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on Computational Economics |
| By: | Anishka Chauhan; Pratham Mayur; Yeshwanth Sai Gokarakonda; Pooriya Jamie; Naman Mehrotra |
| Abstract: | This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.02236 |
| By: | Zheng, Maoyong; Escalante, Cesar L. |
| Keywords: | Agricultural Finance, Farm Management, Agribusiness |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343857 |
| By: | Stevens, Alexander; Okrent, Abigail M.; Mancino, Lisa |
| Keywords: | Food Consumption/Nutrition/Food Safety |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343997 |
| By: | Villacis, Alexis H.; Badruddoza, Syed; Mishra, Ashok K. |
| Keywords: | International Development, Research Methods/Statistical Methods, Food Security And Poverty |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343542 |
| By: | Yu Cheng; Junjie Guo; Shiqing Long; You Wu; Mengfang Sun; Rong Zhang |
| Abstract: | The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model's understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.06529 |
| By: | Mühlbauer, Sabrina (Institute for Employment Research (IAB), Nuremberg, Germany); Weber, Enzo (Institute for Employment Research (IAB), Nuremberg, Germany) |
| Abstract: | "This paper develops a large-scale algorithm-based application to improve the match quality in the labor market. We use comprehensive administrative data on employment biographies in Germany to predict job match quality in terms of job stability and wages. The models are estimated with both machine learning (ML) (i.e., XGBoost) and common statistical methods (i.e., OLS, logit). Compared to the latter approach, we find that XGBoost performs better for pattern recognition, analyzes large amounts of data in an efficient way and minimizes the prediction error in the application. Finally, we combine our results with algorithms that optimize matching probability to provide a ranked list of job recommendations based on individual characteristics for each job seeker. This application could support caseworkers and job seekers in expanding their job search strategy." (Author's abstract, IAB-Doku) ((en)) |
| JEL: | C14 C45 J64 C55 |
| Date: | 2024–07–12 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabdpa:202409 |
| By: | Alexandre Truc (Université Côte d'Azur, CNRS, GREDEG, France); Muriel Dal Pont Legrand (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | In the present paper, we investigate the diffusion of agent-based models (ABMs) in economics using a quantitative approach to better understand how the introduction of this tool in economics influenced the structure of the field as well as research programs in recent years. Our analysis shows that the proliferation of ABMs has resulted in the emergence of diverse research subfields rather than one unified research program. Most notably, we highlight how interdisciplinarity plays a pivotal role in understanding the diversity of ways in which agent-based models are integrated into economics. While in some cases ABMs are used by economists as an imported tool to address disciplinary-oriented questions in dedicated subfields journals, in other cases ABMs are a vehicle for more interdisciplinary transfers and interactions (e.g., interdisciplinary co-authorship) that are more challenging to the traditional frontiers of economics. |
| Keywords: | Agent-Based, Interdisciplinarity, Social Network Analysis |
| JEL: | B2 B21 B4 D9 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2024-19 |
| By: | Jinniao Qiu; Antony Ware; Yang Yang |
| Abstract: | This paper is devoted to the price-storage dynamics in natural gas markets. A novel stochastic path-dependent volatility model is introduced with path-dependence in both price volatility and storage increments. Model calibrations are conducted for both the price and storage dynamics. Further, we discuss the pricing problem of discrete-time swing options using the dynamic programming principle, and a deep learning-based method is proposed for numerical approximations. A numerical algorithm is provided, followed by a convergence analysis result for the deep-learning approach. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.16400 |
| By: | Jibang Wu; Siyu Chen; Mengdi Wang; Huazheng Wang; Haifeng Xu |
| Abstract: | The agency problem emerges in today's large scale machine learning tasks, where the learners are unable to direct content creation or enforce data collection. In this work, we propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. The problem, termed \emph{contractual reinforcement learning}, naturally arises from the classic model of Markov decision processes, where a learning principal seeks to optimally influence the agent's action policy for their common interests through a set of payment rules contingent on the realization of next state. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation, reducing the complexity analysis to the construction of efficient search algorithms. For several natural classes of problems, we design tailored search algorithms that provably achieve $\tilde{O}(\sqrt{T})$ regret. We also present an algorithm with $\tilde{O}(T^{2/3})$ for the general problem that improves the existing analysis in online contract design with mild technical assumptions. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01458 |
| By: | Yupeng Cao; Zhiyuan Yao; Zhi Chen; Zhiyang Deng |
