nep-cmp New Economics Papers
on Computational Economics
Issue of 2020‒04‒20
25 papers chosen by
Stan Miles
Thompson Rivers University

  1. Deep learning for Stock Market Prediction By Mojtaba Nabipour; Pooyan Nayyeri; Hamed Jabani; Amir Mosavi
  2. EFFECTS OF TERMS OF TRADE SHOCKS ON THE RUSSIAN ECONOMY By Natalia Turdyeva
  3. Exact Simulation of Variance Gamma related OU processes: Application to the Pricing of Energy Derivatives By Piergiacomo Sabino
  4. Social media and price discovery: the case of cross-listed firms By Rui Fan; Oleksandr Talavera; Vu Tran
  5. An Application of Deep Reinforcement Learning to Algorithmic Trading By Thibaut Th\'eate; Damien Ernst
  6. Company classification using machine learning By Sven Husmann; Antoniya Shivarova; Rick Steinert
  7. Machine Learning Algorithms for Financial Asset Price Forecasting By Philip Ndikum
  8. Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics By Amir Mosavi; Pedram Ghamisi; Yaser Faghan; Puhong Duan
  9. Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making By Jonathan Sadighian
  10. Deep Probabilistic Modelling of Price Movements for High-Frequency Trading By Ye-Sheen Lim; Denise Gorse
  11. Information Token Driven Machine Learning for Electronic Markets: Performance Effects in Behavioral Financial Big Data Analytics By Jim Samuel
  12. A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels By Hannes Wallimann; David Imhof; Martin Huber
  13. Extensions of Random Orthogonal Matrix Simulation for Targetting Kollo Skewness By Carol Alexander; Xiaochun Meng; Wei Wei
  14. Financial Market Trend Forecasting and Performance Analysis Using LSTM By Jonghyeon Min
  15. A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding By Ioannis Boukas; Damien Ernst; Thibaut Th\'eate; Adrien Bolland; Alexandre Huynen; Martin Buchwald; Christelle Wynants; Bertrand Corn\'elusse
  16. An economic analysis of the US-China trade conflict By Bekkers, Eddy; Schroeter, Sofia
  17. Networking topography and default contagion in China’s financial system By Fittje, Jens; Wagner, Helmut
  18. Rational Heuristics? Expectations and Behaviors in Evolving Economies with Heterogeneous Interacting Agents By Giovanni Dosi; Mauro Napoletano; Andrea Roventini; Joseph E. Stiglitz; Tania Treibich
  19. Manipulation-Proof Machine Learning By Daniel Bj\"orkegren; Joshua E. Blumenstock; Samsun Knight
  20. Computational Complexity of the Hylland-Zeckhauser Scheme for One-Sided Matching Markets By Vijay V. Vazirani; Mihalis Yannakakis
  21. Health Risk and the Welfare Effects of Social Security By Shantanu Bagchi; Juergen Jung
  22. What matters in funding: The value of research coherence and alignment in evaluators' decisions By Ayoubi, Charles; Barbosu, Sandra; Pezzoni, Michele; Visentin, Fabiana
  23. Globalization in the Time of COVID-19 By Alessandro Sforza; Marina Steininger
  24. Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large scale agent-based model By Kirill S. Glavatskiy; Mikhail Prokopenko; Adrian Carro; Paul Ormerod; Michael Harre
  25. How should a points pension system be managed? By Antoine Bozio; Simon Rabaté; Audrey Rain; Maxime Tô

  1. By: Mojtaba Nabipour; Pooyan Nayyeri; Hamed Jabani; Amir Mosavi
    Abstract: Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01497&r=all
  2. By: Natalia Turdyeva
    Abstract: The principal interest of the paper is the quantification of terms of trade shocks response of the Russian economy on a detailed computable general equilibrium (CGE) model calibrated with Russian input-output data. The results suggest a decrease of welfare of the representative consumer and real GDP with the deterioration of the terms of trade. In the Central scenario (a 10% decrease in the world price of crude oil, a 3% decrease in the world price of natural gas and an 8% decrease in the world price of petroleum products) welfare of the representative consumer decreases by -1,17% of benchmark consumption level or -0,58% of the base year GDP in the comparative static model. Percentage change of the GDP in the Central scenario of the comparative static model is of the same magnitude as decrease in representative consumer’s welfare in terms of the benchmark GDP: -1,55%. Welfare changes associated with the Central scenario of the steady-state model, where capital stock adjusts to its long-term level, indicate a significant decrease in the welfare of the representative consumer up to -2,64% of benchmark consumption level or -1,23% of the base year GDP. Percentage change of the GDP in the Central scenario of the steady-state model exceeds representative consumer’s decrease in welfare in terms of the benchmark GDP: -2,51%. The model was validated by historical simulation with observed levels of exogenous parameters, mimicking change in economic environment from 2011 to 2015. The results of the historical simulation stress the importance of fiscal parameters (i.e. export taxes) in analysis of production behaviour of Russian extraction industries.
