Skewness Research Articles

How does digital transformation affect the profitability of rural commercial banks?

Under the impact of the business sinking of large commercial banks and the cross-border competition of fintech companies, digital transformation has become a strategic priority for rural commercial banks. However, can digital transformation improve the profitability of rural commercial banks (PORCB)? This aspect has not been thoroughly examined in academic literature. This study utilizes unbalanced panel data from 54 Chinese rural commercial banks spanning from 2010 to 2021. The Peking University Commercial Bank Digital Transformation Index is employed to gauge the extent of digital transformation, while ordinary least squares estimation is used to examine its effect on the profitability of rural commercial banks and elucidate its underlying mechanisms. The findings show that: (1) Digital transformation can significantly improve the PORCB. (2) Digital transformation improves the PORCB by enhancing asset quality, operating efficiency and risk-taking. (3) The profitability-enhancing effect of digital transformation is more pronounced among rural commercial business banks with larger assets and lower equity concentration, as well as among state-owned rural commercial banks. This paper's findings represent a novel investigation into the correlation between digital transformation and the profitability of rural commercial banks. They offer a theoretical foundation and empirical evidence for future studies examining the economic implications of bank digital transformation, and offer valuable insights for enhancing the digitalisation of rural banks.

Global fluid turbulence simulations in the scrape-off layer of a stellarator island divertor

Isothermal fluid turbulence simulations have been performed in the edge and scrape-off layer (SOL) of an analytic stellarator configuration with an island divertor, thereby providing numerical insight into edge turbulence in regions around islands in a stellarator. The steady-state transport follows the a curvature drive that is inverse to the major radius ( $1/R$ ) toward the outboard side, but large fluctuations are present throughout the island divertor region, with the average wavelength of similar size to the island width. The system exhibits a prominent $m=2$ , $n=5$ mode, where m is the poloidal mode number and n is the toroidal mode number, although other modes are present. The amplitude and radial extent of the density fluctuations are similar throughout the edge and SOL, but can decrease near island O-points. The fluctuations exhibit a predominantly positive skewness on the outboard midplane, indicating blob-like perturbations for the transport into the outer SOL. It is determined that a point on the separatrix is generally more correlated with regions outside of the SOL than a nearby reference point which does not lie on the separatrix.

Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease.

Frailty is a medical syndrome caused by multiple factors, characterized by decreased strength, endurance, and diminished physiological function, resulting in increased susceptibility to dependence and/or death. Patients with chronic obstructive pulmonary disease (COPD) tend to be more vulnerable to frailty due to their physical and psychological burdens. Therefore, the aim of this study was to develop a reliable and accurate vulnerability risk prediction model for frailty in patients with COPD in order to improve the identification and prediction of patient frailty. The specific objectives of this study were to determine the prevalence of frailty in patients with COPD and develop a prediction model and evaluate its predictive power. Clinical information was analyzed using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, and 34 indicators, including behavioral factors, health status, mental health parameters, and various sociodemographic variables, were examined in the study. The adaptive synthetic sampling technique was used for unbalanced data. Three methods, ridge regressor, extreme gradient boosting (XGBoost) classifier, and random forest (RF) regressor, were used to filter predictors. Seven machine learning (ML) techniques including logistic regression (LR), support vector machines (SVM), multilayer perceptron, light gradient-boosting machine, XGBoost, RF, and K-nearest neighbors were used to analyze and determine the optimal model. For customized risk assessment, an online predictive risk modeling website was created, along with Shapley additive explanation (SHAP) interpretations. Depression, smoking, gender, social activities, dyslipidemia, asthma, and residence type (urban vs. rural) were predictors for the development of frailty in patients with COPD. In the test set, the XGBoost model had an area under the curve of 0.942 (95% confidence interval: 0.925-0.959), an accuracy of 0.915, a sensitivity of 0.873, and a specificity of 0.911, indicating that it was the best model. The ML predictive model developed in this study is a useful and easy-to-use instrument for assessing the vulnerability risk of patients with COPD and may aid clinical physicians in screening high-risk patients.