| Abstract: | The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community. This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within three critical areas of financial tasks: financial classification, financial text summarization, and single stock trading. We adopted Llama3-8B and Mistral-7B as base models, fine-tuning them through Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) approaches. To enhance model performance, we combine datasets from task 1 and task 2 for data fusion. Our approach aims to tackle these diverse tasks in a comprehensive and integrated manner, showcasing LLMs' capacity to address diverse and complex financial tasks with improved accuracy and decision-making capabilities. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01953 |
| By: | Mario Martinoli; Raffaello Seri; Fulvio Corsi |
| Abstract: | Linking the statistic and the machine learning literature, we provide new general results on the convergence of stochastic approximation schemes and inexact Newton methods. Building on these results, we put forward a new optimization scheme that we call generalized inexact Newton method (GINM). We select $P$ points $\mathcal{P}_{i}\left(\boldsymbol{\theta}^{\left(i\right)}\right)=\left\{ \boldsymbol{\theta}_{1}, \dots, \boldsymbol{\theta}_{P}\right\} $ of the parameter space in a neighborhood of $\boldsymbol{\theta}^{\left(i\right)}$ and we compute the objective function through a (polynomial) regression. Then, we estimate the parameter(s) $\boldsymbol{\theta}$ using inexact Newton methods. We extensively discuss the theoretical and the computational aspects of the GINM. The results apply to both deterministic and stochastic approximation schemes, and are particular effective in the case in which the objective function to be optimized is highly irregular and/or the stochastic equicontinuity hypothesis is violated. Examples are common in dynamic discrete choice models and complex simulation models characterized by nonlinearities and high levels of heterogeneity. The theory is supported by extensive Monte Carlo experiments. |
| Keywords: | Optimization, stochastic approximation, Newton-Raphson methods, asymptotic convergence; M-estimation; stochastic equicontinuity |
| Date: | 2024–07–23 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/18 |
| By: | Daiya Mita (Nomura Asset Mahagement Co., Ltd); Akihiko Takahashi (The University of Tokyo) |
| Abstract: | This study proposes a novel equity investment strategy that effectively integrates artificial intelligence (AI) techniques, multi factor models and financial technical indicators. To be practically useful as an investment fund, the strategy is designed to achieve high investment performance without losing interpretability, which is not always the case especially for complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a five factor model in Fama & French [1], a well-known finance model for its explainability to predict future returns by using a gradient boosting machine (GBM) and a state space model. In addition, an index futures short (selling) strategy for downside hedging is developed with IF-THEN rules and three technical indicators. Combining individual equity long and index futures short models, the strategy invests in high expected return equities when the expected return of the portfolio is positive and also the market is expected to rise, otherwise it shorts (sells) index futures. To the best of our knowledge, the current study is the first attempt to develop an equity investment strategy based on a new predictable five factor model, which becomes successful with effective use of AI techniques and technical indicators. Finally, empirical analysis shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:tky:fseres:2024cf1230 |
| By: | Jung-Lundberg, Saman |
| Abstract: | Künstliche Intelligenz kurz KI vollzieht seit einiger Zeit eine rasante Entwicklung. Besonders die unterschiedlichen Industrie- und Wirtschaftszweige erhoffen sich in dieser Evolution eine Möglichkeit mit aktuellen Problemstellungen umzugehen. Durch die bisher in diesem Ausmaß nicht gekannte Option Maschinen durch eigenständiges Anlernen in die Lage zu versetzen konkrete Aufgabenstellungen selbstständig zu lösen, lassen sich unterschiedliche Szenarien entwickeln. Besonders im Hinblick auf den mittlerweile teils akuten Fachkräftemangel in einigen Branchen werden große Erwartungen an eine KIbasierte Kompensation dieses Missstandes geweckt. Diese Aussicht ist auch in der deutschen Bau- und Immobilienwirtschaft aufgekommen. Durch die gezielte Applikation von Künstlicher Intelligenz können verschiedene Teilbereiche im Bauprojektverlauf bedient werden. Dieses Paper soll einen kompakten ersten Eindruck von den Anwendungsbereichen aufzeigen. Besonders die Fachdisziplin der Bauprojektsteuerung wird hier hervorgehoben. Durch die hier vorhandenen Aufgabenstellungen bietet sich ein breites Spektrum für die Übernahme durch eine entsprechend angelernte KI an. |
| Abstract: | Artificial intelligence, or AI for short, has been undergoing rapid development for some time now. The various industrial and economic sectors in particular are hoping that this evolution will enable them to deal with current problems. Various scenarios can be developed thanks to the previously unknown option of enabling machines to solve specific tasks independently through autonomous learning. Particularly in view of the now acute shortage of skilled labour in some sectors, great expectations are being placed on AI-based compensation for this shortcoming. This prospect has also emerged in the German construction and property industry. The targeted application of artificial intelligence can be used in various areas of the construction project process. This paper is intended to provide a compact first impression of the areas of application. The specialist discipline of construction project management is particularly emphasised here. The tasks involved here offer a broad spectrum for appropriately trained AI to take over. |
