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on Computational Economics |
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Issue of 2024‒05‒27
nine papers chosen by |
| By: | Francisco G\'omez Casanova; \'Alvaro Leitao; Fernando de Lope Contreras; Carlos V\'azquez |
| Abstract: | This paper addresses the problem of pricing involved financial derivatives by means of advanced of deep learning techniques. More precisely, we smartly combine several sophisticated neural network-based concepts like differential machine learning, Monte Carlo simulation-like training samples and joint learning to come up with an efficient numerical solution. The application of the latter development represents a novelty in the context of computational finance. We also propose a novel design of interdependent neural networks to price early-exercise products, in this case, Bermudan swaptions. The improvements in efficiency and accuracy provided by the here proposed approach is widely illustrated throughout a range of numerical experiments. Moreover, this novel methodology can be extended to the pricing of other financial derivatives. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11257&r=cmp |
| By: | Anoop Kumar; Suresh Dodda; Navin Kamuni; Rajeev Kumar Arora |
| Abstract: | This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by -11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.07225&r=cmp |
| By: | James T. Wilkinson; Jacob Kelter; John Chen; Uri Wilensky |
| Abstract: | We present a novel agent-based approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers and agent visibility is constrained to a network topology. Dynamics, such as changes in price, result from agent-level interactions that ubiquitously occur via market maker agents acting as liquidity providers. Two additional agents are considered: trend investors use a deep convolutional neural network paired with a deep Q-learning framework to inform trading decisions by analysing price history; and value investors use a static price-target to determine their trade directions and sizes. We demonstrate that our novel inclusion of a network topology with market makers facilitates explorations into various market structures. First, we present the model and an overview of its mechanics. Second, we validate our findings via comparison to the real-world: we demonstrate a fat-tailed distribution of price changes, auto-correlated volatility, a skew negatively correlated to market maker positioning, predictable price-history patterns and more. Finally, we demonstrate that our network-based model can lend insights into the effect of market-structure on price-action. For example, we show that markets with sparsely connected intermediaries can have a critical point of fragmentation, beyond which the market forms distinct clusters and arbitrage becomes rapidly possible between the prices of different market makers. A discussion is provided on future work that would be beneficial. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02480&r=cmp |
| By: | Anna Knezevic |
| Abstract: | Enhancing the existing solution for pricing of fixed income instruments within Black-Karasinski model structure, with neural network at various parameterisation points to demonstrate that the method is able to achieve superior outcomes for multiple calibrations across extended projection horizons. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08903&r=cmp |
| By: | Jose Ignacio Hernandez; Niek Mouter; Sander van Cranenburgh |
| Abstract: | Random utility maximisation (RUM) models are one of the cornerstones of discrete choice modelling. However, specifying the utility function of RUM models is not straightforward and has a considerable impact on the resulting interpretable outcomes and welfare measures. In this paper, we propose a new discrete choice model based on artificial neural networks (ANNs) named "Alternative-Specific and Shared weights Neural Network (ASS-NN)", which provides a further balance between flexible utility approximation from the data and consistency with two assumptions: RUM theory and fungibility of money (i.e., "one euro is one euro"). Therefore, the ASS-NN can derive economically-consistent outcomes, such as marginal utilities or willingness to pay, without explicitly specifying the utility functional form. Using a Monte Carlo experiment and empirical data from the Swissmetro dataset, we show that ASS-NN outperforms (in terms of goodness of fit) conventional multinomial logit (MNL) models under different utility specifications. Furthermore, we show how the ASS-NN is used to derive marginal utilities and willingness to pay measures. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.13198&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. |
| JEL: | D0 D9 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32381&r=cmp |
| By: | Tom Ewen |
