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on Computational Economics |
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Issue of 2022‒09‒26
23 papers chosen by |
| By: | Kang Gao; Perukrishnen Vytelingum; Stephen Weston; Wayne Luk; Ce Guo |
| Abstract: | This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.14207&r= |
| By: | Zheng Cao; Wenyu Du; Kirill V. Golubnichiy |
| Abstract: | This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.14385&r= |
| By: | Kang Gao; Perukrishnen Vytelingum; Stephen Weston; Wayne Luk; Ce Guo |
| Abstract: | This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, aiming at precipitating mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.13654&r= |
| By: | Taylan Kabbani; Ekrem Duman |
| Abstract: | Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07165&r= |
| By: | Ricard Durall |
| Abstract: | Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07158&r= |
| By: | Theis Ingerslev Jensen (Copenhagen Business School); Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Lasse Heje Pedersen (AQR Capital Management, LLC; Copenhagen Business School - Department of Finance; New York University (NYU); Centre for Economic Policy Research (CEPR)) |
| Abstract: | We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of "economic feature importance." |
| Keywords: | asset pricing, machine learning, transaction costs, economic significance, investments |
| JEL: | C5 C61 G00 G11 G12 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2263&r= |
| By: | James T. E. Chapman; Ajit Desai |
| Abstract: | Predicting the economy's short-term dynamics -- a vital input to economic agents' decision-making process -- often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods. This paper aims to demonstrate that non-traditional and timely data such as retail and wholesale payments, with the aid of nonlinear machine learning approaches, can provide policymakers with sophisticated models to accurately estimate key macroeconomic indicators in near real-time. Moreover, we provide a set of econometric tools to mitigate overfitting and interpretability challenges in machine learning models to improve their effectiveness for policy use. Our models with payments data, nonlinear methods, and tailored cross-validation approaches help improve macroeconomic nowcasting accuracy up to 40\% -- with higher gains during the COVID-19 period. We observe that the contribution of payments data for economic predictions is small and linear during low and normal growth periods. However, the payments data contribution is large, asymmetrical, and nonlinear during strong negative or positive growth periods. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.00948&r= |
| By: | S\'andor Kuns\'agi-M\'at\'e; G\'abor F\'ath; Istv\'an Csabai; G\'abor Moln\'ar-S\'aska |
| Abstract: | Recent studies have demonstrated the efficiency of Variational Autoencoders (VAE) to compress high-dimensional implied volatility surfaces. The encoder part of the VAE plays the role of a calibration operation which maps the vol surface into a low dimensional latent space representing the most relevant implicit model parameters. The decoder part of the VAE performs a pricing operation and reconstructs the vol surface from the latent (model) space. Since this decoder module predicts volatilities of vanilla options directly, it does not provide any explicit information about the dynamics of the underlying asset. It is unclear how the latent model could be used to price exotic, non-vanilla options. In this paper we demonstrate an effective way to overcome this problem. We use a Weighted Monte Carlo approach to first generate paths from a simple a priori Brownian dynamics, and then calculate path weights to price options correctly. We develop and successfully train a neural network that is able to assign these weights directly from the latent space. Combining the encoder network of the VAE and this new "weight assigner" module we are able to build a dynamic pricing framework which cleanses the volatility surface from irrelevant noise fluctuations, and then can price not just vanillas, but also exotic options on this idealized vol surface. This pricing method can provide relative value signals for option traders as well. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.14038&r= |
| By: | Peter B. Dixon; Maureen T. Rimmer |
| Abstract: | Since 2018, we have built a series of GTAP models for Global Affairs Canada (GAC). Each of these models introduces modifications to the standard GTAP model. This paper describes GTAP-GAC3 in which we add an FDI extension. Our main focus is on the role of foreignaffiliate production as a substitute for imports and thereby a method for getting behind a tariff wall. GTAP-GAC3 could also be used for investigating scenarios in which FDI is motivated by productivity effects. Relative to other CGE models that incorporate FDI, GTAP-GAC3 has several advantages, including: year-on-year dynamics; realistic labour-market responses and high levels of commodity and country disaggregation. As explained in the paper, these advantages are achieved mainly by simplifications in demand-side specifications relative to those in other FDI-extended CGE models. By comparing GTAP-GAC3 tariff simulations conducted without and with the FDI extension, we show that FDI can have significant implications for simulation results. |
| Keywords: | Foreign direct investment, computable general equilibrium modelling, GTAP model |
| JEL: | C68 F21 F23 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:cop:wpaper:g-333&r= |
| By: | Jaydip Sen; Arpit Awad; Aaditya Raj; Gourav Ray; Pusparna Chakraborty; Sanket Das; Subhasmita Mishra |