    Keywords: terms of trade, oil price shock, computable general equilibrium models, input-output table, industry output; CGE model validation.
    JEL: F17 C68 D58
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:bkr:wpaper:wps48&r=all
  3. By: Piergiacomo Sabino
    Abstract: In this study we define a three-step procedure to relate the self-decomposability of the stationary law of a generalized Ornstein-Uhlenbeck process to the law of the increments of such processes. Based on this procedure and the results of Qu et al. (2019), we derive the exact simulation, without numerical inversion, of the skeleton of a Variance Gamma, and of a symmetric Variance Gamma driven Ornstein-Uhlenbeck process. Extensive numerical experiments are reported to demonstrate the accuracy and efficiency of our algorithms. These results are instrumental to simulate the spot price dynamics in energy markets and to price Asian options and gas storages by Monte Carlo simulations in a framework similar to the one discussed in Cummins et al. (2017, 2018).
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.06786&r=all
  4. By: Rui Fan (Swansea University); Oleksandr Talavera (University of Birmingham); Vu Tran (University of Reading)
    Abstract: This paper examines whether social media information affects the price discovery process for cross-listed companies. Using over 29 million overnight tweets mentioning cross-listed companies, we investigate the role of social media for the linkage between the last periods of trading in the US markets and the first periods in the UK market. Our estimates suggest that the size and content of information flows in social networks support the price discovery process. The interactions between lagged US stock features and overnight tweets significantly affect stock returns and volatility of cross-listed stocks when the UK market opens. These effects weaken and disappear after one to three hours after the UK market opening. We also develop a profitable trading strategy based on overnight social media, and the profits remain economically significant after considering transaction costs.
    Keywords: Twitter, investor sentiment, cross-listed stocks, text classification, computational linguistics
    JEL: G12 G14 L86
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:bir:birmec:20-05&r=all
  5. By: Thibaut Th\'eate; Damien Ernst
    Abstract: This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.06627&r=all
  6. By: Sven Husmann; Antoniya Shivarova; Rick Steinert
    Abstract: The recent advancements in computational power and machine learning algorithms have led to vast improvements in manifold areas of research. Especially in finance, the application of machine learning enables researchers to gain new insights into well-studied areas. In our paper, we demonstrate that unsupervised machine learning algorithms can be used to visualize and classify company data in an economically meaningful and effective way. In particular, we implement the t-distributed stochastic neighbor embedding (t-SNE) algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool in combination with spectral clustering to perform company classification. The resulting groups can then be implemented by experts in the field for empirical analysis and optimal decision making. By providing an exemplary out-of-sample study within a portfolio optimization framework, we show that meaningful grouping of stock data improves the overall portfolio performance. We, therefore, introduce the t-SNE algorithm to the financial community as a valuable technique both for researchers and practitioners.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01496&r=all
  7. By: Philip Ndikum
    Abstract: This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01504&r=all
  8. By: Amir Mosavi; Pedram Ghamisi; Yaser Faghan; Puhong Duan
    Abstract: The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01509&r=all
  9. By: Jonathan Sadighian
    Abstract: There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.06985&r=all
  10. By: Ye-Sheen Lim; Denise Gorse
    Abstract: In this paper we propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices, important for the risk management of automated trading systems. Our proposed architecture incorporates probabilistic mixture models into deep recurrent neural networks. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies that were previously neglected in the literature: 1) probabilistic forecasting of the price movements; 2) single objective prediction of both the direction and size of the price movements. We train our models on high-frequency Bitcoin market data and evaluate them against benchmark models obtained from the literature. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01498&r=all
  11. By: Jim Samuel
    Abstract: Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of big-data phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned- ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions. Keywords: Information; Big Data; Electronic Markets; Analytics; Behavior
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.06642&r=all
  12. By: Hannes Wallimann; David Imhof; Martin Huber
    Abstract: We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging. We demonstrate that our algorithm outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.05629&r=all
  13. By: Carol Alexander; Xiaochun Meng; Wei Wei