Can eXplainable AI Offer a New Perspective for Groundwater Recharge Estimation?—Global‐Scale Modeling Using Neural Network

AbstractDue to the difficulties in estimating groundwater recharge and cross‐boundary nature of many aquifers, estimating groundwater recharge at large scale has been called upon. Process‐based models as well as data‐driven models have been established to meet this need. Meanwhile, with the advent of explainable artificial intelligence (XAI) methods, data‐driven machine learning models can take advantage of enhanced explainability while keeping the strength of high flexibility. In this study, an ensemble neural network model was built to check the suitability of the model to predict groundwater recharge and the possibility to gain new insights from large data set. Recent large inputs of groundwater recharge data and additional input for the Arabian Peninsula collated in this study were fed to the model with multiple predictors related to climatology considering seasonality, soil and plant characteristics, topography, and hydrogeology. The model showed higher performance (adjusted R2: 0.702, RMSE: 193.35 mm yr−1) than a recent global process‐based model in predicting groundwater recharge. Using XAI methods as individual conditional expectations and Shapley Additive Explanation interaction values, the model behavior was analyzed and possible linear and non‐linear relationships between the predictors and the groundwater recharge rate were found. Long‐term averaged precipitation and enhanced vegetation index showed non‐linear relationships with groundwater recharge rate, while slope, compound topographic index, and water table depth showed low importance to the model results. Most model behaviors followed the domain knowledge, while multi‐correlation between predictors and data skewness hindered the model from learning.

Market Concentration, Bank Characteristics, Macroeconomic Conditions, and Indonesian Islamic Bank Financing

As the latest player in the banking market in Indonesia, Islamic banks are not as experienced as their counterpart conventional banks. In addition, the types of Islamic bank financing (loans) are different from conventional bank loans. Our work investigates the determinants of Islamic bank financing in Indonesia. Our concern variables are market concentration, bank fundamentals, and macroeconomic conditions, including Covid-19. This study examines all Islamic banks in Indonesia from 2015 to 2020 using quarterly data. Our data set is 724 observations with unbalanced panel data. We employ the dynamic panel data using the two-step system GMM that is more robust than two-step difference GMM. Market concentration encourages financing. Profitability, bank size, and financing loss provision also enhance financing. However, a high degree of risk aversion and inefficiency reduces financing. Furthermore, Islamic bank financing also depends on macroeconomic conditions. Economic upturns strengthen financing. Strong bank fundamentals, particularly bank size, are the key to success for Islamic bank financing. The results draw an important practical implication. Large Islamic bank is a necessary condition to compete with a conventional bank. Accordingly, the spin off policy of Islamic bank windows to full-fledged Islamic bank should be implemented immediately.

ESG controversies, corporate governance, and the market for corporate control

ABSTRACT Capitalizing on a unique measure of takeover vulnerability, we examine how the takeover market, which is widely regarded as a crucial instrument of external governance, influences environmental, social, and governance (ESG) controversies. This paper is the first to investigate how corporate control markets are influenced by the market for corporate control. The sample consists of unbalanced panel data from 6,236 firm-year observations during 2002–2014. We use Propensity Score Matching (PSM) and Instrumental Variable (IV) analyses to address potential endogeneity, and entropy-balancing approach to address the issue of observable selection. The results show that the disciplinary mechanism associated with the takeover market compels managers to take actions that benefit shareholders, thus avoiding ESG controversies. An increase in takeover susceptibility by one standard deviation resulted in a 10.12% decline in controversial activities. Furthermore, we find that firm profitability drops, and risk increases substantially in response to ESG controversies.

An analysis on the accumulation of serotonin indicates the positively correlation between MdT5H5 and serotonin content in apple fruits

Serotonin (5-hydroxytryptamine, 5-HT), a tryptophan derivative, is an important functional component in fruits. Although 5-HT was found in most fruits, the accumulation characteristics of its biosynthesis in apple fruits remain unclear. Here, we conducted a large-scale survey of 148 apple fruits in 5-HT content, and investigated the biosynthesis differences of 5-HT. Our results displayed a broad skewed normal distribution of 5-HT in apple fruits. 5-HT accumulated in all tissues of apple plants, with the highest content in the seeds and the higher content in the pulp than that of the peel. A significant increase of 5-HT at ripening stage was observed in the fruits with high 5-HT content, however, the 5-HT content did not change significantly during fruit development in the fruits with low 5-HT content. The bagging reduced the 5-HT biosynthesis compared to the fruits exposed to light conditions. In addition, the expression level of MdT5H5 was positively correlated with the 5-HT content, suggesting that MdT5H5 may play a key regulatory role in the 5-HT biosynthesis in apple fruits.

Disentangling Gender and Relative Age Effects in Women's and Girls' Rugby Union.

