nep-cmp New Economics Papers
on Computational Economics
Issue of 2019‒08‒26
seventeen papers chosen by
Stan Miles
Thompson Rivers University

  1. A hybrid neural network model based on improved PSO and SA for bankruptcy prediction By Fatima Zahra Azayite; Said Achchab
  2. Nowcasting US GDP with artificial neural networks By Loermann, Julius; Maas, Benedikt
  3. Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models By Auclert, Adrien; Bardoczy, Bence; Rognlie, Matthew; Straub, Ludwig
  4. The Use of Binary Choice Forests to Model and Estimate Discrete Choice Models By Ningyuan Chen; Guillermo Gallego; Zhuodong Tang
  5. Realized Volatility Forecasting with Neural Networks By Bucci, Andrea
  6. Solving the Economic Scheduling of Grid-Connected Microgrid Based on the Strength Pareto Approach By Seruca, Manuel; Mota, Andrade; Rodrigues, David
  7. Applying meta-modeling for extended CGE-modeling: Sampling techniques and potential application By Jin, Ding; Hedtrich, Johannes; Henning, Christian H. C. A.
  8. Economic Operation of Self-Sustained Microgrid Optimal Operation by Multiobjective Evolutionary Algorithm Based on Decomposition By Mahdavi, Sadegh; Bayat, Alireza; Khazaei, Ehsan; Jamaledini, Ashkan
  9. Taxable Stock Trading with Deep Reinforcement Learning By Shan Huang
  10. Determining the Importance of an Attribute in a Demand System: Structural versus Machine Learning Approach By Badruddoza, Syed; Amin, Modhurima D.
  11. Large scale continuous-time mean-variance portfolio allocation via reinforcement learning By Haoran Wang; Xun Yu Zhou
  12. Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning Model By Shen, Ze; Wan, Qing; Leatham, David J.
  13. A procedure for loss-optimising default definitions across simulated credit risk scenarios By Arno Botha; Conrad Beyers; Pieter de Villiers
  14. Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions By Anna Stelzer
  15. Portfolio optimization while controlling Value at Risk, when returns are heavy tailed By Subhojit Biswas; Diganta Mukherjee
  16. Computational method for probability distribution on recursive relationships in financial applications By Jong Jun Park; Kyungsub Lee
  17. Algorithmic market making: the case of equity derivatives By Bastien Baldacci; Philippe Bergault; Olivier Gu\'eant

  1. By: Fatima Zahra Azayite; Said Achchab
    Abstract: Predicting firm's failure is one of the most interesting subjects for investors and decision makers. In this paper, a bankruptcy prediction model is proposed based on Artificial Neural networks (ANN). Taking into consideration that the choice of variables to discriminate between bankrupt and non-bankrupt firms influences significantly the model's accuracy and considering the problem of local minima, we propose a hybrid ANN based on variables selection techniques. Moreover, we evolve the convergence of Particle Swarm Optimization (PSO) by proposing a training algorithm based on an improved PSO and Simulated Annealing. A comparative performance study is reported, and the proposed hybrid model shows a high performance and convergence in the context of missing data.
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1907.12179&r=all
  2. By: Loermann, Julius; Maas, Benedikt
    Abstract: We use a machine learning approach to forecast the US GDP value of the current quarter and several quarters ahead. Within each quarter, the contemporaneous value of GDP growth is unavailable but can be estimated using higher-frequency variables that are published in a more timely manner. Using the monthly FRED-MD database, we compare the feedforward artificial neural network forecasts of GDP growth to forecasts of state of the art dynamic factor models and the Survey of Professional Forecasters, and we evaluate the relative performance. The results indicate that the neural network outperforms the dynamic factor model in terms of now- and forecasting, while it generates at least as good now- and forecasts as the Survey of Professional Forecasters.
    Keywords: Nowcasting; Machine learning; Neural networks; Big data
    JEL: C32 C53 E32
    Date: 2019–05
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:95459&r=all
  3. By: Auclert, Adrien; Bardoczy, Bence; Rognlie, Matthew; Straub, Ludwig
    Abstract: We propose a general and highly efficient method for solving and estimating general equilibrium heterogeneous-agent models with aggregate shocks in discrete time. Our approach relies on the rapid computation and composition of sequence-space Jacobians-the derivatives of perfect-foresight equilibrium mappings between aggregate sequences around the steady state. We provide a fast algorithm for computing Jacobians for heterogeneous agents, a technique to substantially reduce dimensionality, a rapid procedure for likelihood-based estimation, a determinacy condition for the sequence space, and a method to solve nonlinear perfect-foresight transitions. We apply our methods to three canonical heterogeneous-agent models: a neoclassical model, a New Keynesian model with one asset, and a New Keynesian model with two assets.