| Keywords: | Künstliche Intelligenz (KI), Projektsteuerung, Nachtragsmanagement, Building Information Modeling (BIM), Bauwesen |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iudpda:300244 |
| By: | Daiya Mita (Nomura Asset Management Co, ltd., Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (Graduate School of Economics, The University of Tokyo) |
| Abstract: | This study proposes a novel equity investment strategy that effectively integrates artificial intelligence (AI) techniques, multi factor models and financial technical indicators. To be practically useful as an investment fund, the strategy is designed to achieve high investment performance without losing interpretability, which is not always the case especially for complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a five factor model in Fama & French [1], a well-known finance model for its explainability to predict future returns by using a gradient boosting machine (GBM) and a state space model. In addition, an index futures short (selling) strategy for downside hedging is developed with IF-THEN rules and three technical indicators. Combining individual equity long and index futures short models, the strategy invests in high expected return equities when the expected return of the portfolio is positive and also the market is expected to rise, otherwise it shorts (sells) index futures. To the best of our knowledge, the current study is the first attempt to develop an equity investment strategy based on a new predictable five factor model, which becomes successful with effective use of AI techniques and technical indicators. Finally, empirical analysis shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf588 |
| By: | Parisa Davar; Fr\'ed\'eric Godin; Jose Garrido |
| Abstract: | This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.15612 |
| By: | Kamil Kashif; Robert \'Slepaczuk |
| Abstract: | This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boosts the results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across three equity indices (S&P 500, FTSE 100, and CAC 40) using daily frequency data from January 2000 to August 2023. The testing architecture is based on the walk-forward procedure for the hyperparameter tunning phase that uses Random Search and backtesting the algorithms. The selection of the optimal model is determined based on adequately selected performance metrics focused on risk-adjusted return measures. We considered two strategies for each algorithm: Long-Only and Long-Short to present the situation of two various groups of investors with different investment policy restrictions. For each strategy and equity index, we compute the performance metrics and visualize the equity curve to identify the best strategy with the highest modified information ratio. The findings conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms across all the equity indices which confirms the strong potential behind hybrid ML-TS (machine learning - time series) models in searching for the optimal algorithmic investment strategies. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.18206 |
| By: | Helmut Wasserbacher; Martin Spindler |
| Abstract: | Why do companies choose particular capital structures? A compelling answer to this question remains elusive despite extensive research. In this article, we use double machine learning to examine the heterogeneous causal effect of credit ratings on leverage. Taking advantage of the flexibility of random forests within the double machine learning framework, we model the relationship between variables associated with leverage and credit ratings without imposing strong assumptions about their functional form. This approach also allows for data-driven variable selection from a large set of individual company characteristics, supporting valid causal inference. We report three findings: First, credit ratings causally affect the leverage ratio. Having a rating, as opposed to having none, increases leverage by approximately 7 to 9 percentage points, or 30\% to 40\% relative to the sample mean leverage. However, this result comes with an important caveat, captured in our second finding: the effect is highly heterogeneous and varies depending on the specific rating. For AAA and AA ratings, the effect is negative, reducing leverage by about 5 percentage points. For A and BBB ratings, the effect is approximately zero. From BB ratings onwards, the effect becomes positive, exceeding 10 percentage points. Third, contrary to what the second finding might imply at first glance, the change from no effect to a positive effect does not occur abruptly at the boundary between investment and speculative grade ratings. Rather, it is gradual, taking place across the granular rating notches ("+/-") within the BBB and BB categories. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.18936 |
| By: | Domenico Delli Gatti; Tommaso Ferraresi; Filippo Gusella; Lilit Popoyan; Giorgio Ricchiuti; Andrea Roventini |
