and contextual coherence are critical. Recent innovations, including sparse attention mechanisms and domain-specific model adaptations, have improved the processing of lengthy texts, allowing for more accurate analysis of financial documents by capturing field-specific semantics. This paper also highlights the transformative role of NLP in financial analysis, especially where structured data is limited. Selecting the most suitable model for specific tasks is essential for maximizing NLP's impact in finance. Organized to provide a thorough overview, the paper covers text representation techniques, strategies for handling long texts, and applications in finance, establishing a foundation for advancing NLP-driven data analysis in this field. |
| Keywords: | Long Text, Financial Document Representation, Natural Language Processing, Transformers |
| JEL: | C45 G2 G23 L26 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:pie:dsedps:2024/317 |
| By: | Ruyi Tao; Kaiwei Liu; Xu Jing; Jiang Zhang |
| Abstract: | Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or they rely solely on time series forecasting techniques, ignoring interpretability and the inherent mechanisms of company growth. In this paper, we propose a machine learning-based prediction framework that incorporates an econophysics model for company growth. Our model captures both the intrinsic growth mechanisms of companies led by scaling laws and the fluctuations influenced by random factors and individual decisions, demonstrating superior predictive performance compared with methods that use time series techniques alone. Its advantages are more pronounced in long-range prediction tasks. By explicitly modeling the baseline growth and volatility components, our model is more interpretable. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.17587 |
| By: | Sonnleitner, Benedikt; Stapf, Jelena; Wulff, Kai |
| Abstract: | Among the most important tasks of central banks is to ensure the availability of cash to credit institutions and retailers. Forecasting the demand for cash on a granular level is crucial in the process to keep logistics costs low, while being resilient to demand or supply shocks. Whereas to date, cash forecasts with central banks mostly comprise structural models to define banknote production for the coming years, our contribution is to combine features of macro level forecasting with more granular and short term regional forecasts methods. We show in an inventory simulation, that elaborate forecasting methods on granular level can substantially improve inventory performance for this use-case. To guide the implementation of a forecasting process at the Bundesbank, we benchmark statistical and machine learning methods on demand and supply of cash, using anonymized data on transactions of six regional branches of Deutsche Bundesbank. We use a pseudo out of sample predictive performance framework to evaluate the accuracy of our forecasts and perform an inventory cost simulation. We find that (i) DeepAR outperforms the other benchmarks substantially on all data sets. (ii) ETS, ARIMA, and DeepAR clearly outperform the naive benchmark in terms of accuracy across all data sets, and inventory performance. |
| Keywords: | Global learning, Forecasting, Machine Learning |
| JEL: | E31 G21 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:305276 |
| By: | Christoph Koenig (DEF, University of Rome "Tor Vergata") |
| Abstract: | Election forensics are a widespread tool for diagnosing electoral manipulation out of statistical anomalies in publicly available election micro-data. Yet, in spite of their popularity, they are only rarely used to measure and compare variation in election fraud at the sub-national level. The typical challenges faced by researchers are the wide range of forensic indicators to choose from, the potential variation in manipulation methods across time and space and the difficulty in creating a measure of fraud intensity that is comparable across geographic units and elections. This paper outlines a procedure to overcome these issues by making use of directly observed instances of fraud and machine learning methods. I demonstrate the performance of this procedure for the case of post-2000 Russia and discuss advantages and pitfalls. The resulting estimates of fraud intensity are closely in line with quantitative and qualitative secondary data at the cross-sectional and time-series level. |
| Keywords: | Bayesian Additive Regression Trees, Election Forensics, Election Fraud, Election Monitoring, Machine Learning, Russia |
| Date: | 2024–10–28 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:584 |
| By: | Michel F. C. Haddad; Martin Huber; Lucas Z. Zhang |
| Abstract: | We propose a difference-in-differences (DiD) method for a time-varying continuous treatment and multiple time periods. Our framework assesses the average treatment effect on the treated (ATET) when comparing two non-zero treatment doses. The identification is based on a conditional parallel trend assumption imposed on the mean potential outcome under the lower dose, given observed covariates and past treatment histories. We employ kernel-based ATET estimators for repeated cross-sections and panel data adopting the double/debiased machine learning framework to control for covariates and past treatment histories in a data-adaptive manner. We also demonstrate the asymptotic normality of our estimation approach under specific regularity conditions. In a simulation study, we find a compelling finite sample performance of undersmoothed versions of our estimators in setups with several thousand observations. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21105 |