Relative age effects (RAEs) within sports refer to the overrepresentation of athletes born earlier in the selection year and the underrepresentation of those born later in the selection year. Research examining RAEs in women's and girls' rugby union remains limited in comparison to the male literature, whilst the impacts of RAEs on the youth-senior transition are yet to be explored in a female sport context. As such, the purpose of this study was to examine RAEs during entry into the women's and girls' premiership and international rugby union pathways in England, as well as to compare them to their respective senior cohort (n = 1367): (a) U18 England Rugby Centre of Excellence Player (n = 325) vs. Senior Premiership Player (n = 868), and (b) U18 England Player (n = 49) vs. Senior England Player (n = 125). Chi-square (χ2) analyses compared birth quarter (BQ) distributions against expected distributions. The findings revealed no significant difference in BQ distributions at either youth or senior levels, as well as no significant differences in the BQ distributions of those who were likely to transition from youth to senior levels (all p > 0.05). Importantly, though, descriptive statistics showed a skewed birthdate distribution in both U18 England Rugby Centre of Excellence Player (BQ1 = 30% vs. BQ4 = 20%) and U18 England Player cohorts (BQ1 = 33% vs. BQ4 = 18%). We highlight the gender-specific mechanisms that potentially explain the variations between male and female RAEs in rugby union, including developmental differences, sport popularity, and sociocultural norms. We also warn against a 'copy and paste' template from the male provision to ensure the recent growth of female rugby union does not fall victim to the same RAEs in the future.

Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies.

Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time-consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta-analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post-transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject-level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large-sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney-transplant patients compared to the standard-of-care.

Collaborative research in healthcare: uncovering the impact of industry collaboration on the service innovativeness of university hospitals

AbstractIn the face of the challenges of rising demand and increasing economic pressure, medical service innovations and university hospitals are becoming increasingly important as cornerstones for the continuous development of medical care. Simultaneously, increasing technological complexity, scarce funding, and resource-intensive technology development are forcing university hospitals to open up their internal innovation processes. Numerous studies have highlighted the beneficial effects of cross-sectoral collaboration on the innovation performance of industrial entities, while neglecting the perspective of academic institutions. The impact of industry collaborations on the service innovativeness of university hospitals in particular has not been addressed in previous research. Although the potential for nonlinear effects of university-industry collaborations has been discussed in research, adequate evidence, particularly in the healthcare context, has not been provided. Therefore, this study addresses the questions: (1) What is the impact of university-hospital-industry collaboration (UHIC) on the service innovativeness of university hospitals? (2) Can a nonlinear relationship between UHIC and university hospitals' service innovativeness be empirically verified? The hypotheses are tested based on an unbalanced panel data set, which combines three distinct data sources and comprises annual observations from 2011 until 2019 of all German university hospitals. The findings indicate that the UHIC intensity has a positive effect on university hospitals’ service innovativeness with a one-year time lag. Furthermore, a negative quadratic UHIC effect was found. In doing so, this study complements existing research and opens the black box by uncovering the downside of cross-sectoral collaboration for a more complete understanding of the underlying dynamics of this growing area of research, with a particular focus on university hospitals.

Characterising information gains and losses when collecting multiple epidemic model outputs

BackgroundCollaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. MethodsWe compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. ResultsBy collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. ConclusionsWe observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.

Ensemble classifiers using multi-objective Genetic Programming for unbalanced data

Genetic Programming (GP) can be used to design effective classifiers due to its built-in feature selection and feature construction characteristics. Unbalanced data distributions affect the classification performance of GP classifiers. Some fitness functions have been proposed to solve the class imbalance problem of GP classifiers. However, with the evolution of GP, single-objective GP classifiers evaluated by a single fitness function have poor generalization ability. Moreover, using the best evolved GP classifier for decision-making can easily lead to the possibility of misclassification. In this paper, multi-objective GP is used to optimize multiple fitness functions including AUC approximation (Wmw), Distance (Dist), and Complexity to evolve ensemble classifiers, which jointly determines the class labels of unknown instances. Experiments on sixteen datasets show that our multi-objective GP can significantly improve classification performance compared with single-objective GP, and our proposed ensemble classifiers evolved by multi-objective GP can further improve the classification performance than the single best GP classifier. Comparisons with six GP-based and five traditional machine learning algorithms show that our proposed approaches can achieve significantly better classification performance on most cases.

