|
on Computational Economics |
|
Issue of 2023‒07‒31
25 papers chosen by |
| By: | Juan Esteban Berger |
| Abstract: | Researchers have been using Neural Networks and other related machine-learning techniques to price options since the early 1990s. After three decades of improvements in machine learning techniques, computational processing power, cloud computing, and data availability, this paper is able to provide a comparison of using Google Cloud's AutoML Regressor, TensorFlow Neural Networks, and XGBoost Gradient Boosting Decision Trees for pricing European Options. All three types of models were able to outperform the Black Scholes Model in terms of mean absolute error. These results showcase the potential of using historical data from an option's underlying asset for pricing European options, especially when using machine learning algorithms that learn complex patterns that traditional parametric models do not take into account. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.00476&r=cmp |
| By: | David Noel |
| Abstract: | This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.12969&r=cmp |
| By: | Gerhard Hellstern; Vanessa Dehn; Martin Zaefferer |
| Abstract: | The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat the feature selection task as a quadratic unconstrained optimization problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. We compare the different results in small-sized problem setups. According to the results of our study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, we compare the convergence behavior of the QUBO methods via quantum computing with classical stochastic optimization methods. Due to persisting error rates, the classical stochastic optimization methods are still superior. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.10591&r=cmp |
| By: | Marc Chen; Mohammad Shirazi; Peter A. Forsyth; Yuying Li |
| Abstract: | We propose a novel data-driven neural network (NN) optimization framework for solving an optimal stochastic control problem under stochastic constraints. Customized activation functions for the output layers of the NN are applied, which permits training via standard unconstrained optimization. The optimal solution yields a multi-period asset allocation and decumulation strategy for a holder of a defined contribution (DC) pension plan. The objective function of the optimal control problem is based on expected wealth withdrawn (EW) and expected shortfall (ES) that directly targets left-tail risk. The stochastic bound constraints enforce a guaranteed minimum withdrawal each year. We demonstrate that the data-driven approach is capable of learning a near-optimal solution by benchmarking it against the numerical results from a Hamilton-Jacobi-Bellman (HJB) Partial Differential Equation (PDE) computational framework. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.10582&r=cmp |
| By: | Stempel, Daniel; Zahner, Johannes |
| Abstract: | In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries. The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. They authors find that a neural network performs best out-of-sample. They use this algorithm to generally classify historical EMU data, and to determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Their findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. They show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend. |
| Keywords: | New Keynesian Models, Monetary Policy, European Monetary Union, Neural Networks, Transfer Learning |
| JEL: | E58 C45 C53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:imfswp:188&r=cmp |
| By: | Pumplun, Luisa; Peters, Felix; Gawlitza, Joshua; Buxmann, Peter |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:138523&r=cmp |
| By: | Marc Velay; Bich-Li\^en Doan; Arpad Rimmel; Fabrice Popineau; Fabrice Daniel |
| Abstract: | Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implies a need for extensive efforts in market representation, behavior objectives, and training processes, which have often been lacking in previous works. We propose a training and evaluation process to assess the performance of classical DRL algorithms for portfolio management. We found that most Deep Reinforcement Learning algorithms were not robust, with strategies generalizing poorly and degrading quickly during backtesting. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.10950&r=cmp |
| By: | Wenbo Ge; Pooia Lalbakhsh; Leigh Isai; Artem Lensky; Hanna Suominen |
| Abstract: | This study aims at comparing several deep learning-based forecasters in the task of volatility prediction using multivariate data, proceeding from simpler or shallower to deeper and more complex models and compare them to the naive prediction and variations of classical GARCH models. Specifically, the volatility of five assets (i.e., S\&P500, NASDAQ100, gold, silver, and oil) was predicted with the GARCH models, Multi-Layer Perceptrons, recurrent neural networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In most cases the Temporal Fusion Transformer followed by variants of Temporal Convolutional Network outperformed classical approaches and shallow networks. These experiments were repeated, and the difference between competing models was shown to be statistically significant, therefore encouraging their use in practice. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.12446&r=cmp |
