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 Computational EconomicsJournal Prestige (SJR): 0.433 Citation Impact (citeScore): 1Number of Followers: 11      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-9974 - ISSN (Online) 0927-7099 Published by Springer-Verlag  [2469 journals]
• Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance
Feature Engineering

Abstract: The emergence of big data, information technology, and social media provides an enormous amount of information about firms’ current financial health. When facing this abundance of data, decision makers must identify the crucial information to build upon an effective and operative prediction model with a high quality of the estimated output. The feature selection technique can be used to select significant variables without lowering the quality of performance classification. In addition, one of the main goals of bankruptcy prediction is to identify the model specification with the strongest explanatory power. Building on this premise, an improved XGBoost algorithm based on feature importance selection (FS-XGBoost) is proposed. FS-XGBoost is compared with seven machine learning algorithms based on three well-known feature selection methods that are frequently used in bankruptcy prediction: stepwise discriminant analysis, stepwise logistic regression, and partial least squares discriminant analysis (PLS-DA). Our experimental results confirm that FS-XGBoost provides more accurate predictions, outperforming traditional feature selection methods.
PubDate: 2022-01-23

• National Governance Differences and Foreign Bank Performance in Asian
Countries: The Role of Bank Competition

Abstract: Previous studies pay a little attention to whether the differences in national governance quality between home and host country substantially affect foreign bank performance. Based on a bank panel data with 375 foreign banks in 47 Asian countries between 2004 and 2019, the impact of the host–home country difference in governance quality on foreign bank performance is empirically investigated by considering the degree of banking competition measured with the Boone index. Using a panel data model with multilevel mixed-effects, this paper finds that foreign banks in Asia present higher profits than domestic banks. Specifically, banks locating in a less competitive host nation show higher profitability, while host–home differences in aggregated quality of national governance also significantly reduce the foreign bank profitability in terms of individual indicator for voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption. Finally, foreign banks could gauge their market power in a less competitive banking structure in the host country to mitigate the negative influence of national governance between host and home country on their financial performance.
PubDate: 2022-01-22

• Kelly-Based Options Trading Strategies on Settlement Date via Supervised
Learning Algorithms

Abstract: Option is a well-known financial derivative that attracts attention from investors and scholars, due to its flexible investment strategies. In this paper, we sought to establish an option trading system on settlement dates with money management and machine learning to improve the performance and to control risk. First, we fixed the odds of the option, and applied a machine learning algorithm to predict the win rate. Then, we adopted the money management module of Kelly criterion to obtain the optimal bidding fraction. In addition, we adopted ensemble learning algorithm to enhance the predicting power. The result of the experiments shows that the random forest and SVM have more powerful prediction capabilities in our experiments. Even if the prediction is barely acceptable, the systems still obtain profits and steadily rising equity curves through money management, which significantly reduce drawdown risk. In addition, the ensemble system achieves the outstanding trading performance with a profit factor of 2.429 and a Sharpe ratio of 1.227. Overall, the proposed option trading strategy can generate positive profit, and the money management module can well control the risk, and the ensemble learning module can significantly enhance the trading performance.
PubDate: 2022-01-22

• Valuation of Spark-Spread Option Written on Electricity and Gas Forward
Contracts Under Two-Factor Models with Non-Gaussian Lévy Processes

Abstract: In energy markets, especially electricity and gas, one experience rather large and dramatic spikes in spot prices, but they are quickly reverting back. Hence it is appropriate to take into account the factors for the spike behavior observed in the spot price series, while other factors induce price evolution when the market is stable. To illustrate this issue, we analyze the dynamics of spikes and seasonality through a normal probability test for returns of spot prices. We propose two-factor model separately for gas and electricity markets, such that in both market model the logarithmic spot price is a stationary Ornstein-Uhlenbeck process and the long-term variations are a drifted Brownian motion. We derive the forward price and its dynamic under proposed model and prove the uniqueness of solution of the stochastic differential equation related to forward price. Then we price the spark-spread option written on electricity and gas forward contracts. In the following, we derive the spark-spread option price under a hybrid geometric Brownian motion and prove that it converges to the sparks-spread option price under the proposed two-factor model. Since the market model is incomplete, we apply the quadratic hedging strategy which minimizes the hedging error. We also investigate the convergence of this strategy through the spark-spread option under the hybrid geometric Brownian motion. Numerical results confirm the achievement of all these convergences. In order to estimate the model parameters, we consider the Lévy processes as the independent normal inverse Gaussian processes and independent compound Poisson processes which have the jump size with the exponential distribution.
PubDate: 2022-01-17

