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on Computational Economics |
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Issue of 2024‒04‒29
seventeen papers chosen by |
| By: | Zhiyuan Yao; Zheng Li; Matthew Thomas; Ionut Florescu |
| Abstract: | Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets. However, traditional rule-based market simulators often fall short in accurately capturing the dynamic behavior of market participants, particularly in response to external market impact events or changes in the behavior of other participants. In this study, we explore an agent-based simulation framework employing reinforcement learning (RL) agents. We present the implementation details of these RL agents and demonstrate that the simulated market exhibits realistic stylized facts observed in real-world markets. Furthermore, we investigate the behavior of RL agents when confronted with external market impacts, such as a flash crash. Our findings shed light on the effectiveness and adaptability of RL-based agents within the simulation, offering insights into their response to significant market events. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19781&r=cmp |
| By: | Ardyn Nordstrom; Morgan Nordstrom; Matthew D. Webb |
| Abstract: | This paper details an innovative methodology to integrate image data into traditional econometric models. Motivated by forecasting sales prices for residential real estate, we harness the power of deep learning to add "information" contained in images as covariates. Specifically, images of homes were categorized and encoded using an ensemble of image classifiers (ResNet-50, VGG16, MobileNet, and Inception V3). Unique features presented within each image were further encoded through panoptic segmentation. Forecasts from a neural network trained on the encoded data results in improved out-of-sample predictive power. We also combine these image-based forecasts with standard hedonic real estate property and location characteristics, resulting in a unified dataset. We show that image-based forecasts increase the accuracy of hedonic forecasts when encoded features are regarded as additional covariates. We also attempt to "explain" which covariates the image-based forecasts are most highly correlated with. The study exemplifies the benefits of interdisciplinary methodologies, merging machine learning and econometrics to harness untapped data sources for more accurate forecasting. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19915&r=cmp |
| By: | Qishuo Cheng |
| Abstract: | In recent decades, financial quantification has emerged and matured rapidly. For financial institutions such as funds, investment institutions are increasingly dissatisfied with the situation of passively constructing investment portfolios with average market returns, and are paying more and more attention to active quantitative strategy investment portfolios. This requires the introduction of active stock investment fund management models. Currently, in my country's stock fund investment market, there are many active quantitative investment strategies, and the algorithms used vary widely, such as SVM, random forest, RNN recurrent memory network, etc. This article focuses on this trend, using the emerging LSTM-GRU gate-controlled long short-term memory network model in the field of financial stock investment as a basis to build a set of active investment stock strategies, and combining it with SVM, which has been widely used in the field of quantitative stock investment. Comparing models such as RNN, theoretically speaking, compared to SVM that simply relies on kernel functions for high-order mapping and classification of data, neural network algorithms such as RNN and LSTM-GRU have better principles and are more suitable for processing financial stock data. Then, through multiple By comparison, it was finally found that the LSTM- GRU gate-controlled long short-term memory network has a better accuracy. By selecting the LSTM-GRU algorithm to construct a trading strategy based on the Shanghai and Shenzhen 300 Index constituent stocks, the parameters were adjusted and the neural layer connection was adjusted. Finally, It has significantly outperformed the benchmark index CSI 300 over the long term. The conclusion of this article is that the research results can provide certain quantitative strategy references for financial institutions to construct active stock investment portfolios. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.01624&r=cmp |
| By: | Florian Krach; Josef Teichmann; Hanna Wutte |
| Abstract: | Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve robust utility optimization problems in general and realistic settings. In particular, we model both the investor and the market by neural networks (NN) and train them in a mini-max zero-sum game. This approach is applicable for any continuous utility function and in realistic market settings with trading costs, where only observable information of the market can be used. A large empirical study shows the versatile usability of our method. Whenever an optimal reference strategy is available, our method performs on par with it and in the (many) settings without known optimal strategy, our method outperforms all other reference strategies. Moreover, we can conclude from our study that the trained path-dependent strategies do not outperform Markovian ones. Lastly, we uncover that our generative approach for learning optimal, (non-) robust investments under trading costs generates universally applicable alternatives to well known asymptotic strategies of idealized settings. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15243&r=cmp |
| By: | Joshua S. Gans |
| Abstract: | This paper surveys the relevant existing literature that can help researchers and policy makers understand the drivers of competition in markets that constitute the provision of artificial intelligence products. The focus is on three broad markets: training data, input data, and AI predictions. It is shown that a key factor in determining the emergence and persistence of market power will be the operation of markets for data that would allow for trading data across firm boundaries. |
| JEL: | L15 L40 O34 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32270&r=cmp |
| By: | Muriel, Juan E.; Zhang, Lele; Fransoo, Jan C. (Tilburg University, School of Economics and Management); Villegas, Juan G. |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:b3811dad-50fa-486b-8255-3b3f9e9f4003&r=cmp |
| By: | Holtemöller, Oliver; Kozyrev, Boris |
