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on Computational Economics |
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Issue of 2024‒02‒12
seventeen papers chosen by |
| By: | Brahmana, Rayenda Khresna |
| Abstract: | The emergence of cryptocurrencies as digital investments drives scholars to explore their predictive prices. Intriguingly, most research focuses on its price and returns prediction using various models, leaving out the importance of persistent risk for portfolio management. This is not to mention that most research focuses only on Bitcoin, neglecting other altcoins and stablecoins. Therefore, this study comprehensively examines the cryptocurrency investment’s persistent risk from the forecasting point of view. We focus on comparing the best forecasting methods because they are vital for volatility-targeting and risk-parity in portfolio strategy. Four time-series model performances will be compared to select a suitable volatility prediction model: Machine Learning-Based GARCH, Machine Learning-Based SVR-GARCH, Neural Network, and Deep Learning. Using six different cryptocurrencies proxies: Bitcoin, Ethereum, Ripple, USD Coin, Tether, and Binance Coin, we found that ML-Based SVR-GARCH outperformed the peers in volatility forecasting. However, the prediction accuracy differences among all models are not significant. Finally, our paper provides new insights into machine learning methods’ applications in cryptocurrency market volatility prediction, which is helpful for academics, policy-makers, and investors in forming portfolio strategies. |
| Keywords: | Volatility Forecasting; Cryptocurrencies; Bitcoin; SVR-GARCH; Neural Network; Deep Learning |
| JEL: | C53 G17 G32 |
| Date: | 2022–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119598&r=cmp |
| By: | Vetter, Oliver A.; Sturm, Timo; Fecho, Mariska; Buxmann, Peter |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:142034&r=cmp |
| By: | Achim Ahrens; Christian B. Hansen; Mark E. Schaffer; Thomas Wiemann |
| Abstract: | This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to substantially reduce the computational burden and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and R software implementing our proposals. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.01645&r=cmp |
| By: | Yue Chen; Xingyi Andrew; Salintip Supasanya |
| Abstract: | Historically, the economic recession often came abruptly and disastrously. For instance, during the 2008 financial crisis, the SP 500 fell 46 percent from October 2007 to March 2009. If we could detect the signals of the crisis earlier, we could have taken preventive measures. Therefore, driven by such motivation, we use advanced machine learning techniques, including Random Forest and Extreme Gradient Boosting, to predict any potential market crashes mainly in the US market. Also, we would like to compare the performance of these methods and examine which model is better for forecasting US stock market crashes. We apply our models on the daily financial market data, which tend to be more responsive with higher reporting frequencies. We consider 75 explanatory variables, including general US stock market indexes, SP 500 sector indexes, as well as market indicators that can be used for the purpose of crisis prediction. Finally, we conclude, with selected classification metrics, that the Extreme Gradient Boosting method performs the best in predicting US stock market crisis events. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.06172&r=cmp |
| By: | Emmanuil H. Georgoulis; Antonis Papapantoleon; Costas Smaragdakis |
| Abstract: | We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new implicit-explicit minimizing movement time-stepping approach, involving approximation by deep, residual-type Artificial Neural Networks (ANNs) for each time step. The integral operator is discretized via two different approaches: a) a sparse-grid Gauss--Hermite approximation following localised coordinate axes arising from singular value decompositions, and b) an ANN-based high-dimensional special-purpose quadrature rule. Crucially, the proposed ANN is constructed to ensure the asymptotic behavior of the solution for large values of the underlyings and also leads to consistent outputs with respect to a priori known qualitative properties of the solution. The performance and robustness with respect to the dimension of the methods are assessed in a series of numerical experiments involving the Merton jump-diffusion model. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.06740&r=cmp |
