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on Computational Economics |
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Issue of 2024‒05‒20
twenty papers chosen by |
| By: | Masanori Hirano |
| Abstract: | Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle incomplete markets. However, deep hedging usually requires underlying asset simulations, and it is challenging to select the best model for such simulations. This study proposes a new approach using artificial market simulations for underlying asset simulations in deep hedging. Artificial market simulations can replicate the stylized facts of financial markets, and they seem to be a promising approach for deep hedging. We investigate the effectiveness of the proposed approach by comparing its results with those of the traditional approach, which uses mathematical finance models such as Brownian motion and Heston models for underlying asset simulations. The results show that the proposed approach can achieve almost the same level of performance as the traditional approach without mathematical finance models. Finally, we also reveal that the proposed approach has some limitations in terms of performance under certain conditions. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.09462&r=cmp |
| By: | Böcking, Lars; Michaelis, Anne; Schäfermeier, Bastian; Baier, André; Kühl, Niklas; Körner, Marc-Fabian; Nolting, Lars |
| Keywords: | Generative Künstliche Intelligenz, GenAI, Energiewirtschaft, TenneT |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bayism:290410&r=cmp |
| By: | Francisco de Arriba-P\'erez; Silvia Garc\'ia-M\'endez; Jos\'e A. Regueiro-Janeiro; Francisco J. Gonz\'alez-Casta\~no |
| Abstract: | Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors' opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term "opportunities" that are akin to "anticipation" in Plutchik's theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors' decision making. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.07224&r=cmp |
| By: | Masanori Hirano; Kentaro Imajo |
| Abstract: | Large language models (LLMs) are now widely used in various fields, including finance. However, Japanese financial-specific LLMs have not been proposed yet. Hence, this study aims to construct a Japanese financial-specific LLM through continual pre-training. Before tuning, we constructed Japanese financial-focused datasets for continual pre-training. As a base model, we employed a Japanese LLM that achieved state-of-the-art performance on Japanese financial benchmarks among the 10-billion-class parameter models. After continual pre-training using the datasets and the base model, the tuned model performed better than the original model on the Japanese financial benchmarks. Moreover, the outputs comparison results reveal that the tuned model's outputs tend to be better than the original model's outputs in terms of the quality and length of the answers. These findings indicate that domain-specific continual pre-training is also effective for LLMs. The tuned model is publicly available on Hugging Face. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.10555&r=cmp |
| By: | Pawanesh Yadav; Charu Sharma; Niteesh Sahni |
| Abstract: | This paper presents an analysis of the Indian stock market using a method based on embedding the network in a hyperbolic space using Machine learning techniques. We claim novelty on four counts. First, it is demonstrated that the hyperbolic clusters resemble the topological network communities more closely than the Euclidean clusters. Second, we are able to clearly distinguish between periods of market stability and volatility through a statistical analysis of hyperbolic distance and hyperbolic shortest path distance corresponding to the embedded network. Third, we demonstrate that using the modularity of the embedded network significant market changes can be spotted early. Lastly, the coalescent embedding is able to segregate the certain market sectors thereby underscoring its natural clustering ability. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.04710&r=cmp |
| By: | Mark Setterfield (Department of Economics, New School For Social Research, USA); George Wheaton (Department of Economics, New School For Social Research, USA) |
| Abstract: | The Goodwin pattern – a counter-cyclical rotation in real activity × wage share space – is an established stylized fact of capitalist macrodynamics. Existing models typically assume profit-led real-sector dynamics, together with a ‘reserve army’ or profit squeeze mechanism, in order to reproduce this pattern. We extend an animal-spirits-driven business cycle model, in which the demand regime determining real-sector outcomes is wage-led, by adding a reserve army effect determining real wage dynamics. We construct an agent-based simulation of the model and show that we are able to reproduce the Goodwin pattern. Our results add to the literature suggesting that the Goodwin pattern can arise from a variety of different macrodynamic ensembles – including some that feature wage-led real sectors. |
| Keywords: | Goodwin pattern, animal spirits, wage-led demand, reserve army mechanism, profit squeeze, agent-based model, cyclical growth |
| JEL: | C63 E11 E12 E32 E37 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:new:wpaper:2407&r=cmp |
| By: | Alicia Vidler |
| Abstract: | Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recommendations against the backdrop of the analyst's recommendation track record and the applicability of the analyst to the recommendation they provide. Many firms regularly ask analysts to provide a "conviction" level on their recommendations. In the eyes of PMs, understanding a human analyst's track record has typically come down to basic spread sheet tabulation or, at best, a "virtual portfolio" paper trading book to keep track of results of recommendations. Analysts' conviction around their recommendations and their "paper trading" track record are two crucial workflow components between analysts and portfolio construction. Many human PMs may not even appreciate that they factor these data points into their decision-making logic. This chapter explores how Artificial Intelligence (AI) can be used to replicate these two steps and bridge the gap between AI data analytics and AI-based portfolio construction methods. This field of AI is referred to as Recommender Systems (RS). This chapter will further explore what metadata that RS systems functionally supply to downstream systems and their features. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11080&r=cmp |