    Keywords: Computational Methods; General Equilibrium; Heterogeneous Agent; linearization
    JEL: C63 E21 E32
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:13890&r=all
  4. By: Ningyuan Chen; Guillermo Gallego; Zhuodong Tang
    Abstract: We show the equivalence of discrete choice models and the class of binary choice forests, which are random forest based on binary choice trees. This suggests that standard machine learning techniques based on random forest can serve to estimate discrete choice model with an interpretable output. This is confirmed by our data driven result that states that random forest can accurately predict the choice probability of any discrete choice model. Our framework has unique advantages: it can capture behavioral patterns such as irrationality or sequential searches; it handles nonstandard formats of training data that result from aggregation; it can measure product importance based on how frequently a random customer would make decisions depending on the presence of the product; it can also incorporate price information. Our numerical results show that binary choice forest can outperform the best parametric models with much better computational times.
    Date: 2019–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1908.01109&r=all
  5. By: Bucci, Andrea
    Abstract: In the last few decades, a broad strand of literature in finance has implemented artificial neural networks as forecasting method. The major advantage of this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating process. This makes it suitable for forecasting time series which exhibit long memory and nonlinear dependencies, like conditional volatility. In this paper, I compare the predictive performance of feed-forward and recurrent neural networks (RNN), particularly focusing on the recently developed Long short-term memory (LSTM) network and NARX network, with traditional econometric approaches. The results show that recurrent neural networks are able to outperform all the traditional econometric methods. Additionally, capturing long-range dependence through Long short-term memory and NARX models seems to improve the forecasting accuracy also in a highly volatile framework.
    Keywords: Neural Networks; Realized Volatility; Forecast
    JEL: C22 C45 C53 G17
    Date: 2019–08
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:95443&r=all
  6. By: Seruca, Manuel; Mota, Andrade; Rodrigues, David
    Abstract: This paper focuses on the operation of a microgrd which is connected to the main grid and can exchange power with it. To do so, the problem is modeled using mixed-integer linear programming (MILP). In the next step, an evolutionary algorithm known as Strength Perto Algorithm (SPA) is utilized to solve the model. In the end, the performance accuracy of the method is tested on a modified IEEE 69 bus test system.
    Keywords: Microgrid, Distribution Grids, Optimization, Economic Scheduling
    JEL: A1 A10 L0 L5 O1 O4 P0
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:95391&r=all
  7. By: Jin, Ding; Hedtrich, Johannes; Henning, Christian H. C. A.
    Abstract: Apart from the computational time and expenses of the CGE model, the discussion of elasticity parameter estimation and various closure rules as well as the difficulty of combining the results with other analysis approaches always poses obstacles ahead of us, therefore we are motivated to apply the Bayesian model selection method and the meta-modeling technique in order to tackle these problems from a new perspective in the framework of the Senegal-CGE model and even compare the CGE models. The meta-modeling technique can be deemed as an intermediate step towards the application of Bayesian model selection method because the CGE models cannot be directly integrated into the method. The meta-modeling technique, whose three essential components are the simulation models, the meta-models and the design of experimetns, aims at generating valid and simplified approximation models of the simulation models and gives us the opportunity of combining the CGE models with the Bayesian model selection method. The purpose of this paper is to demonstrate the meta-modeling techinique, test the performace of the meta-models generated by it and analyze whether various combinations of elasticity parameters affect the outputs of the CGE models which are quantified by the marginal effects. Our findings show that the produced meta-models possess a decent prediction capacity but we have not detected significant variability of the marginal effects within each unique sector.
    Keywords: Bayesian model selection,CGE modeling,Elasticities,Closure Rules,Meta-modeling,Meta-models,DOE
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:zbw:cauapw:wp201803&r=all
  8. By: Mahdavi, Sadegh; Bayat, Alireza; Khazaei, Ehsan; Jamaledini, Ashkan
    Abstract: This paper focuses on the optimal operation of the islanded microgrid. A novel heuristic method known as the Multiobjective Evolutionary Algorithm Based on Decomposition is presented to search for the optimal solution with a fast response. The efficiency of the method is tested on the IEEE 33 bus test network.