| By: | Vetter, Oliver A.; Pumplun, Luisa; Koppe, Timo |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:142032&r=cmp |
| By: | Simons, Martin; Roloff, Malte; Liebe, Andrea; Lundborg, Martin |
| Abstract: | Automatisiertes Maschinelles Lernen (AutoML) sowie Low-Code und No-Code versprechen, im Sinne des Citizen Developer-Konzepts eine einfachere Nutzung von Künstlicher Intelligenz (KI) indem die erforderlichen Programmierkenntnisse bzw. der Entwicklungsaufwand reduziert werden. Diese neuen Ansätze erleichtern somit insbesondere kleinen und mittleren Unternehmen (KMU) den Einstieg und bieten damit das Potenzial für eine schnelle Verbreitung und stärkere Nutzung von KI-Lösungen. Ziel dieser Studie ist es zu untersuchen, ob diese Versprechen eingelöst werden können. |
| Abstract: | Automated Machine Learning (AutoML), low-code and no-code promise to make artificial intelligence (AI) easier to use, in line with the citizen developer concept, by reducing the programming skills and development effort required. These new approaches therefore make it easier for small and medium-sized enterprises (SMEs) in particular to get started. They thus offer the potential for rapid dissemination and greater use of AI solutions. The aim of this study is to investigate whether these promises can be realised. |
| Keywords: | Künstliche Intelligenz, Automatisierung, PC-Software, KMU, Deutschland |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wikdps:280934&r=cmp |
| By: | Otero, Fernando; Managó, Bianca Bietti |
| Abstract: | In this work, we propose, design, and implement a solution to a problem of evaluating a numerical method. This problem was conceived with the intention of applying the methodology of problem-based learning (PBL) in postgraduate students of a course called "Applied Mathematics to Indirect Measurements". The main objective is for the student to understand and relate several fundamental concepts that appear in the problem at hand, without adding additional complications in the development of the code. Therefore, a linear toy model with a single random variable is considered. The specific problem is the study of the performance of a sequential Monte Carlo (SMC) method, developed by the authors in the field of inverse problems, through the estimation of the method's tuning parameter. To do this, a supervised classification is proposed that use as training data those generated by the optimal estimation (OE) method. The idea of the work is that students, through the developed code, can analyze how the topics of two very important areas of applied mathematics today, namely, inverse problems (IP) and machine learning (ML), are integrated in practice through a Bayesian approach, such as the OE method. |
| Date: | 2024–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:xhztv&r=cmp |
| By: | Paolo Pellizzari (Department of Economics, Ca’ Foscari University of Venice) |
| Abstract: | Can traders in a financial market learn whether to be informed and which information to use in their demand for risky assets? We describe in this paper an agent-based model where heterogeneous traders seek short-term profits and differ in their choices to use or discard some signals. In the model, a vector of fresh news/signals is available at every period and some (but not all) the signals affect the stochastic payoff of the stock. Under an evolutionary dynamics favouring higher myopic returns we find that, in equilibrium, traders mostly end up in either discarding all signals or being (perfectly) informed using all the relevant signals (paying the related costs). Moreover, the rate of use of information strongly depends on the "complexity" of the market: an excessively large abundance of signals to be screened or a high volatility of the market, result in large shares of passive agents who overestimate the market's risk; conversely, low market complexity is associated with a more intense use of information and aggressiveness of informed traders. Evolutionary models and Agent-based models and Information in financial markets |
| Keywords: | financial markets w information, agent-based models, evolutionary game theory, equity premium puzzle |
| JEL: | G17 D83 D91 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2024:03&r=cmp |
| By: | Ali Mehrban; Pegah Ahadian |
| Abstract: | This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in a daily timeframe. The methods used to teach the data are hybrid and backpropagation algorithms, as well as grid partition, subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05441&r=cmp |
| By: | Dar\'io Blanco-Fern\'andez; Stephan Leitner; Alexandra Rausch |