| By: | Johanna Deperi (University of Brescia); Ludovic Dibiaggio (SKEMA Business School); Mohamed Keita (SKEMA Business School); Lionel Nesta (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po) |
| Abstract: | Artificial intelligence (AI) is viewed as the next technological revolution. The aim of this Policy Brief is to identify France's strengths and weaknesses in this great race for AI innovation. We characterise France's positioning relative to other key players and make the following observations: 1. Without being a world leader in innovation incorporating artificial intelligence, France is showing moderate but significant activity in this field. 2. France specialises in machine learning, unsupervised learning and probabilistic graphical models, and in developing solutions for the medical sciences, transport and security. 3. The AI value chain in France is poorly integrated, mainly due to a lack of integration in the downstream phases of the innovation chain. 4. The limited presence of French private players in the global AI arena contrasts with the extensive involvement of French public institutions. French public research organisations produce patents with great economic value. 5. Public players are the key actors in French networks for collaboration in patent development, but are not open to international and institutional diversity. In our opinion, France runs the risk of becoming a global AI laboratory located upstream in the AI innovation value chain. As such, it is likely to bear the sunk costs of AI invention, without enjoying the benefits of AI exploitation on a larger scale. In short, our fear is that French AI will be exported to other locations to prosper and grow. |
| Date: | 2023–06–26 |
| URL: | http://d.repec.org/n?u=RePEc:hal:spmain:hal-04144817&r=cmp |
| By: | Ellenrieder, Sara; Jourdan, Nicolas; Biegel, Tobias; Bretones Cassoli, Beatriz; Metternich, Joachim; Buxmann, Peter |
| Date: | 2023–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:138521&r=cmp |
| By: | Arnau Quera-Bofarull; Joel Dyer; Anisoara Calinescu; Michael Wooldridge |
| Abstract: | Agent-based models (ABMs) are a promising approach to modelling and reasoning about complex systems, yet their application in practice is impeded by their complexity, discrete nature, and the difficulty of performing parameter inference and optimisation tasks. This in turn has sparked interest in the construction of differentiable ABMs as a strategy for combatting these difficulties, yet a number of challenges remain. In this paper, we discuss and present experiments that highlight some of these challenges, along with potential solutions. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01085&r=cmp |
| By: | Yang Qiao; Yiping Xia; Xiang Li; Zheng Li; Yan Ge |
| Abstract: | Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.15526&r=cmp |
| By: | Joel Ong; Dorien Herremans |
| Abstract: | A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility, such as forecasting realized volatility as measured by different volatility estimators. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio's performance. These findings demonstrate that MTL can be a powerful tool in finance. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.13661&r=cmp |
| By: | Sherly Alfonso-S\'anchez; Jes\'us Solano; Alejandro Correa-Bahnsen; Kristina P. Sendova; Cristi\'an Bravo |
| Abstract: | Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. In particular, because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining an individual's current credit limit. To find this policy, we first formulated this decision-making question as an optimization problem in which the expected profit was maximized; therefore, we balanced two adversarial goals: maximizing the portfolio's revenue and minimizing the portfolio's provisions. Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app (i.e., a mobile application that offers various services from goods deliveries to financial products) in Latin America to train our reinforcement learning agent. Our results show that a Double Q-learning agent with optimized hyperparameters can outperform other strategies and generate a non-trivial optimal policy reflecting the complex nature of this decision. Our research not only establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment, presenting an objective technique to make these decisions primarily based on data-driven methods rather than relying only on expert-driven systems but also provides insights into the effect of alternative data usage for determining these modifications. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.15585&r=cmp |
| By: | Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Andrea Roventini (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po) |
| Abstract: | We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first "hard" lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second "mild" lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second "hard" lockdown. |