• CO2 Emission Allowances Risk Prediction with GAS and GARCH Models

Abstract: We analyse the predictive and the forecasting ability of various Generalized Autoregressive Score (GAS) and GARCH frameworks for European Union Allowances (EUAs) daily returns (EUAs returns) for the period 22/04/2005–28/02/2019. We further examine the impact of different distributional assumptions on risk prediction. The Model Confidence Set (MCS) is employed to compare and select a superior predictive model of Value-at-Risk (VaR) thresholds. We find that GAS under skewed t-student error distribution and gjr-GARCH under general error distribution deliver excellent results for the Value-at-Risk (VaR) prediction for EUA at 1% and 5% levels, respectively. These results are robust with respect to three back-testing procedures (i.e., Unconditional Coverage, Conditional Coverage, and Dynamic Quantile tests). These results are of particular importance for the development of EUA pricing policies and risk management strategies.
PubDate: 2022-01-16

• When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune'

Abstract: We present a textual analysis that explains how Elon Musk’s sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world’s wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla’s CEO. However, we did not find evidence to support linkage between Musk’s sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.
PubDate: 2022-01-13

• The Influence of Economic Policy Uncertainty and Business Cycles on Fine
Wine Prices

Abstract: This study investigates the impact of both economic policy uncertainty (EPU) and business cycles on the fine wine market. We use a nonlinear autoregressive distributed lag model to measure the influence of these two variables on three major Liv-ex indices over the period 2005M01–2020M12. Our results are multiple. First, fine wine prices are relatively unaffected asymmetrically by EPU, while the economic cycle has a more pronounced asymmetric effect, especially in the short run. Second, uncertainty in Europe and the USA affect fine wine prices more than in China. Third, in the short term, fine wine prices react more strongly to changes in business cycles than to uncertainty. Finally, prices of the five first growths of Bordeaux are asymmetrically influenced by EPU, unlike of the rest of the most prestigious Bordeaux wines. The study also has implications for investment. We argue that a strong and professional strategic intelligence watch would help stakeholders in the secondary wine market to improve their returns, especially when European and US wines are involved. While short-runners should focus on information relative to changes in the business cycle, long-term investors would find it more interesting to closely monitor policy decisions liable to have long-term effects on wine prices (such as taxation, monetary measures…).
PubDate: 2022-01-11

• Use of Econometric Predictors and Artificial Neural Networks for the
Construction of Stock Market Investment Bots

Abstract: The gradual replacement of human operators by investor bots in financial markets has changed the way assets are traded on stock markets. The use of strategies based on artificial intelligence has became a hot trend, especially due to improvements in computer processing capacity. Recent works showed that the adoption of strategies based on classical predictors has decreased considerably over the past years. In this sense, hybrid approaches, which combine artificial intelligence and classical predictors, have risen as an interesting research subject. This manuscript introduces an investor bot that combines predictions made by two classes of artificial neural networks and three classes of econometric predictors. Such a combination are accomplished by ensembles, which are continuously re-optimized with the intention of identifying profitable opportunities. Data from the Brazilian stock market, with daily granularity, was used. Models generated from such data were applied in a period of economic and political crisis, which leaded to the fall of the Brazilian international investment rating. Superior results were obtained against the benchmarks, despite the brokerage costs and high-price volatility in such a period.
PubDate: 2022-01-06

• The $$\alpha$$ α -Tail Distance with an Application to Portfolio
Optimization Under Different Market Conditions

Abstract: It is very important to find some new distance measurement methods to estimate the correlation of the return of stocks because that the traditional distance measurement methods do not consider the influence of the market conditions. In this paper, a new distance measurement which called the $$\alpha$$ -tail distance is introduced to measure the correlations of stock’s returns under the different market conditions. We give some properties of the $$\alpha$$ -tail distance and provide some details on how to determine the parametric $$\alpha$$ in the $$\alpha$$ -tail distance via the market condition evaluation indices. A mean variance model with variable cardinality constraints based on the hierarchical clustering is given as an application of the $$\alpha$$ -tail distance. Moreover, the numerical example verifies that the $$\alpha$$ -tail distance is more suitable for measuring the correlation between stock’s returns than other traditional distance measurements under the different market conditions.
PubDate: 2021-12-01

• A Unifying Model for Statistical Arbitrage: Model Assumptions and
Empirical Failure