| Abstract: | This paper examines the interactions between selected coordination modes and dynamic team composition, and their joint effects on task performance under different task complexity and individual learning conditions. Prior research often treats dynamic team composition as a consequence of suboptimal organizational design choices. The emergence of new organizational forms that consciously employ teams that change their composition periodically challenges this perspective. In this paper, we follow the contingency theory and characterize dynamic team composition as a design choice that interacts with other choices such as the coordination mode, and with additional contextual factors such as individual learning and task complexity. We employ an agent-based modeling approach based on the NK framework, which includes a reinforcement learning mechanism, a recurring team formation mechanism based on signaling, and three different coordination modes. Our results suggest that by implementing lateral communication or sequential decision-making, teams may exploit the benefits of dynamic composition more than if decision-making is fully autonomous. The choice of a proper coordination mode, however, is partly moderated by the task complexity and individual learning. Additionally, we show that only a coordination mode based on lateral communication may prevent the negative effects of individual learning. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05832&r=cmp |
| By: | Frank Xing |
| Abstract: | Large language models (LLMs) have drastically changed the possible ways to design intelligent systems, shifting the focuses from massive data acquisition and new modeling training to human alignment and strategical elicitation of the full potential of existing pre-trained models. This paradigm shift, however, is not fully realized in financial sentiment analysis (FSA), due to the discriminative nature of this task and a lack of prescriptive knowledge of how to leverage generative models in such a context. This study investigates the effectiveness of the new paradigm, i.e., using LLMs without fine-tuning for FSA. Rooted in Minsky's theory of mind and emotions, a design framework with heterogeneous LLM agents is proposed. The framework instantiates specialized agents using prior domain knowledge of the types of FSA errors and reasons on the aggregated agent discussions. Comprehensive evaluation on FSA datasets show that the framework yields better accuracies, especially when the discussions are substantial. This study contributes to the design foundations and paves new avenues for LLMs-based FSA. Implications on business and management are also discussed. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05799&r=cmp |
| By: | Alexander Leodolter; Aleksander Rutkowski |
| Abstract: | Mortgage interest tax relief contributes to the favourable tax treatment of owner-occupied housing com-pared to other investments. It thereby creates market distortions and may at the same time often not give rise to its intended effect, namely to increase homeownership. EU country-specific recommendations have asked for a reduction of the relief in Member States, also in view of risks to macroeconomic stability. The paper analyses the effects of removing mortgage interest tax relief on public revenue and expenditure, household disposable income and income inequality in 14 EU Member States with the microsimulation model EUROMOD. It finds that the tax relief largely benefits households at medium to high income levels. Consequently, its removal could help decrease income inequality in almost all Member States. |
| Keywords: | The Fiscal and Distributional Effects of Removing Mortgage Interest Tax Relief in EU Member States, Leodolter, Rutkowski, mortgage interest tax relief, mortgage interest tax deductibility, immovable property, housing, taxation, owner-occupied housing, homeownership tax bias, EUROMOD, simulation, inequality. |
| JEL: | D1 D14 D3 D31 H2 H21 H22 H23 H24 H31 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:euf:ecobri:072&r=cmp |
| By: | Amina Badreddine (UMBB - Université de M'Hamed Bougara Boumerdes, Algérie); Hadjira Larbi Cherif (UMBB - Université de M'Hamed Bougara Boumerdes, Algérie) |