| Keywords: | Input-output, Covid-19, Lockdown, Italy |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-04103906&r=cmp |
| By: | Yu-Chin Hsu; Martin Huber; Yu-Min Yen |
| Abstract: | We suggest double/debiased machine learning estimators of direct and indirect quantile treatment effects under a selection-on-observables assumption. This permits disentangling the causal effect of a binary treatment at a specific outcome rank into an indirect component that operates through an intermediate variable called mediator and an (unmediated) direct impact. The proposed method is based on the efficient score functions of the cumulative distribution functions of potential outcomes, which are robust to certain misspecifications of the nuisance parameters, i.e., the outcome, treatment, and mediator models. We estimate these nuisance parameters by machine learning and use cross-fitting to reduce overfitting bias in the estimation of direct and indirect quantile treatment effects. We establish uniform consistency and asymptotic normality of our effect estimators. We also propose a multiplier bootstrap for statistical inference and show the validity of the multiplier bootstrap. Finally, we investigate the finite sample performance of our method in a simulation study and apply it to empirical data from the National Job Corp Study to assess the direct and indirect earnings effects of training. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01049&r=cmp |
| By: | Gideon du Rand; Hylton Hollander; Dawie van Lill |
| Abstract: | In this study, we provide a comprehensive estimation of the contemporary Phillips curve relationship in the South African economy using a novel deep learning technique. Our approach incorporates multiple measures of economic slack/tightness and inflation expectations, contributing to the debate on the relevance of the Phillips curve in South Africa, where previous findings have been inconclusive. Our analysis reveals that long-run inflation expectations are the primary driver of inflation, with these expectations anchored around 5% historically but declining since the financial crisis. |
| Keywords: | Inflation, Output gap, Monetary policy |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2023-79&r=cmp |
| By: | Haohan Zhang; Fengrui Hua; Chengjin Xu; Jian Guo; Hao Kong; Ruiting Zuo |
| Abstract: | The rapid advancement of Large Language Models (LLMs) has led to extensive discourse regarding their potential to boost the return of quantitative stock trading strategies. This discourse primarily revolves around harnessing the remarkable comprehension capabilities of LLMs to extract sentiment factors which facilitate informed and high-frequency investment portfolio adjustments. To ensure successful implementations of these LLMs into the analysis of Chinese financial texts and the subsequent trading strategy development within the Chinese stock market, we provide a rigorous and encompassing benchmark as well as a standardized back-testing framework aiming at objectively assessing the efficacy of various types of LLMs in the specialized domain of sentiment factor extraction from Chinese news text data. To illustrate how our benchmark works, we reference three distinctive models: 1) the generative LLM (ChatGPT), 2) the Chinese language-specific pre-trained LLM (Erlangshen-RoBERTa), and 3) the financial domain-specific fine-tuned LLM classifier(Chinese FinBERT). We apply them directly to the task of sentiment factor extraction from large volumes of Chinese news summary texts. We then proceed to building quantitative trading strategies and running back-tests under realistic trading scenarios based on the derived sentiment factors and evaluate their performances with our benchmark. By constructing such a comparative analysis, we invoke the question of what constitutes the most important element for improving a LLM's performance on extracting sentiment factors. And by ensuring that the LLMs are evaluated on the same benchmark, following the same standardized experimental procedures that are designed with sufficient expertise in quantitative trading, we make the first stride toward answering such a question. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.14222&r=cmp |
| By: | Gianluca Cubadda; Francesco Giancaterini; Alain Hecq; Joann Jasiak |
| Abstract: | This paper investigates the performance of the Generalized Covariance estimator (GCov) in estimating mixed causal and noncausal Vector Autoregressive (VAR) models. The GCov estimator is a semi-parametric method that minimizes an objective function without making any assumptions about the error distribution and is based on nonlinear autocovariances to identify the causal and noncausal orders of the mixed VAR. When the number and type of nonlinear autocovariances included in the objective function of a GCov estimator is insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise, resulting in local minima in the objective function of the estimator at parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point, the optimization algorithm may converge to a local minimum, leading to inaccurate estimates. To circumvent this issue, the paper proposes the use of the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that the SA optimization algorithm performs effectively when applied to multivariate mixed VAR models, successfully eliminating the effects of local minima. The approach is illustrated by simulations and an empirical application of a bivariate mixed VAR model with commodity price series. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.14653&r=cmp |