    Abstract: Modelling multivariate systems is important for many applications in engineering and operational research. The multivariate distributions under scrutiny usually have no analytic or closed form. Therefore their modelling employs a numerical technique, typically multivariate simulations, which can have very high dimensions. Random Orthogonal Matrix (ROM) simulation is a method that has gained some popularity because of the absence of certain simulation errors. Specifically, it exactly matches a target mean, covariance matrix and certain higher moments with every simulation. This paper extends the ROM simulation algorithm presented by Hanke et al. (2017), hereafter referred to as HPSW, which matches the target mean, covariance matrix and Kollo skewness vector exactly. Our first contribution is to establish necessary and sufficient conditions for the HPSW algorithm to work. Our second contribution is to develop a general approach for constructing admissible values in the HPSW. Our third theoretical contribution is to analyse the effect of multivariate sample concatenation on the target Kollo skewness. Finally, we illustrate the extensions we develop here using a simulation study.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.06586&r=all
  14. By: Jonghyeon Min
    Abstract: The financial market trend forecasting method is emerging as a hot topic in financial markets today. Many challenges still currently remain, and various researches related thereto have been actively conducted. Especially, recent research of neural network-based financial market trend prediction has attracted much attention. However, previous researches do not deal with the financial market forecasting method based on LSTM which has good performance in time series data. There is also a lack of comparative analysis in the performance of neural network-based prediction techniques and traditional prediction techniques. In this paper, we propose a financial market trend forecasting method using LSTM and analyze the performance with existing financial market trend forecasting methods through experiments. This method prepares the input data set through the data preprocessing process so as to reflect all the fundamental data, technical data and qualitative data used in the financial data analysis, and makes comprehensive financial market analysis through LSTM. In this paper, we experiment and compare performances of existing financial market trend forecasting models, and performance according to the financial market environment. In addition, we implement the proposed method using open sources and platform and forecast financial market trends using various financial data indicators.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01502&r=all
  15. By: Ioannis Boukas; Damien Ernst; Thibaut Th\'eate; Adrien Bolland; Alexandre Huynen; Martin Buchwald; Christelle Wynants; Bertrand Corn\'elusse
    Abstract: The large integration of variable energy resources is expected to shift a large part of the energy exchanges closer to real-time, where more accurate forecasts are available. In this context, the short-term electricity markets and in particular the intraday market are considered a suitable trading floor for these exchanges to occur. A key component for the successful renewable energy sources integration is the usage of energy storage. In this paper, we propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market where exchanges occur through a centralized order book. The goal of the storage device operator is the maximization of the profits received over the entire trading horizon, while taking into account the operational constraints of the unit. The sequential decision-making problem of trading in the intraday market is modelled as a Markov Decision Process. An asynchronous distributed version of the fitted Q iteration algorithm is chosen for solving this problem due to its sample efficiency. The large and variable number of the existing orders in the order book motivates the use of high-level actions and an alternative state representation. Historical data are used for the generation of a large number of artificial trajectories in order to address exploration issues during the learning process. The resulting policy is back-tested and compared against a benchmark strategy that is the current industrial standard. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.05940&r=all
  16. By: Bekkers, Eddy; Schroeter, Sofia
    Abstract: This paper provides an economic analysis of the trade conflict between the US and China, providing an overview of the tariff increases, a discussion of the background of the trade conflict, and an analysis of the economic effects of the trade conflict, based both on empirics (ex post analysis) and on simulations (ex ante analysis). Bilateral tariffs have increased on average to 17% between the US and China, and the Phase One Agreement signed in January 2020 between the two countries only leads to minor reductions in the tariffs to 16%. The trade conflict has led to a sizeable reduction in trade between the US and China in 2019 and is accompanied by considerable trade diversion to imports from other regions, leading to a reorganization of value chains in (East) Asia. The simulation analysis shows that the direct effects of the tariff increases on the global economy are limited (0.1% reduction in global GDP). The impact of the Phase One Agreement on the global economy is even smaller, although the US is projected to turn real income losses into real income gains because of the Chinese commitments to buy additional US goods. The biggest impact of the trade conflict is provoked by rising uncertainty about trade policy and the paper provides a framework to analyze the uncertainty effects.