| Abstract: | في الساحة الأكاديمية، يستخدم الأساتذة والطلاب نماذج الذكاء الاصطناعي مثل ChatGPT في مجموعة متنوعة من المهام الأكاديمية وغير الأكاديمية، بما في ذلك كتابة المقالات وكتابة الخطابات الرسمية وغير الرسمية وتلخيص المقالات الأدبية وتوليد الأفكار الإبداعية. ومع ذلك، ما زال هناك جدل كبير حول دور ChatGPT في البحث العلمي. تهدف هذا الدراسة إلى تسليط الضوء على استخدام ChatGPT في البحث الأكاديمي من خلال أمثلة حقيقية. تشير نتائجنا إلى أن ChatGPT يمكن أن يكون أداة قوية لتوليد الأفكار الأولية في البحث العلمي. ومع ذلك، قد تظهر تحديات عندما يتعلق الأمر بالدراسات السابقة الاستشهادات و المصادر وتحديد مشكلات البحث وتحديد الفجوات البحثية وإجراء تحليل البيانات. لذا، يجب على الاكاديمين أن يكونوا حذرين عند استخدام ChatGPT في البحث الأكاديمي. لذلك نرى أنه من الضروري إنشاء إرشادات واضحة لكيفية استخدام الذكاء الاصطناعي بحذر في البحث والنشر العلمي. |
| Abstract: | In the academic sphere, educators, scholars, and students have previously utilized Large Language Models like ChatGPT for a range of academic and non-academic tasks, including essay composition, formal and informal speechwriting, literary summarization, and creative idea generation. However, a debate continues regarding ChatGPT's role in scholarly research. This study aims to illuminate ChatGPT's use in academic research through practical examples and recommendations. Our findings suggest that ChatGPT can be a powerful tool for generating preliminary ideas in scientific research. However, challenges may arise when it comes to integrating literature, citing sources, defining research problems, identifying gaps, and conducting data analysis. Thus, scholars should exercise caution when considering ChatGPT for these tasks in academic research. Given ChatGPT's potential uses and implications, the academic and scientific community needs to establish guidelines for the judicious use of LLMs, especially ChatGPT, in academic research. |
| Keywords: | ChatGPT Academic research authorship gaps, ChatGPT, Academic research, authorship, gaps |
| Date: | 2023–11–14 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04379581&r=cmp |
| By: | Vetter, Oliver A.; Efremov, Alexander |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:142030&r=cmp |
| By: | Sara Slamić Tarade (Zagreb University of Applied Sciences, Vrbik 8, 10000, Zagreb, Croatia Author-2-Name: Dijana Vuković Author-2-Workplace-Name: University of North, Jurja Križanića 31b, 42000, Varaždin, Croatia Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | "Objective - This paper focuses on analyzing the significance of sports footwear brands by processing large text data from the Internet. In a modern environment, the brand distinguishes a company's products or services from those of its competitors. A strong brand can help build trust with customers as they perceive the brand as reliable and trustworthy. Methodology/Technique - The study uses NLP (Natural Language Processing) methods to analyze rich text content on the Internet. The research focus is based on the application of innovative methods to determine the importance and value of a brand using NLP techniques by analyzing the content of a large corpus of text originating from websites dealing with sports footwear brands. The NLP analysis models were programmed using the low-code analysis tool KNIME. Findings - The analysis is carried out for the most well-known sports footwear brands such as Nike, Adidas, Puma, Under Armour, Reebok and Asics. The research object refers to the analysis of brand significance and the evaluation of consumer opinions on sports footwear, based on the processing of large text data from the internet. Novelty - The research results are based on an innovative approach to measuring and evaluating the brand significance in sports footwear using NLP methods to analyze large text content from the Internet. The results obtained show that this new approach to metrics and evaluation can significantly improve existing methods of brand evaluation. Type of Paper - Empirical" |
| Keywords: | Brand, NLP Method, Text Analysis, Online Brand Management Strategies, Sports Footwear |
| JEL: | M39 |
| Date: | 2023–12–31 |
| URL: | http://d.repec.org/n?u=RePEc:gtr:gatrjs:jmmr326&r=cmp |
| By: | Baptiste Lefort; Eric Benhamou; Jean-Jacques Ohana; David Saltiel; Beatrice Guez; Damien Challet |
| Abstract: | We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document a statistically significant positive correlation between the sentiment score and future equity market returns over short to medium term, which reverts to a negative correlation over longer horizons. Validation of this correlation pattern across multiple equity markets indicates its robustness across equity regions and resilience to non-linearity, evidenced by comparison of Pearson and Spearman correlations. Finally, we provide an estimate of the optimal horizon that strikes a balance between reactivity to new information and correlation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05447&r=cmp |