| Abstract: | The accurate valuation of financial derivatives plays a pivotal role in the finance industry. Although closed formulas for pricing are available for certain models and option types, exemplified by the European Call and Put options in the Black-Scholes Model, the use of either more complex models or more sophisticated options precludes the existence of such formulas, thereby requiring alternative approaches. The Monte Carlo simulation, an alternative approach effective in nearly all scenarios, has already been challenged by quantum computing techniques that leverage Amplitude Estimation. Despite its theoretical promise, this approach currently faces limitations due to the constraints of hardware in the Noisy Intermediate-Scale Quantum (NISQ) era. In this study, we introduce and analyze a quantum algorithm for pricing European call options across a broad spectrum of asset models. This method transforms a classical approach, which utilizes the Fast Fourier Transform (FFT), into a quantum algorithm, leveraging the efficiency of the Quantum Fourier Transform (QFT). Furthermore, we compare this novel algorithm with existing quantum algorithms for option pricing. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.14115&r=cmp |
| By: | Jérôme Baray (ARGUMans - Laboratoire de recherche en gestion Le Mans Université - UM - Le Mans Université); Alain Decrop (FUNDP - Facultés Universitaires Notre Dame de la Paix); Gérard Cliquet (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | This study focuses on evaluating the performance of artificial intelligence (AI), with a particular emphasis on ChatGPT, in the context of marketing research. Following a literature review on the use of AI in academic research, we developed a standardized methodology to assess the effectiveness of AI in all phases of research, from idea generation to article writing. Through tests based on realistic research scenarios and focused on the hot topics identified by the French Marketing Association (afm), we evaluated ChatGPT's ability to generate relevant, reliable, and valid information for each stage. Our findings reveal that ChatGPT is particularly effective at proposing research questions, synthesizing information, analyzing qualitative data, and assisting in the structuring of articles. The study also highlights the limitations and challenges associated with the use of AI, particularly in terms of ethical considerations and potential biases |
| Abstract: | Cette étude se concentre sur l'évaluation des performances de l'intelligence artificielle (IA), avec un accent particulier sur ChatGPT, dans le cadre de la recherche en marketing. Suite à une revue de la littérature sur l'emploi de l'IA en recherche académique, nous avons développé une méthodologie standardisée pour évaluer l'efficacité de l'IA dans toutes les phases de la recherche, de la génération d'idées à la rédaction d'articles. À travers des tests basés sur des scénarios réalistes de recherche et axés sur les sujets brûlants identifiés par l'Association Française du Marketing (afm), nous avons évalué la capacité de ChatGPT à générer des informations pertinentes, fiables et valables pour chaque étape. Nos découvertes révèlent que ChatGPT est particulièrement efficace pour proposer des questions de recherche, synthétiser des informations, analyser des données qualitatives et aider à la structuration d'articles. L'étude met également en lumière les limitations et les défis associés à l'utilisation de l'IA, en particulier en ce qui concerne les questions éthiques et les biais potentiels. |
| Keywords: | AI, research process, marketing research, evaluation methods, ChatGPT, IA, processus de recherche, recherche en marketing, méthodes d'évaluation, intelligence artificielle, IA générative |
| Date: | 2024–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04547450&r=cmp |
| By: | Douglas Kiarelly Godoy de Araujo |
| Abstract: | Synthetic control methods are a data-driven way to calculate counterfactuals from control individuals for the estimation of treatment effects in many settings of empirical importance. In canonical implementations, this weighting is linear and the key methodological steps of donor pool selection and covariate comparison between the treated entity and its synthetic control depend on some degree of subjective judgment. Thus current methods may not perform best in settings with large datasets or when the best synthetic control is obtained by a nonlinear combination of donor pool individuals. This paper proposes "machine controls", synthetic controls based on automated donor pool selection through clustering algorithms, supervised learning for flexible non-linear weighting of control entities and manifold learning to confirm numerically whether the synthetic control indeed resembles the target unit. The machine controls method is demonstrated with the effect of the 2017 labour deregulation on worker productivity in Brazil. Contrary to policymaker expectations at the time of enactment of the reform, there is no discernible effect on worker productivity. This result points to the deep challenges in increasing the level of productivity, and with it, economic welfare. |
| Keywords: | causal inference, synthetic controls, machine learning, labour reforms, productivity |
| JEL: | B41 C32 C54 E24 J50 J83 O47 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1181&r=cmp |