    Keywords: Economic operation, energy management, industrial power
    JEL: A1 A20 L1 L6 O3 O4
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:95393&r=all
  9. By: Shan Huang
    Abstract: In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategies with and without considering taxes by reinforcement learning. The result shows that tax ignorance could induce more than 62% loss on the average portfolio returns, implying that taxes should be embedded in the environment of continuous stock trading on AI platforms.
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1907.12093&r=all
  10. By: Badruddoza, Syed; Amin, Modhurima D.
    Keywords: Research Methods/ Statistical Methods
    Date: 2019–06–25
    URL: http://d.repec.org/n?u=RePEc:ags:aaea19:291210&r=all
  11. By: Haoran Wang; Xun Yu Zhou
    Abstract: We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the optimality of a multivariate Gaussian feedback policy, with time-decaying variance, in trading off exploration and exploitation. Based on a provable policy improvement theorem, we devise a scalable and data-efficient RL algorithm and conduct large scale empirical tests using data from the S&P 500 stocks. We found that our method consistently achieves over 10% annualized returns and it outperforms econometric methods and the deep RL method by large margins, for both long and medium terms of investment with monthly and daily trading.
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1907.11718&r=all
  12. By: Shen, Ze; Wan, Qing; Leatham, David J.
    Keywords: Agribusiness
    Date: 2019–06–25
    URL: http://d.repec.org/n?u=RePEc:ags:aaea19:290696&r=all
  13. By: Arno Botha; Conrad Beyers; Pieter de Villiers
    Abstract: A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative delinquency measures, other than simply measuring payments in arrears, can also be evaluated using this optimisation procedure. Furthermore, a simulation study is performed in testing the procedure from `first principles' across a wide range of credit risk scenarios. Specifically, three probabilistic techniques are used to generate cash flows, while the parameters of each are varied, as part of the simulation study. The results show that loss minima can exist for a select range of credit risk profiles, which suggests that the loss optimisation of default thresholds can become a viable practice. The default decision is therefore framed anew as an optimisation problem in choosing a default threshold that is neither too early nor too late in loan life. These results also challenges current practices wherein default is pragmatically defined as `90 days past due', with little objective evidence for its overall suitability or financial impact, at least beyond flawed roll rate analyses or a regulator's decree.
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1907.12615&r=all
  14. By: Anna Stelzer
    Abstract: This study conducts a benchmarking study, comparing 23 different statistical and machine learning methods in a credit scoring application. In order to do so, the models' performance is evaluated over four different data sets in combination with five data sampling strategies to tackle existing class imbalances in the data. Six different performance measures are used to cover different aspects of predictive performance. The results indicate a strong superiority of ensemble methods and show that simple sampling strategies deliver better results than more sophisticated ones.
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1907.12996&r=all
  15. By: Subhojit Biswas; Diganta Mukherjee
    Abstract: We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to the Value at Risk assuming a heavy tail distribution of the stock prices return. We use Markov Decision Process and dynamic programming principle to get the optimal strategies and the value function which maximize the expected utility for parametric as well as non parametric distributions. Due to lack of explicit solution in the non parametric case, we use numerical integration for optimization
    Date: 2019–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1908.03907&r=all
  16. By: Jong Jun Park; Kyungsub Lee
    Abstract: In quantitative finance, it is often necessary to analyze the distribution of the sum of specific functions of observed values at discrete points of an underlying process. Examples include the probability density function, the hedging error, the Asian option, and statistical hypothesis testing. We propose a method to calculate such a distribution, utilizing a recursive method, and examine it using various examples. The results of the numerical experiment show that our proposed method has high accuracy.
    Date: 2019–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1908.04959&r=all
  17. By: Bastien Baldacci; Philippe Bergault; Olivier Gu\'eant
    Abstract: In this article, we tackle the problem of a market maker in charge of a book of equity derivatives on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an equity option market maker is in fact tractable. More precisely, the problem faced by an equity option market maker is characterized by a two-dimensional functional equation that can be solved numerically using interpolation techniques and classical Euler schemes, even for large portfolios. Numerical examples are provided for a large book of equity options.
    Date: 2019–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1907.12433&r=all

This nep-cmp issue is ©2019 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.