| Abstract: | In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between "normal" times and situations where the time-series behavior is very different from "normal" times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately. |
| Keywords: | forecasting, neural network, nowcasting, time series models |
| JEL: | C22 C45 C53 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:287749&r=cmp |
| By: | Taejin Park |
| Abstract: | This paper introduces a Large Language Model (LLM)-based multi-agent framework designed to enhance anomaly detection within financial market data, tackling the longstanding challenge of manually verifying system-generated anomaly alerts. The framework harnesses a collaborative network of AI agents, each specialised in distinct functions including data conversion, expert analysis via web research, institutional knowledge utilization or cross-checking and report consolidation and management roles. By coordinating these agents towards a common objective, the framework provides a comprehensive and automated approach for validating and interpreting financial data anomalies. I analyse the S&P 500 index to demonstrate the framework's proficiency in enhancing the efficiency, accuracy and reduction of human intervention in financial market monitoring. The integration of AI's autonomous functionalities with established analytical methods not only underscores the framework's effectiveness in anomaly detection but also signals its broader applicability in supporting financial market monitoring. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19735&r=cmp |
| By: | S\'ebastien Bossu (LPSM, UPCit\'e); St\'ephane Cr\'epey (LPSM, UPCit\'e); Hoang-Dung Nguyen (LPSM, UPCit\'e) |
| Abstract: | We propose a distributional formulation of the spanning problem of a multi-asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier-based formula to calculate the solution. Financial payoffs are typically piecewise linear, resulting in a solution that may be derived explicitly, yet may also be hard to numerically exploit. One-hidden-layer feedforward neural networks instead provide a natural and efficient numerical alternative for discrete spanning. We test this approach for a selection of archetypal payoffs and obtain better hedging results with vanilla basket options compared to industry-favored approaches based on single-asset vanilla hedges. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.14231&r=cmp |
| By: | Boming Ning; Kiseop Lee |
| Abstract: | Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spread of paired stocks. Studies on this strategy often rely heavily on model assumption. In this study, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.12180&r=cmp |
| By: | Divyanshu Daiya; Monika Yadav; Harshit Singh Rao |
| Abstract: | In this work, we propose an approach to generalize denoising diffusion probabilistic models for stock market predictions and portfolio management. Present works have demonstrated the efficacy of modeling interstock relations for market time-series forecasting and utilized Graph-based learning models for value prediction and portfolio management. Though convincing, these deterministic approaches still fall short of handling uncertainties i.e., due to the low signal-to-noise ratio of the financial data, it is quite challenging to learn effective deterministic models. Since the probabilistic methods have shown to effectively emulate higher uncertainties for time-series predictions. To this end, we showcase effective utilisation of Denoising Diffusion Probabilistic Models (DDPM), to develop an architecture for providing better market predictions conditioned on the historical financial indicators and inter-stock relations. Additionally, we also provide a novel deterministic architecture MaTCHS which uses Masked Relational Transformer(MRT) to exploit inter-stock relations along with historical stock features. We demonstrate that our model achieves SOTA performance for movement predication and Portfolio management. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.14063&r=cmp |
| By: | Mariarosaria Comunale; Andrea Manera |
| Abstract: | We review the literature on the effects of Artificial Intelligence (AI) adoption and the ongoing regulatory efforts concerning this technology. Economic research encompasses growth, employment, productivity, and income inequality effects, while regulation covers market competition, data privacy, copyright, national security, ethics concerns, and financial stability. We find that: (i) theoretical research agrees that AI will affect most occupations and transform growth, but empirical findings are inconclusive on employment and productivity effects; (ii) regulation has focused primarily on topics not explored by the academic literature; (iii) across countries, regulations differ widely in scope and approaches and face difficult trade-offs. |
| Keywords: | Artificial Intelligence (AI); labor market; task exposure; productivity; regulation; governance. |
| Date: | 2024–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/065&r=cmp |
| By: | Benjamin Bittschi (WIFO); Thomas Horvath; Helmut Mahringer (WIFO); Christine Mayrhuber (WIFO); Martin Spielauer (WIFO); Philipp Warum (WIFO) |
| Abstract: | The aim of this study is to assess the impact of the ongoing harmonisation of the retirement age for women with that for men on women's labour supply in Austria. According to the current legal framework, the standard retirement age for women will be gradually raised from 60 to 65 years from 2024 onwards, with the retirement age being raised by 6 months each year. The impact of the pension reform on women's labour supply is quantified using the dynamic microsimulation model microDEMS. This model integrates demographic changes in line with official population projections and detailed labour market modelling. According to our projections, the labour supply of women aged 60 to 64 increases by 87, 000 in 2040 compared to a scenario in which the retirement age remains unchanged. We compare our results with two alternative approaches: the more stylised microWELT simulation model and a purely data-driven approach. While all methods produce very similar results in the long run, the detailed modelling in microDEMS provides more plausible results during the transition period when the reform is gradually implemented. This is because it allows for a realistic representation of pension paths, taking into account all relevant pension types and the corresponding eligibility criteria, such as sufficient accumulated insurance periods. In contrast to a purely data-driven approach, microDEMS modelling also has the advantage of explicitly representing and quantifying the components of the change in labour supply. |