| Abstract: | The stock market offers a platform where people buy and sell shares of publicly listed companies. Generally, stock prices are quite volatile; hence predicting them is a daunting task. There is still much research going to develop more accuracy in stock price prediction. Portfolio construction refers to the allocation of different sector stocks optimally to achieve a maximum return by taking a minimum risk. A good portfolio can help investors earn maximum profit by taking a minimum risk. Beginning with Dow Jones Theory a lot of advancement has happened in the area of building efficient portfolios. In this project, we have tried to predict the future value of a few stocks from six important sectors of the Indian economy and also built a portfolio. As part of the project, our team has conducted a study of the performance of various Time series, machine learning, and deep learning models in stock price prediction on selected stocks from the chosen six important sectors of the economy. As part of building an efficient portfolio, we have studied multiple portfolio optimization theories beginning with the Modern Portfolio theory. We have built a minimum variance portfolio and optimal risk portfolio for all the six chosen sectors by using the daily stock prices over the past five years as training data and have also conducted back testing to check the performance of the portfolio. We look forward to continuing our study in the area of stock price prediction and asset allocation and consider this project as the first stepping stone. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07166&r= |
| By: | Angun, Ebru; Kleijnen, Jack (Tilburg University, Center For Economic Research) |
| Keywords: | Simulation; design of experiments; simulation; statistical analysis; artificial intelligence; computational- experiments; inventory-production |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiucen:7215b496-e653-434e-bde4-582e02dc9532&r= |
| By: | Henri Arno; Klaas Mulier; Joke Baeck; Thomas Demeester |
| Abstract: | Models for bankruptcy prediction are useful in several real-world scenarios, and multiple research contributions have been devoted to the task, based on structured (numerical) as well as unstructured (textual) data. However, the lack of a common benchmark dataset and evaluation strategy impedes the objective comparison between models. This paper introduces such a benchmark for the unstructured data scenario, based on novel and established datasets, in order to stimulate further research into the task. We describe and evaluate several classical and neural baseline models, and discuss benefits and flaws of different strategies. In particular, we find that a lightweight bag-of-words model based on static in-domain word representations obtains surprisingly good results, especially when taking textual data from several years into account. These results are critically assessed, and discussed in light of particular aspects of the data and the task. All code to replicate the data and experimental results will be released. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.11334&r= |
| By: | Samuel Palmer; Konstantinos Karagiannis; Adam Florence; Asier Rodriguez; Roman Orus; Harish Naik; Samuel Mugel |
| Abstract: | In this work, we demonstrate how to apply non-linear cardinality constraints, important for real-world asset management, to quantum portfolio optimization. This enables us to tackle non-convex portfolio optimization problems using quantum annealing that would otherwise be challenging for classical algorithms. Being able to use cardinality constraints for portfolio optimization opens the doors to new applications for creating innovative portfolios and exchange-traded-funds (ETFs). We apply the methodology to the practical problem of enhanced index tracking and are able to construct smaller portfolios that significantly outperform the risk profile of the target index whilst retaining high degrees of tracking. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.11380&r= |
| By: | Jun Lu; Danny Ding |
| Abstract: | Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.07159&r= |
| By: | Yacine Aït-Sahalia; Jianqing Fan; Lirong Xue; Yifeng Zhou |
| Abstract: | This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent, predictability in high frequency returns and durations is large, systematic and pervasive over short horizons. We identify the relevant predictors constructed from trades and quotes data and examine what determines the variation in predictability across different stock's own characteristics and market environments. Next, we compute how the predictability improves with the timeliness of the data on a scale of milliseconds, providing a valuation of each millisecond gained. Finally, we simulate the impact of getting an (imperfect) peek at the incoming order flow, a look ahead ability that is often attributed to the fastest high frequency traders, in terms of improving the predictability of the following returns and durations. |
| JEL: | C45 C53 C58 G12 G14 G17 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30366&r= |
| By: | Müller, Henrik; Rieger, Jonas; Schmidt, Tobias; Hornig, Nico |
| Keywords: | latent Dirichlet allocation,inflation,expectations,narratives,text mining,computational methods,behavioral economics |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:docmaw:12&r= |
| By: | Yoonsik Hong; Yanghoon Kim; Jeonghun Kim; Yongmin Choi |
| Abstract: | A significant number of equity funds are preferred by index funds nowadays, and market sensitivities are instrumental in managing them. Index funds might replicate the index identically, which is, however, cost-ineffective and impractical. Moreover, to utilize market sensitivities to replicate the index partially, they must be predicted or estimated accurately. Accordingly, first, we examine deep learning models to predict market sensitivities. Also, we present pragmatic applications of data processing methods to aid training and generate target data for the prediction. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes' efficacy is corroborated by the Korea Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations, and competitive tracking errors of replicating the index using fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study that addresses market sensitivities focused on deep learning. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.00780&r= |
| By: | Ercio Munoz (The World Bank) |