| By: | Mahdi Goldani |
| Abstract: | Political stability is crucial for the socioeconomic development of nations, particularly in geopolitically sensitive regions such as the Gulf Cooperation Council Countries, Saudi Arabia, UAE, Kuwait, Qatar, Oman, and Bahrain. This study focuses on predicting the political stability index for these six countries using machine learning techniques. The study uses data from the World Banks comprehensive dataset, comprising 266 indicators covering economic, political, social, and environmental factors. Employing the Edit Distance on Real Sequence method for feature selection and XGBoost for model training, the study forecasts political stability trends for the next five years. The model achieves high accuracy, with mean absolute percentage error values under 10, indicating reliable predictions. The forecasts suggest that Oman, the UAE, and Qatar will experience relatively stable political conditions, while Saudi Arabia and Bahrain may continue to face negative political stability indices. The findings underscore the significance of economic factors such as GDP and foreign investment, along with variables related to military expenditure and international tourism, as key predictors of political stability. These results provide valuable insights for policymakers, enabling proactive measures to enhance governance and mitigate potential risks. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21516 |
| By: | Mukashov, Askar; Robinson, Sherman; Thurlow, James; Arndt, Channing; Thomas, Timothy S. |
| Abstract: | This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven sectoral productivity. In these and other developing countries, recent decades have been characterized by increased risks associated with all these factors, and there is a demand for instruments that can help to disentangle them. For each country, we utilize historical data to develop multi-variate distributions of shocks. We then sample from these distributions to obtain a series of shock vectors, which we label economic uncertainty scenarios. These scenarios are then entered into economywide computable general equilibrium (CGE) simulation models for the three countries, which allow us to quantify the impact of increased uncertainty on major economic indicators. Finally, we utilize importance metrics from the random forest machine learning algorithm and relative importance metrics from multiple linear regression models to quantify the importance of country-specific risk factors for country performance. We find that Malawi and Rwanda are more vulnerable to sectoral productivity shocks, and Kenya is more exposed to external risks. These findings suggest that a country’s level of development and integration into the global economy are key driving forces defining their risk profiles. The methodology of Systematic Risk Profiling can be applied to many other countries, delineating country-specific risks and vulnerabilities. |
| Keywords: | climate; computable general equilibrium models; machine learning; risk; uncertainty; Africa; Eastern Africa; Sub-Saharan Africa; Kenya; Rwanda; Malawi |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:2286 |
| By: | Rotem Zelingher |
| Abstract: | Ensuring food security is a critical global challenge, particularly for low-income countries where food prices impact the access to nutritious food. The volatility of global agricultural commodity (AC) prices exacerbates food insecurity, with international trade restrictions and market disruptions further complicating the situation. Despite online platforms for monitoring food prices, there is a significant gap in providing detailed explanations and forecasts accessible to non-specialists. To address this, we propose the Agricultural Commodity Analysis and Forecasts (AGRICAF) methodology, integrating explainable machine learning (XML) and econometric techniques to analyse and forecast global agricultural commodity prices up to one year ahead, dynamically adapting to different forecast horizons. This innovative integration allows us to model complex interactions and dynamics while providing clear, interpretable results. This paper demonstrates how AGRICAF can be used, applying it to three major agricultural commodities - maize, soybean, and wheat - and explaining how different factors impact prices across various months and forecast horizons. By facilitating access to accurate and interpretable medium-term forecasts of AC prices, AGRICAF can contribute to developing a fair and sustainable food system. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.20363 |
| By: | Yakymovych, Yaroslav (Institute for Housing and Urban Research, Uppsala University) |