| By: | Abha Naik; Esra Yeniaras; Gerhard Hellstern; Grishma Prasad; Sanjay Kumar Lalta Prasad Vishwakarma |
| Abstract: | In this paper, we provide an overview of the recent work in the quantum finance realm from various perspectives. The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation. Furthermore, we give a comprehensive overview of the applications of quantum computing in the field of blockchain technology which is a main concept in fintech. In that sense, we first introduce the general overview of blockchain with its main cryptographic primitives such as digital signature algorithms, hash functions, and random number generators as well as the security vulnerabilities of blockchain technologies after the merge of quantum computers considering Shor's quantum factoring and Grover's quantum search algorithms. We then discuss the privacy preserving quantum-resistant blockchain systems via threshold signatures, ring signatures, and zero-knowledge proof systems i.e. ZK-SNARKs in quantum resistant blockchains. After emphasizing the difference between the quantum-resistant blockchain and quantum-safe blockchain we mention the security countermeasures to take against the possible quantumized attacks aiming these systems. We finalize our discussion with quantum blockchain, efficient quantum mining and necessary infrastructures for constructing such systems based on quantum computing. This review has the intention to be a bridge to fill the gap between quantum computing and one of its most prominent application realms: Finance. We provide the state-of-the-art results in the intersection of finance and quantum technology for both industrial practitioners and academicians. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01155&r=cmp |
| By: | Mingxiao Song; Yunsong Liu; Agam Shah; Sudheer Chava |
| Abstract: | The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. Amateur traders and retail investors comprise a significant fraction of the NFT market. Hence it is important that researchers highlight the relevant risks involved in NFT trading. In this paper, we attempt to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we design quantitative features from the network, monetary, and temporal perspectives that are fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discuss the clustering results' significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.04643&r=cmp |
| By: | Manuel Alejandro Cardenete; M. Carmen Lima; Ferran Sancho |
| Abstract: | The input-output (I-O) model is a widely employed tool for examining the interconnected structure of an economy and evaluating policy impacts. The current model consists of two separate and independent modules that describe the underlying factors governing quantities and prices. However, these modules lack any form of interaction, existing in isolated spheres where prices do not influence quantities, and quantities do not affect prices. Consequently, the I-O model has been questioned for its limited descriptive capability, particularly when a more comprehensive assessment is necessary. This study aims to enhance the explanatory capabilities of the I-O model by proposing a novel improvement. We introduce an extended version of the traditional I-O price and quantity models, which integrates them into a unified "price-quantity" model, establishing interdependencies between the two modules. This integrated model could be useful in advancing the explanatory capacity of I-O analysis, without having to resort to computational general equilibrium (CGE) models. As we know, CGE models are considerably more complex and resource-intensive in terms of data requirements compared to I-O models. To evaluate the impact of NextGenerationEU funds on the Spanish economy, we apply this integrated I-O model, utilizing data from a Social Accounting Matrix (SAM) for 2016, the latest year with available official I-O data. |
| Keywords: | Price-quantity feedback, Social Accounting data, Impact evaluation |
| JEL: | C67 D57 E37 |
| Date: | 2023–07–07 |
| URL: | http://d.repec.org/n?u=RePEc:aub:autbar:973.23&r=cmp |
| By: | Boyu Zhang; Hongyang Yang; Xiao-Yang Liu |
| Abstract: | Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and fine-tuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.12659&r=cmp |
| By: | Sajedinia, Ehsanreza |
| Keywords: | Resource/Energy Economics and Policy, Environmental Economics and Policy, Agricultural and Food Policy |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea22:335950&r=cmp |
| By: | Cong Zheng; Jiafa He; Can Yang |
| Abstract: | This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.17178&r=cmp |