| Abstract: | Ex-ante evaluation of the distributional effects of a macroeconomic shock is a difficult task. One approach relies on microsimulation models often combined with a macroeconomic model (e.g., a CGE model). This approach typically follows a top-down sequence where the microsimulation model takes the outputs from the macroeconomic model as given and then uses a household survey to generate changes in the data that mimic the resulting macroeconomic aggregates. For example, this approach could be used to model how changes in the level of employment and wages by industry derived from a given macroeconomic scenario (e.g., a set of climate change policies) impact poverty and inequality. This presentation compares two methods (reweighting versus modeling occupational choices) for analyzing changes in the labor market in the context of a top-down macro–micro model. I use two surveys that are more than 10 years apart to explore how these two different ways of modeling changes in the labor market using the older survey can predict what we observe in the newer survey. |
| Date: | 2022–08–11 |
| URL: | http://d.repec.org/n?u=RePEc:boc:usug22:12&r= |
| By: | Sojung Kim; Marcel Kleiber; Stefan Weber |
| Abstract: | The paper develops a methodology to enable microscopic models of transportation systems to be accessible for a statistical study of traffic accidents. Our approach is intended to permit an understanding not only of historical losses, but also of incidents that may occur in altered, potential future systems. Through this, it is possible, from both an engineering and insurance perspective, to assess changes in the design of vehicles and transport systems in terms of their impact on functionality and road safety. Structurally, we characterize the total loss distribution approximatively as a mean-variance mixture. This also yields valuation procedures that can be used instead of Monte Carlo simulation. Specifically, we construct an implementation based on the open-source traffic simulator SUMO and illustrate the potential of the approach in counterfactual case studies. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.12530&r= |
| By: | Angarita, Juan Sebastián; Sandoval, Carlos |
| Abstract: | En este documento se presentan los pasos para la aplicación de un modelo de simulación de la movilidad sostenible. Esta herramienta permite desarrollar de manera interactiva escenarios futuros de movilidad que integren variables de diferentes áreas temáticas, para, de ese modo, visualizar las interrelaciones entre dimensiones del desarrollo y sus consecuencias en la movilidad de una ciudad. El uso del modelo permite identificar, mediante una interfaz visual de fácil operación, los impactos indirectos de ciertas variables o los efectos en el largo plazo de la aplicación de políticas, programas o proyectos sobre movilidad urbana. El modelo está disponible para ser usado en procesos prospectivos o de planificación urbana y forma parte de la caja de herramientas prospectivas para la movilidad sostenible. |
| Keywords: | CIUDADES, DESARROLLO URBANO, TRANSPORTE URBANO, INFRAESTRUCTURA DEL TRANSPORTE, ASPECTOS AMBIENTALES, DESARROLLO SOSTENIBLE, METODOS DE SIMULACION, TENDENCIAS DEL DESARROLLO, CITIES, URBAN DEVELOPMENT, URBAN TRANSPORT, TRANSPORT INFRASTRUCTURE, ENVIRONMENTAL ASPECTS, SUSTAINABLE DEVELOPMENT, SIMULATION METHODS, DEVELOPMENT TRENDS |
| Date: | 2022–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:ecr:col022:48001&r= |
| By: | Artés, Joaquín; Kaufman, Aaron Russell; Richter, Brian Kelleher; Timmons, Jeffrey F. |
| Abstract: | We provide the first evidence that firms, not just voters, are gerrymandered. We compare allocations of firms in enacted redistricting plans to counterfactual distributions constructed using simulation methods. We find that firms are over-allocated to districts held by the mapmakers' party when partisans control the redistricting process. Firms are more proportionately allocated by redistricting commissions. Our results hold when we account for the gerrymandering of seats: holding fixed the number of seats the mapmakers' party wins, firms tend to obtain more firms than expected. Our research reveals that partisan mapmakers target more than just voters. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cbscwp:320&r= |
| By: | Pieter Van Rymenant; Freddy Heylen; Dirk Van de gaer (-) |
| Abstract: | This paper studies the effects of the sharp decline since 1980 in U.S. federal estate taxes on the past and future evolution of per capita growth, labor supply, the wealth-to-GDP ratio (capital-output ratio), the real interest rate, and cross-sectional wealth inequality and concentration. To do so, we construct, calibrate, and simulate a dynamic general equilibrium model featuring firms, a fiscal government, and overlapping generations of heterogeneous households connected via bequests and inter-vivos transfers. The model includes crucial elements in the debate on the effects of estate tax changes and accounts for structural developments in recent decades, such as demographic change and ‘skill-biased’ technological progress. It replicates key U.S. data since the 1960s quite well. We find that the studied estate tax reforms have not generated the desired positive effects on labor supply, private capital formation, and economic activity. Rather, they have contributed considerably to rising aftertax wealth inequality and concentration and explain a fraction of the long-term decline in the real interest rate. The key underlying result from our simulations is that the aggregate stocks of pre-tax wealth and pre-tax bequests are insensitive to changes in the estate tax, even when all households have an after-tax bequest motive. As a result, the foregone estate tax revenues are large. |
| Keywords: | Wealth inequality, economic growth, bequests, estate tax, OLG model |
| JEL: | E17 E21 E27 E62 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:22/1052&r= |
| By: | Kleijnen, Jack (Tilburg University, School of Economics and Management); van Nieuwenhuyse, I.; van Beers, W.C.M. (Tilburg University, School of Economics and Management) |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:903e51c8-bed3-4e97-990f-c052a3686676&r= |