| Abstract: | We present a multi-country, multi-sector agent-based model that extends Dosi et al. (2019) and incorporates the exchange market and its interaction with the real economy. The exchange rate is influenced not only by trade flows but also by the heterogeneous demand for foreign currencies from financial traders. In this respect, the dual nature of the exchange rate is highlighted, acting both as a transmission channel of endogenous shocks and as a source of shocks. Indeed, differing beliefs bring about real-financial non-linear patterns with feedback mechanisms. Simulations show that the introduction of speculative sentiment behaviour reflects important stylised facts of bilateral exchange rate series. Furthermore, the findings indicate that trend-following behaviour substantially increases financial turbulence and contributes to real economic fluctuations. Finally, we highlight the power and limitations of the central bank as an actor in the exchange rate market, showing that while the central bank's interventions can effectively curb boom-bust cycles, their outcomes differ substantially. |
| Keywords: | agent-based model, exchange rate dynamics, endogenous cycles, heterogeneous traders, central bank interventions. |
| JEL: | E3 F41 O4 O41 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:frz:wpaper:wp2024_10.rdf |
| By: | Jesper Kristensen; David Wender; Carl Anthony |
| Abstract: | The rapid advancements in artificial intelligence, big data analytics, and cloud computing have precipitated an unprecedented demand for computational resources. However, the current landscape of computational resource allocation is characterized by significant inefficiencies, including underutilization and price volatility. This paper addresses these challenges by introducing a novel global platform for the commodification of compute hours, termed the Global Compute Exchange (GCX) (Patent Pending). The GCX leverages blockchain technology and smart contracts to create a secure, transparent, and efficient marketplace for buying and selling computational power. The GCX is built in a layered fashion, comprising Market, App, Clearing, Risk Management, Exchange (Offchain), and Blockchain (Onchain) layers, each ensuring a robust and efficient operation. This platform aims to revolutionize the computational resource market by fostering a decentralized, efficient, and transparent ecosystem that ensures equitable access to computing power, stimulates innovation, and supports diverse user needs on a global scale. By transforming compute hours into a tradable commodity, the GCX seeks to optimize resource utilization, stabilize pricing, and democratize access to computational resources. This paper explores the technological infrastructure, market potential, and societal impact of the GCX, positioning it as a pioneering solution poised to drive the next wave of innovation in commodities and compute. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.19261 |
| By: | Peng Yifeng; Gao Chen |
| Abstract: | This study proposes an innovative evaluation method based on large language models (LLMs) specifically designed to measure the digital transformation (DT) process of enterprises. By analyzing the annual reports of 4407 companies listed on the New York Stock Exchange and Nasdaq from 2005 to 2022, a comprehensive set of DT indicators was constructed. The findings revealed that DT significantly improves a company's financial performance, however, different digital technologies exhibit varying effects on financial performance. Specifically, blockchain technology has a relatively limited positive impact on financial performance. In addition, this study further discovered that DT can promote the growth of financial performance by enhancing operational efficiency and reducing costs. This study provides a novel DT evaluation tool for the academic community, while also expanding the application scope of generative artificial intelligence technology in economic research. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.18440 |
| By: | Will Hicks |
| Abstract: | We discuss the role of information entropy on the behaviour of random processes, and how this might take effect in the dynamics of financial market prices. We then go on to show how the Open Quantum Systems approach can be used as a more flexible alternative to classical methods in terms of modelling the entropy gain of a random process. We start by describing an open quantum system that can be used to model the state of a financial market. We then go on to show how to represent an essentially classical diffusion in this framework. Finally, we show how by relaxing certain assumptions, one can generate interesting and essentially non-classical results, which are highlighted through numerical simulations. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.20027 |
| By: | Haibo Han (Lanzhou University of Finance and Economics); Bin Wang (Lanzhou University of Finance and Economics) |
| Abstract: | Employment policy is an important factor affecting the employment level of college graduates. Based on the policy documents of the Ministry of Education and the text data of the annual report on the employment quality of college graduates from 2015 to 2019 this presentation uses the text data analysis method to calculate the policy perception of colleges and universities, and uses the panel regression model to evaluate the policy effect. The study found that the policy perception of colleges and universities often increases gradually with time and the midland is higher than the eastern and western parts of China. In addition, no matter whether control education investment, policy perception can signi |