| Keywords: | Dynamic microsimulation, Pension reform, Labour force participation |
| Date: | 2024–03–28 |
| URL: | http://d.repec.org/n?u=RePEc:wfo:wpaper:y:2024:i:673&r=cmp |
| By: | Kaushalya Kularatnam; Tania Stathaki |
| Abstract: | As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large datasets and the continually changing tricks of the trade make it difficult to adapt to new market conditions and detect bad actors. To that end, we propose a framework that can be adapted easily to various problems in the space of detecting market manipulation. Our approach entails initially employing a labelling algorithm which we use to create a training set to learn a weakly supervised model to identify potentially suspicious sequences of order book states. The main goal here is to learn a representation of the order book that can be used to easily compare future events. Subsequently, we posit the incorporation of expert assessment to scrutinize specific flagged order book states. In the event of an expert's unavailability, recourse is taken to the application of a more complex algorithm on the identified suspicious order book states. We then conduct a similarity search between any new representation of the order book against the expert labelled representations to rank the results of the weak learner. We show some preliminary results that are promising to explore further in this direction |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.13429&r=cmp |
| By: | Dai, Yongsheng; Wang, Hui; Rafferty, Karen; Spence, Ivor; Quinn, Barry |
| Abstract: | Time series anomaly detection plays a critical role in various applications, from finance to industrial monitoring. Effective models need to capture both the inherent characteristics of time series data and the unique patterns associated with anomalies. While traditional forecasting-based and reconstruction-based approaches have been successful, they tend to struggle with complex and evolving anomalies. For instance, stock market data exhibits complex and ever-changing fluctuation patterns that defy straightforward modelling. In this paper, we propose a novel approach called TDSRL (Time Series Dual Self-Supervised Representation Learning) for robust anomaly detection. TDSRL leverages synthetic anomaly segments which are artificially generated to simulate real-world anomalies. The key innovation lies in dual self-supervised pretext tasks: one task characterises anomalies in relation to the entire time series, while the other focuses on local anomaly boundaries. Additionally, we introduce a data degradation method that operates in both the time and frequency domains, creating a more natural simulation of real-world anomalies compared to purely synthetic data. Consequently, TDSRL is expected to achieve more accurate predictions of the location and extent of anomalous segments. Our experiments demonstrate that TDSRL outperforms state-of-the-art methods, making it a promising avenue for time series anomaly detection. |
| Keywords: | Time series anomaly detection, self-supervised representation learning, contrastive learning, synthetic anomaly |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:qmsrps:202403&r=cmp |
| By: | Jonathan F. Cogliano and Roberto Veneziani |
| Abstract: | In A Mathematical Formulation of the Ricardian System, Pasinetti (1960) lays out the foundations of what has been dubbed the canonical classical model. He proves the model to be logically consistent and determinate in all its macro-economic features, and derives the solutions for all key variables independently of demand conditions. The model thus provides macroeconomic foundations to the classical theory of distribution. This paper examines the decentralised, competitive mechanism underlying the macroeconomic outcomes. First, we model a classical economy with capitalists, workers, and landlords and define the notion of a Classical Competitive Equilibrium (CCE). A unique CCE exists in a large class of concave classical economies and the resulting income distribution is proved to coincide with that of Pasinetti’s canonical classical model. Second, we use an agent-based model in order to examine more explicitly the decentralised competitive mechanisms at play in the classical economy. We show that a realistic competitive interaction between boundedly rational agents with localised knowledge generates classical gravitational dynamics with the key distributive variables oscillating around their equilibrium values. |
| Keywords: | Luigi Pasinetti, Income distribution, Classical competition, Agent-based model. |
| JEL: | B51 C63 D50 |
| Date: | 2024–04–09 |
| URL: | http://d.repec.org/n?u=RePEc:mab:wpaper:2024-01&r=cmp |
| By: | Thomas Horvath; Martin Spielauer (WIFO); Philipp Warum (WIFO) |
| Abstract: | Capturing the heterogeneity of life courses improves the accuracy, detail and policy relevance of population and labour force projections. Our study uses the microsimulation model microDEMS for Austria, which simulates individual life courses at a high level of detail and in their family context. The model pays particular attention to educational attainment, health and labour market participation. By maintaining the longitudinal consistency of labour market careers, including the tracking of insurance periods, together with the implementation of detailed retirement rules, our model provides realistic representations of retirement decisions. While we reproduce the demographic outcomes of official (Statistics Austria) population projections, including international migration by region of birth, we integrate several additional dimensions, such as educational differentials in mortality and fertility. MicroDEMS allows to consider a wide range of scenarios when assessing the sensitivity of results, or to focus on the impact of policy changes targeted at specific population subgroups, such as mothers, immigrants, or people with health impairments or lower educational levels. MicroDEMS is a detailed national version of the comparative microWELT model. In this context, microDEMS is used for sensitivity analysis and case studies to assess potential specification bias introduced in microWELT due to the neglect of institutional detail or the less detailed treatment of population heterogeneity, such as in the case of international migration. |
| Keywords: | Dynamic microsimulation, Pension reform, Labour force participation |
| Date: | 2024–04–15 |
| URL: | http://d.repec.org/n?u=RePEc:wfo:wpaper:y:2024:i:674&r=cmp |