| By: | Thiago C. Silva; Paulo V. B. Wilhelm; Diego R. Amancio |
| Abstract: | This study examines the effects of de-globalization trends on international trade networks and their role in improving forecasts for economic growth. Using section-level trade data from nearly 200 countries from 2010 to 2022, we identify significant shifts in the network topology driven by rising trade policy uncertainty. Our analysis highlights key global players through centrality rankings, with the United States, China, and Germany maintaining consistent dominance. Using a horse race of supervised regressors, we find that network topology descriptors evaluated from section-specific trade networks substantially enhance the quality of a country's GDP growth forecast. We also find that non-linear models, such as Random Forest, XGBoost, and LightGBM, outperform traditional linear models used in the economics literature. Using SHAP values to interpret these non-linear model's predictions, we find that about half of most important features originate from the network descriptors, underscoring their vital role in refining forecasts. Moreover, this study emphasizes the significance of recent economic performance, population growth, and the primary sector's influence in shaping economic growth predictions, offering novel insights into the intricacies of economic growth forecasting. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08712&r=cmp |
| By: | Chen, Yudong; Chen, Yining |
| Abstract: | Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated by many existing tests for the presence of a change point being representable by a simple neural network, and thus a neural network trained with sufficient data should have performance at least as good as these methods. We present theory that quantifies the error rate for such an approach, and how it depends on the amount of training data. Empirical results show that, even with limited training data, its performance is competitive with the standard cumulative sum (CUSUM) based classifier for detecting a change in mean when the noise is independent and Gaussian, and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. Our method also shows strong results in detecting and localizing changes in activity based on accelerometer data. |
| Keywords: | automatic statistician; classification; likelihood-free inference; neural networks; structural breaks; supervised learning; OUP deal |
| JEL: | C1 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121252&r=cmp |
| By: | Gavioli-Akilagun, Shakeel |
| Keywords: | OUP deal |
| JEL: | C1 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121251&r=cmp |
| By: | Daube, Carl Heinz; Krivenkov, Vladislav |
| Abstract: | Dieses Working Paper untersucht den Einsatz von Generative AI Anwendungen zur Prognose von Leitzinsentscheidungen der Federal Reserve. Es bewertet, ob diese Anwendungen eingesetzt werden können, um Leitzinsänderungen vorherzusagen, und vergleicht ihre Vorhersagegenauigkeit mit den Markterwartungen über einen Zeitraum von sechs Monaten. |
| Abstract: | This working paper investigates the use of generative AI applications for predicting interest rate decisions by the Federal Reserve. It assesses whether these applications can be utilized to forecast changes in interest rates and compares their predictive accuracy with market expectations over a period of six months. |
| Keywords: | Künstliche Intelligenz, FED Leitzinsentscheidungen, large language model (LLM), natural language processing (NLP), Generative AI Anwendung ChatGPT, Generative AI AnwendungW Google Gemini |
| JEL: | G0 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:293992&r=cmp |
| By: | Nisarg Patel; Harmit Shah; Kishan Mewada |
| Abstract: | Navigating the intricate landscape of financial markets requires adept forecasting of stock price movements. This paper delves into the potential of Long Short-Term Memory (LSTM) networks for predicting stock dynamics, with a focus on discerning nuanced rise and fall patterns. Leveraging a dataset from the New York Stock Exchange (NYSE), the study incorporates multiple features to enhance LSTM's capacity in capturing complex patterns. Visualization of key attributes, such as opening, closing, low, and high prices, aids in unraveling subtle distinctions crucial for comprehensive market understanding. The meticulously crafted LSTM input structure, inspired by established guidelines, incorporates both price and volume attributes over a 25-day time step, enabling the model to capture temporal intricacies. A comprehensive methodology, including hyperparameter tuning with Grid Search, Early Stopping, and Callback mechanisms, leads to a remarkable 53% improvement in predictive accuracy. The study concludes with insights into model robustness, contributions to financial forecasting literature, and a roadmap for real-time stock market prediction. The amalgamation of LSTM networks, strategic hyperparameter tuning, and informed feature selection presents a potent framework for advancing the accuracy of stock price predictions, contributing substantively to financial time series forecasting discourse. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.18822&r=cmp |
| By: | Spurthi Setty; Katherine Jijo; Eden Chung; Natan Vidra |
| Abstract: | The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by RAG rather than the inherent capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine the RAG process. This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms. Implementing these approaches can substantially improve the retrieval quality, thereby elevating the overall performance and reliability of LLMs in processing and responding to queries. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.07221&r=cmp |
| By: | Yanwei Jia |
| Abstract: | This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation with the exponential-form objective. The risk-sensitive objective arises either as the agent's risk attitude or as a distributionally robust approach against the model uncertainty. Owing to the martingale perspective in Jia and Zhou (2023) the risk-sensitive RL problem is shown to be equivalent to ensuring the martingale property of a process involving both the value function and the q-function, augmented by an additional penalty term: the quadratic variation of the value process, capturing the variability of the value-to-go along the trajectory. This characterization allows for the straightforward adaptation of existing RL algorithms developed for non-risk-sensitive scenarios to incorporate risk sensitivity by adding the realized variance of the value process. Additionally, I highlight that the conventional policy gradient representation is inadequate for risk-sensitive problems due to the nonlinear nature of quadratic variation; however, q-learning offers a solution and extends to infinite horizon settings. Finally, I prove the convergence of the proposed algorithm for Merton's investment problem and quantify the impact of temperature parameter on the behavior of the learning procedure. I also conduct simulation experiments to demonstrate how risk-sensitive RL improves the finite-sample performance in the linear-quadratic control problem. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.12598&r=cmp |