| Date: | 2024–06–29 |
| URL: | https://d.repec.org/n?u=RePEc:boc:fsug24:35 |
| By: | Hrishikesh Vinod (Fordham University, Department of Economics) |
| Abstract: | We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stockpicking algorithms involving comparisons of probability distributions. We use data for 30 stocks using the recent 472 months (39+ years) of monthly returns ending in March 2024. Our buy-sell recommendations also use newer "pandemic proof" out-of-sample portfolio performance comparisons from the R package 'generalCorr.' We include modified omega (gain-to-pain ratio) computation to compare stock performance. |
| Keywords: | Dow-Jones |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:frd:wpaper:dp2024-03er:dp2024-03 |
| By: | McWilliams, William N.; Isengildina Massa, Olga; Stewart, Shamar L. |
| Keywords: | Demand And Price Analysis, Agricultural And Food Policy, Risk And Uncertainty |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea22:343923 |
| By: | Sydney Balboni; Grace Ivey; Brett Storoe; John Cisler; Tyge Plater; Caitlyn Grant; Ella Bruce; Benjamin Paulson |
| Abstract: | Price volatility in global food commodities is a critical signal indicating potential disruptions in the food market. Understanding forthcoming changes in these prices is essential for bolstering food security, particularly for nations at risk. The Food and Agriculture Organization of the United Nations (FAO) previously developed sophisticated statistical frameworks for the proactive prediction of food commodity prices, aiding in the creation of global early warning systems. These frameworks utilize food security indicators to produce accurate forecasts, thereby facilitating preparations against potential food shortages. Our research builds on these foundations by integrating robust price security indicators with cutting-edge deep learning (DL) methodologies to reveal complex interdependencies. DL techniques examine intricate dynamics among diverse factors affecting food prices. Through sophisticated time-series forecasting models coupled with a classification model, our approach enhances existing models to better support communities worldwide in advancing their food security initiatives. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.00698 |
| By: | Ecker-Ehrhardt, Matthias |
| Abstract: | Communication professionals working for International Organizations (IOs) are important intermediaries of global governance that increasingly use social media to reach out to citizens directly. Social media pose new challenges for IO public communication, such as a highly competitive economy of attention and the fragmentation of audiences driven by networked curation of content and selective exposure. In this context, IO social media communication has to make tough choices about what to communicate and how, aggravating inherent conflicts of IO communication between comprehensive public information (aiming at institutional transparency) - and partisan political advocacy (aiming at normative change). If IOs choose advocacy, they might garner substantial resonance on social media. IO advocacy nevertheless fails to the extent that it fosters the polarized fragmentation of networked communication and undermines the credibility of IO communication as a source of trust - worthy information across polarized 'echo chambers'. The paper illustrates this argument through a quantitative content and social network analysis of X/Twitter communication on the Global Compact for Safe, Orderly, and Regular Migration (GCM). Remarkably, instead of facilitating cross-cluster communication ('building bridges'), United Nations accounts seem to have substantially fostered ideological fragmentation ('digging the trench') by their way of partisan retweeting, mentioning, and (hash)tagging. |
| Keywords: | international organizations, social media, public communication, echo chambers, advocacy, United Nations, Global Compact for Migration, content analysis, supervised machine learning, social network analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:khkgcr:300240 |
| By: | Cai, Xiqian (Xiamen University); Chen, Shuai (University of Leicester); Cheng, Zhengquan (Xiamen University) |
| Abstract: | Gender inequality and discrimination still persist, even though the gender gap in the labor market has been gradually decreasing. This study examines the effect of the #MeToo movement on judges' gender gap in their vital labor market outcome–judicial decisions on randomly assigned legal cases in China. We apply a difference-in-differences approach to unique verdict data including rich textual information on characteristics of cases and judges, and compare changes in sentences of judges of a different gender after the movement. We find that female judges made more severe decisions post-movement, which almost closed the gender gap. Moreover, we explore a potential mechanism of gender norms, documenting evidence for improved awareness of gender equality among women following the movement and stronger effects on judges' gender gap reduction in regions with better awareness of gender equality. This implies that female judges became willing to stand out and speak up, converging to their male counterparts after the #MeToo movement. |
| Keywords: | #MeToo movement, gender gap, inequality, judicial decision, crime, machine learning |
| JEL: | J16 K14 O12 P35 D63 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17115 |