Abstract: Statistical arbitrage refers to a suite of quantitative investment strategies employed chiefly by hedge funds and proprietary trading firms. The arbitrageur can draw on a number of different approaches to identify and exploit an arbitrage opportunity, though the literature is broadly segmented by the canonical distance, cointegration and time series perspectives. Since the initial academic investigation of statistical arbitrage, its profitability has continued to diminish thanks largely to the increasing proportion of non-convergent opportunities. This paper surveys the existing literature, with particular emphasis given to evidence of statistical arbitrage failure, before unifying the distance, cointegration and time series perspectives under a single explicit model. The failure of statistical arbitrage opportunities is shown to be the direct consequence of implicit model assumptions that are inconsistent with the empirical literature. An alternative model is proposed, and evidence of its relative performance discussed.
PubDate: 2021-12-01

• Matlab, Python, Julia: What to Choose in Economics'

Abstract: We perform a comparison of Matlab, Python and Julia as programming languages to be used for implementing global nonlinear solution techniques. We consider two popular applications: a neoclassical growth model and a new Keynesian model. The goal of our analysis is twofold: First, it is aimed at helping researchers in economics choose the programming language that is best suited to their applications and, if needed, help them transit from one programming language to another. Second, our collections of routines can be viewed as a toolbox with a special emphasis on techniques for dealing with high dimensional economic problems. We provide the routines in the three languages for constructing random and quasi-random grids, low-cost monomial integration, various global solution methods, routines for checking the accuracy of the solutions as well as examples of parallelization. Our global solution methods are not only accurate but also fast. Solving a new Keynesian model with eight state variables only takes a few seconds, even in the presence of an active zero lower bound on nominal interest rates. This speed is important because it allows the model to be solved repeatedly as would be required for estimation.
PubDate: 2021-12-01

• Does Capacity Utilization Predict Inflation' A Wavelet Based Evidence
from United States

Abstract: This paper aims to test a causal nexus between capacity utilization and inflation in the United States for the period from January 1969 to June 2017. Given the non-validity of the constant-parameter linear model (i.e., standard linear Granger causality) in attendance of nonlinearities and structural breaks, we use wavelets to provide a more general picture of the link between the U.S. capacity utilization and U.S. inflation in both time and frequency domains. The findings indicate a positive co-movement between the variables, mainly at high frequencies (shorter term). In addition, we do find evidence of a significant bi-causal relationship between capacity utilization rate and inflation per different frequency, whereas standard linear Granger causality detects a unidirectional link from inflation to capacity utilization. In general, our findings suggest a notable implication for policy makers that are in contradiction to the view of recent scholars regarding deterioration in the inflation–utilisation nexus.
PubDate: 2021-12-01

• Implementing Convex Optimization in R: Two Econometric Examples

Abstract: Economists specify high-dimensional models to address heterogeneity in empirical studies with complex big data. Estimation of these models calls for optimization techniques to handle a large number of parameters. Convex problems can be effectively executed in modern programming languages. We complement Koenker and Mizera (J Stat Softw 60(5):1–23, 2014)’s work on numerical implementation of convex optimization, with focus on high-dimensional econometric estimators. Combining R and the convex solver MOSEK achieves speed gain and accuracy, demonstrated by examples from Su et al. (Econometrica 84(6):2215–2264, 2016) and Shi (J Econom 195(1):104–119, 2016). Robust performance of convex optimization is witnessed across platforms. The convenience and reliability of convex optimization in R make it easy to turn new ideas into executable estimators.
PubDate: 2021-12-01

• Examining Inferences from Neural Network Estimators of Binary Choice
Processes: Marginal Effects, and Willingness-to-Pay

Abstract: To satisfy the utility maximization hypothesis in binary choice modeling, logit and probit models must make a priori assumptions regarding the underlying functional form of a representative utility function. Such theoretical restrictions may leave the postulated estimable model statistically misspecified. This may lead to significant bias in substantive inferences, such as willingness-to-pay (or accept) measures, in environmental, natural resource and applied economic studies. Feed-forward back-propagation artificial neural networks (FFBANN) provide a potentially powerful semi-nonparametric method to avoid potential misspecifications and provide more valid inference. This paper shows that a single-hidden layer FFBANN can be interpreted as a logistic regression with a flexible index function and can be subsequently used for statistical inference purposes, such as estimation of marginal effects and willingness-to-pay measures. To the authors’ knowledge, the derivation and estimation of marginal effects and other substantive measures using neural networks are not available in the economics literature and is thus a novel contribution. An empirical application is conducted using FFBANNs to demonstrate estimation of marginal effects and willingness-to-pay in a contingent valuation and stated choice experimental framework. We find that FFBANNs can replicate results from binary choice models commonly used in the applied economics literature and can improve on substantive inferences derived from these models.
PubDate: 2021-12-01