    Keywords: Trade conflict,Economic simulations,Trade effects of tariffs
    JEL: F12 F13 F14 F17
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:wtowps:ersd202004&r=all
  17. By: Fittje, Jens; Wagner, Helmut
    Abstract: The topography of China's financial network is unique. Is it also uniquely robust to contagion? We explore this question using network theory. We find that networks that are more concentrated are less fragile when connectivity is low. However, they remain in a robust-yet-fragile state longer than decentralized networks, when connectivity is increased. We implement Chinese characteristics into our model and simulate it numerically. The simulations show, that the large state-controlled banks act as effective stop-gaps for contagion, which makes the Chinese network relatively robust. This robustness is significantly reduced, if a significant share of the smaller banks are high-risk institutions.
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:ceames:172020&r=all
  18. By: Giovanni Dosi; Mauro Napoletano; Andrea Roventini; Joseph E. Stiglitz; Tania Treibich
    Abstract: We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information, coordination hurdles and structural breaks. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naïve learning rules. Our results suggest that fast and frugal robust heuristics may not be a second-best option but rather “rational” responses in complex and changing macroeconomic environments.
    JEL: C63 D8 E32 E6 O4
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:26922&r=all
  19. By: Daniel Bj\"orkegren; Joshua E. Blumenstock; Samsun Knight
    Abstract: An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual's observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.03865&r=all
  20. By: Vijay V. Vazirani; Mihalis Yannakakis
    Abstract: In 1979, Hylland and Zeckhauser \cite{hylland} gave a simple and general scheme for implementing a one-sided matching market using the power of a pricing mechanism. Their method has nice properties -- it is incentive compatible in the large and produces an allocation that is Pareto optimal -- and hence it provides an attractive, off-the-shelf method for running an application involving such a market. With matching markets becoming ever more prevalant and impactful, it is imperative to finally settle the computational complexity of this scheme. We present the following partial resolution: 1. A combinatorial, strongly polynomial time algorithm for the special case of $0/1$ utilities. 2. An example that has only irrational equilibria, hence proving that this problem is not in PPAD. Furthermore, its equilibria are disconnected, hence showing that the problem does not admit a convex programming formulation. 3. A proof of membership of the problem in the class FIXP. We leave open the (difficult) question of determining if the problem is FIXP-hard. Settling the status of the special case when utilities are in the set $\{0, {\frac 1 2}, 1 \}$ appears to be even more difficult.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.01348&r=all
  21. By: Shantanu Bagchi (Department of Economics, Towson University); Juergen Jung (Department of Economics, Towson University)
    Abstract: We examine the welfare effects of Social Security in a general equilibrium environment with realistic labor income, mortality, and health risks. We construct an overlapping generations model with rational-expectations households facing idiosyncratic health risk, profit maximizing firms, incomplete insurance markets, and a government that provides pensions and health insurance. We calibrate this model to the U.S. economy and perform two sets of computational experiments: (i) modifying the progressivity of the Social Security's benefit-earnings rule, and (ii) cutting Social Security's payroll tax. We find that both experiments have a larger effect on overall welfare in the presence of health risk, because health risk increases the importance of short-term consumption smoothing, both within work-life and retirement. Increased progressivity allows households to better smooth old-age consumption risk, and the payroll tax cut increases disposable income and allows better self-insurance against early-life health risk. We also find that labor supply is an important self-insurance tool in the presence of health risk, as increasing Social Security's progressivity has a smaller effect on overall welfare and cutting the payroll tax has a larger effect on overall welfare when labor supply is fixed. Finally, low-income households experience larger welfare gains both from increasing Social Security's progressivity and cutting the payroll tax, because of their relatively low ability to self-insure against health risk in general.
    Keywords: Health risk, Social Security, benefit-earnings rule, consumption smoothing, general equilibrium.