| By: | Benjamin S. Manning; Kehang Zhu; John J. Horton |
| Abstract: | We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are both proposed and tested by the system, finding evidence for some and not others. We provide evidence that the insights from these simulations of social interactions are not available to the LLM purely through direct elicitation. When given its proposed structural causal model for each scenario, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those estimates. In the auction experiment, the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from the LLM are inaccurate. However, the LLM's predictions are dramatically improved if the model can condition on the fitted structural causal model. In short, the LLM knows more than it can (immediately) tell. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11794&r=cmp |
| By: | Lee, Wang-Sheng (Monash University); Tran, Trang My (Monash University) |
| Abstract: | Environmental research related to military activities and warfare is sparse and fragmented by discipline. Although achieving military objectives will likely continue to trump any concerns related to the environment during active conflict, military training during peacetime has environmental consequences. This research aims to quantify how much pollution is emitted during regular military exercises which has implications for climate change. Focusing on major military training exercises conducted in Australia, we assess the impact of four international exercises held within a dedicated military training area on pollution levels. Leveraging high-frequency data, we employ a machine learning algorithm in conjunction with program evaluation techniques to estimate the effects of military training activities. Our main approach involves generating counterfactual predictions and utilizing a "prediction-error" framework to estimate treatment effects by comparing a treatment area to a control area. Our findings reveal that these exercises led to a notable increase in air pollution levels, potentially reaching up to 25% relative to mean levels during peak training hours. |
| Keywords: | machine learning, military emissions, military training, pollution |
| JEL: | C55 Q53 Q54 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16889&r=cmp |
| By: | Haotian Chen; Xinjie Shen; Zeqi Ye; Xiao Yang; Xu Yang; Weiqing Liu; Jiang Bian |
| Abstract: | The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments. Researchers often seek the potential research directions by reading and then verifying them through experiments. The process imposes a significant burden on researchers. In the past decade, the data-driven black-box deep learning method demonstrates its effectiveness in a wide range of real-world scenarios, which exacerbates the experimental burden of researchers and thus renders the potential successful discoveries veiled. Therefore, automating such a research and development (R&D) process is an urgent need. In this paper, we serve as the first effort to formalize the goal by proposing a Real-world Data-centric automatic R&D Benchmark, namely RD2Bench. RD2Bench benchmarks all the operations in data-centric automatic R&D (D-CARD) as a whole to navigate future work toward our goal directly. We focuses on evaluating the interaction and synergistic effects of various model capabilities and aiding to select the well-performed trustworthy models. Although RD2Bench is very challenging to the state-of-the-art (SOTA) large language model (LLM) named GPT-4, indicating ample research opportunities and more research efforts, LLMs possess promising potential to bring more significant development to D-CARD: They are able to implement some simple methods without adopting any additional techniques. We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11276&r=cmp |
| By: | Rei Iwafuchi; Yasumasa Matsuda |
| Abstract: | The market for investment trusts of large-scale portfolios, including index funds, continues to grow, and high-dimensional volatility estimation is essential for assessing the risks of such portfolios. However, multivariate volatility models suitable for high-dimensional data have not been extensively studied. This paper introduces a new framework based on the Spatial AR model, which provides fast and stable estimation, and demonstrates its application through simulations using historical data from the S&P 500. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:toh:dssraa:141&r=cmp |
| By: | Marie Hogan; Aakash Kalyani |
| Abstract: | An analysis of the diffusion of PCs, smart devices, cloud computing and 3D printing suggests that AI may spread in a pattern similar to those of PCs and cloud computing. |
| Keywords: | artificial intelligence; personal computers; cloud computing; productivity growth |
| Date: | 2024–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:l00001:98109&r=cmp |
| By: | R. Michael Alvarez; Jacob Morrier |
| Abstract: | This paper presents a new approach to evaluating the quality of answers in political question-and-answer sessions. We propose to measure an answer's quality based on the degree to which it allows us to infer the initial question accurately. This conception of answer quality inherently reflects their relevance to initial questions. Drawing parallels with semantic search, we argue that this measurement approach can be operationalized by fine-tuning a large language model on the observed corpus of questions and answers without additional labeled data. We showcase our measurement approach within the context of the Question Period in the Canadian House of Commons. Our approach yields valuable insights into the correlates of the quality of answers in the Question Period. We find that answer quality varies significantly based on the party affiliation of the members of Parliament asking the questions and uncover a meaningful correlation between answer quality and the topics of the questions. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08816&r=cmp |