| Abstract: | Sickness insurance guarantees employees the right to take leave from work when they are sick, but is vulnerable to excessive use because monitoring of recipients’ health is difficult and costly. In terms of costs, it would be preferable to focus monitoring on individuals whose sickness absence it strongly affects. This paper studies targeted monitoring in the setting of a large-scale randomised experiment where medical certificate requirements were relaxed for some workers. I employ a machine learning method, the generalised random forest, to identify heterogeneous effects on the duration of workers’ sickness absence spells. This allows me to compute treatment effect estimates based on an extensive set of worker characteristics and their potentially complex relationships with each other and with sickness absence duration. The individuals who are most sensitive to monitoring are characterised by a history of extensive sick leave uptake, low socioeconomic status, and male gender. The results suggest that a targeted policy can achieve the same reduction in monitoring costs as took place during the experiment at a 51 percent smaller loss in terms of increased sickness absence. Monitoring all insured individuals is estimated to be inefficient, but the benefits of targeted monitoring are estimated to exceed the costs. |
| Keywords: | Sickness Absence; Monitoring; Heterogeneous Effects; GRF |
| JEL: | C21 I18 J22 |
| Date: | 2024–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ifauwp:2024_019 |
| By: | Joel R. Bock |
| Abstract: | This paper describes experiments on fine-tuning a small language model to generate forecasts of long-horizon stock price movements. Inputs to the model are narrative text from 10-K reports of large market capitalization companies in the S&P 500 index; the output is a forward-looking buy or sell decision. Price direction is predicted at discrete horizons up to 12 months after the report filing date. The results reported here demonstrate good out-of-sample statistical performance (F1-macro= 0.62) at medium to long investment horizons. In particular, the buy signals generated from 10-K text are found most precise at 6 and 9 months in the future. As measured by the F1 score, the buy signal provides between 4.8 and 9 percent improvement against a random stock selection model. In contrast, sell signals generated by the models do not perform well. This may be attributed to the highly imbalanced out-of-sample data, or perhaps due to management drafting annual reports with a bias toward positive language. Cross-sectional analysis of performance by economic sector suggests that idiosyncratic reporting styles within industries are correlated with varying degrees and time scales of price movement predictability. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.18988 |
| By: | Yuan Gao; Dokyun Lee; Gordon Burtch; Sina Fazelpour |
| Abstract: | Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Nearly all advanced approaches fail to replicate human behavior distributions across many models. Causes of failure are diverse and unpredictable, relating to input language, roles, and safeguarding. These results advise caution when using LLMs to study human behavior or as surrogates or simulations. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.19599 |
| By: | Alicia Vidler; Toby Walsh |
| Abstract: | We introduce a novel hybrid approach that augments Agent-Based Models (ABMs) with behaviors generated by Large Language Models (LLMs) to simulate human trading interactions. We call our model TraderTalk. Leveraging LLMs trained on extensive human-authored text, we capture detailed and nuanced representations of bilateral conversations in financial trading. Applying this Generative Agent-Based Model (GABM) to government bond markets, we replicate trading decisions between two stylised virtual humans. Our method addresses both structural challenges, such as coordinating turn-taking between realistic LLM-based agents, and design challenges, including the interpretation of LLM outputs by the agent model. By exploring prompt design opportunistically rather than systematically, we enhance the realism of agent interactions without exhaustive overfitting or model reliance. Our approach successfully replicates trade-to-order volume ratios observed in related asset markets, demonstrating the potential of LLM-augmented ABMs in financial simulations |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21280 |
| By: | Ji Ma |
| Abstract: | As Large Language Model (LLM)-based agents increasingly undertake real-world tasks and engage with human society, how well do we understand their behaviors? This study (1) investigates how LLM agents' prosocial behaviors -- a fundamental social norm -- can be induced by different personas and benchmarked against human behaviors; and (2) introduces a behavioral approach to evaluate the performance of LLM agents in complex decision-making scenarios. We explored how different personas and experimental framings affect these AI agents' altruistic behavior in dictator games and compared their behaviors within the same LLM family, across various families, and with human behaviors. Our findings reveal substantial variations and inconsistencies among LLMs and notable differences compared to human behaviors. Merely assigning a human-like identity to LLMs does not produce human-like behaviors. Despite being trained on extensive human-generated data, these AI agents cannot accurately predict human decisions. LLM agents are not able to capture the internal processes of human decision-making, and their alignment with human behavior is highly variable and dependent on specific model architectures and prompt formulations; even worse, such dependence does not follow a clear pattern. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21359 |