• A New Dynamic Mixture Copula Mechanism to Examine the Nonlinear and
Asymmetric Tail Dependence Between Stock and Exchange Rate Returns

Abstract: This paper develops a new time-varying mixture copula, in which the dynamic weights of four distinct copulas are determined by a two-stratum process, to investigate the magnitude of tail dependence in four independent quadrants. In the two-stratum process, the weight of each copula is determined firstly by the relative importance of positive and negative dependence structures, and then by its own past values and adjustment processes. The weighting mechanism is time-varying in each stratum. This new specification is applied to analyze the asymmetric tail dependencies between the stock and exchange rate markets. Empirical results show four interesting findings. First, the quasi-maximum likelihood estimation (QMLE) has a better fitting ability than does the inference function for margins. The relative efficiency of the QMLE is irrespective of marginal specifications. Second, the goodness-of-fit tests of the new time-varying mixture copula are crucially affected by the marginal specifications. Third, estimation methods impact mixture weights. Four distinct tail dependencies are observed, revealing the importance of considering all four tails concurrently, and not just parts of the four tails. Fourth, the asymmetric positive and negative dependencies are significant. Each country shows a similar pattern of asymmetric negative dependence, but a different pattern of asymmetric positive dependence. These empirical findings provide important portfolio allocation implications.
PubDate: 2021-12-01

• A Statistical Analysis of Global Economies Using Time Varying Copulas

Abstract: The application of time varying copulas has become popular in recent years. Here, we illustrate an application involving stock indices of ten major economies covering all of the six continents. The dependence among them and its variation with respect to time are modeled using ten different copulas. The Gaussian copula is found to give the best fit. Predictions are given in terms of correlations and value at risk.
PubDate: 2021-12-01

• Correlated at the Tail: Implications of Asymmetric Tail-Dependence Across
Bitcoin Markets

Abstract: This paper is the first to fully characterize the relationship among cross-market Bitcoin prices to provide a complete picture of directional predictability of Bitcoin traded in various currencies across five developed markets. To exploit full-distributional dynamics, we employ Cross-quantilogram based Correlation and Dependence model to delve deep into the estimates an asymmetric tail dependence across quantiles would reflect on heterogeneous movement pattern of Bitcoin prices. A cross-quantilogram-based analysis reveals new empirical evidence of a heterogeneous tail dependence pattern: whereas Bitcoin-USD and the Northeast Asian market (viz., Japan) depicts a strong co-movement, smaller markets display weak connectedness and strong market-efficiency.
PubDate: 2021-12-01

• Trust and Social Control: Sources of Cooperation, Performance, and
Stability in Informal Value Transfer Systems

Abstract: We study the functioning of informal value transfer systems through the example of Hawala. By complementing the institutional theory with computational experiments that use the first agent-based model of IVTS, we examine the roles of generalized trust and social control for the emergence, stability, and efficiency of Hawala. We show that both trust and control are necessary, but not sufficient to guarantee its functioning, and that their relationship is time-dependent. The success of Hawala also depends on population size, interaction density, and forgiveness of the agents. Finally, we provide a theoretically grounded operationalization of generalized trust and social control that is applicable to informal exchange systems in general.
PubDate: 2021-12-01

• Reinforcement Learning in a Cournot Oligopoly Model

Abstract: This paper analyzes the learning behavior of firms in a repeated Cournot oligopoly game. Literature shows the degree of information and cognitive capacity of learning firms is a key factor that determines long run outcome of an oligopoly market. In particular, when firms possess the knowledge of market demand and are capable of computing the optimal production quantity given the output of other firms, the resulting market outcome is the Nash equilibrium. On the other hand, imitation that assumes low behavioral sophistication of firms generally favors higher output and converges to the Walrasian equilibrium. In this paper, a reinforcement learning algorithm with low cognitive requirement is adopted to model firms’ learning behavior. Reinforcement learning firms observe past production choices and fine tune them to improve profits. Analytical result shows that the Nash equilibrium is the only fixed point of the reinforcement learning process. Convergence to the Nash equilibrium is observed in computational simulations. When firms are allowed to imitate the most profitable competitor, all states between the Nash equilibrium and the Walrasian equilibrium can be reached. Furthermore, the long run outcome shifts towards the Nash equilibrium as the length of firms’ memory increases.
PubDate: 2021-12-01

• Research on the Effects of Institutional Liquidation Strategies on the
Market Based on Multi-agent Model

Abstract: Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.
PubDate: 2021-12-01

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