    JEL: E62 E21 H31 H55 I14
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:tow:wpaper:2020-02&r=all
  22. By: Ayoubi, Charles (EPFL - Ecole Polytechnique Federale de Lausanne); Barbosu, Sandra (barbosu@sloan.org); Pezzoni, Michele (Université Côte d’Azur/CNRS/GREDEG, Nice, OST-HCERES, Paris, and ICRIOS, Bocconi University, Milan); Visentin, Fabiana (UNU-MERIT, Maastricht University)
    Abstract: Entrepreneurs, managers, and scientists participate in competitive selection processes to obtain resources. The project they propose is a crucial aspect of their success. In this paper, we focus on the selection of scientists applying for academic funding by submitting a research proposal. We argue that two core dimensions of the research proposal affect the probability of funding success: its coherence with the applicant's previous work, and its alignment with subjects of general interest for the scientific community. Employing a neural network algorithm, we analyse the text of 2,494 research proposals for a prestigious fellowship awarded to promising early-stage North American researchers. We find field-specific heterogeneity in the committees' evaluations. In life sciences and chemistry, evaluators value the research proposal's coherence positively with the scientist's recent work and the proposals' alignment with the current subject of general interest for the scientific community. Conversely, in physics, evaluators give more weight to bibliometric indicators and less to the proposal coherence and alignment. Our results can be extended beyond the academic context to managerial implications in cases such as entrepreneurs and managers submitting project proposals to investors
    Keywords: Research trajectories, research funding, coherence, alignment
    JEL: I23 O32 O38
    Date: 2020–03–24
    URL: http://d.repec.org/n?u=RePEc:unm:unumer:2020010&r=all
  23. By: Alessandro Sforza; Marina Steininger
    Abstract: The economic effects of a pandemic crucially depend on the extend to which countries are connected in global production networks. In this paper we incorporate production barriers induced by COVID-19 shock into a Ricardian model with sectoral linkages, trade in intermediate goods and sectoral heterogeneity in production. We use our model to quantify the welfare effect of the disruption in production that started in China and then quickly spread across the world. We find that the COVID-19 shock has a considerable impact on most economies in the world, especially when a share of the labor force is quarantined. Moreover, we show that global production linkages have a clear role in magnifying the effect of the production shock. Finally, the economic effects of the COVID-19 shock are heterogeneous across sectors, regions and countries, depending on the geographic distribution of industries in each region and country and their degree of integration in the global production network.
    Keywords: COVID-19 shock, globalization, production barrier, sectoral interrelations, computational general equilibrium
    JEL: F10 F11 F14 F60
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_8184&r=all
  24. By: Kirill S. Glavatskiy; Mikhail Prokopenko; Adrian Carro; Paul Ormerod; Michael Harre
    Abstract: Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.
    Date: 2020–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2004.07571&r=all
  25. By: Antoine Bozio (IPP - Institut des politiques publiques, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics); Simon Rabaté (IPP - Institut des politiques publiques, Centraal Planbureau); Audrey Rain (IPP - Institut des politiques publiques); Maxime Tô (IPP - Institut des politiques publiques, UCL - University College of London [London], Institute for Fiscal Studies)
    Abstract: A points system, operating at defined yield, makes it possible to rethink how pension systems are managed. Instead of having to make repeated ad hoc changes to the parameters of the system, it is possible to define change rules that other guarantees to future pensioners, as regards not only their entitlements but also the long-term sustainability of the system. In this brief, and based on simulations of a variety of shocks to the pension system, we study what management rules deserve to be chosen. Two rules absolutely must be selected: firstly the growth in the value of the pension point should match the growth in salaries; and secondly converting the points into pension should take into account the life expectancy of each generation (cohort). A third rule that is important for the long term, is the relationship between the rules for index-linking claimed pensions and the amounts of the pensions when they start being claimed. This rule should serve as a guide to managers so that they can steer the system towards an equilibrium that is not based on too low an index-linking of the pensions. Such management implies high institutional autonomy for the system, whereby the managers need to be accountable for the finnancial equilibrium and for the risks to pension revaluation.
    Date: 2019–06
    URL: http://d.repec.org/n?u=RePEc:hal:journl:halshs-02516413&r=all

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