IMPACTO DO PROGRAMA DE MODERNIZAÇÃO DA GESTÃO DO FUTEBOL E RESPONSABILIDADE FISCAL NO DESEMPENHO FINANCEIRO E ESPORTIVO DOS CLUBES BRASILEIROS

The professionalization of football in Brazil has heightened interest in the financial management of clubs, prompting the Brazilian government to establish the Financial Responsibility and Modern Management Program for Football (PROFUT). Hence, our research investigates the impact of adherence to PROFUT on the financial and sporting performance of football clubs in Brazil. We gathered accounting and sports performance data from 58 football clubs participating in the first, second, and third divisions of the Brazilian football championship. This data was sourced from the clubs’ official websites and the Brazilian Football Confederation website. The collected data underwent unbalanced panel data regression analysis using the differences in differences estimator. As regards financial performance, the findings suggest that PROFUT participation increased clubs’ total revenue and the specialization of their revenue structures. However, no impact was observed on indebtedness, undermining the program’s intent to reduce clubs’ liabilities. Furthermore, the results indicate that adherence to the program could potentially diminish a club’s sporting performance, implying that managers seem unable to balance financial health with on-field results effectively. This study ventures beyond investigating clubs’ revenue to examine underlying structural factors. Furthermore, it employs an innovative estimator in the context of financial and sports performance in Brazilian football clubs, particularly in relation to PROFUT regulations. Thus, this empirical research carries significant practical implications for directors and managers associated with football clubs and entities. It can guide regulatory bodies’ strategic decisions, policy formulation, and inspection procedures.

An adaptive weighted ensemble learning network for diabetic retinopathy classification.

Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images.

TRADE SHOCKS AND HIGHER‐ORDER EARNINGS RISK IN LOCAL LABOR MARKETS

AbstractThis article investigates the relationship between international trade and asymmetrical labor income risk. Using the case study of Brazil, we inspect how an increase in import penetration following the China shock impacted the distribution of idiosyncratic earnings changes across the country's local labor markets. We find that an increase in import penetration leads to a more dispersed and negatively skewed distribution. These effects can be explained by an increase in the volatility of hours worked following job and industry transitions, particularly from involuntary job separations.

Does The Military Expenditure Hurt The Economic Growth? Evidence Derive From South Asian Countries

Research Aims: This study examines the impact of military spending onthe economic growth in South Asian countries.Design/methodology/approach: It uses the 134 unbalanced data points ofsix South Asian countries collected from the various reports of the WorldBank. Descriptive and exploratory research designs are used. It is basedon the positivist research philosophy. Some statistical and econometrictools like descriptive statistics, panel unit root test, Johnsen and Kaoresidual cointegration test, and panel ARDL model are used to explorethe impact of military expenditure, inflation rate, unemployment rate,and gross capital formation on the economic growth of South Asiancountries.Research Findings: Inflation has a positive effect on economic growth inthe long run but a negative impact in the short run. Military expenditurehas a positive effect in the long run but a negative impact on economicgrowth; the unemployment rate is negative, and gross capital formationpositively impacts economic growth in the long and short run in SouthAsian Countries. Military spending does not significantly affect economicprogress in the long run, but military expenditure hurts economic growthin the short run. One unit increase in military spending results from a0.633 percent decrease in the GDP growth of South Asian countries. Theyouth unemployment rate negatively impacts economic growth in theshort and long run. One unit increase in the unemployment rate resultsfrom 0.093 and 0.319 unit decrease in economic growth in the long andshort run, respectively. Likewise, one unit increase in capital formationand inflation rate results from 0.203 and 0.116 unit increase in GDPgrowth in the long run, respectively. The inflation rate negatively impactseconomic growth in the short run. One unit increase in inflation rateresults from 0.191 unit decrease in GDP growth in short-run. Prioritizestrategies that mitigate short-term negative impacts of inflation andmilitary expenditure while fostering long-term economic growth throughinvestment in capital formation and addressing youth unemployment inSouth Asian countries.Keywords: Inflation, unemployment, panel data, Keynesian stimulushypothesis, stagnation

Premorbid body weight predicts weight loss in both anorexia nervosa and atypical anorexia nervosa: Further support for a single underlying disorder.

For adolescents, DSM-5 differentiates anorexia nervosa (AN) and atypical AN with the 5th BMI-centile-for-age. We hypothesized that the diagnostic weight cut-off yields (i) lower weight loss in atypical AN and (ii) discrepant premorbid BMI distributions between the two disorders. Prior studies demonstrate that premorbid BMI predicts admission BMI and weight loss in patients with AN. We explore these relationships in atypical AN. Based on admission BMI-centile < or ≥5th, participants included 411 female adolescent inpatients with AN and 49 with atypical AN from our registry study. Regression analysis and t-tests statistically addressed our hypotheses and exploratory correlation analyses compared interrelationships between weight loss, admission BMI, and premorbid BMI in both disorders. Weight loss in atypical AN was 5.6 kg lower than in AN upon adjustment for admission age, admission height, premorbid weight and duration of illness. Premorbid BMI-standard deviation scores differed by almost one between both disorders. Premorbid BMI and weight loss were strongly correlated in both AN and atypical AN. Whereas the weight cut-off induces discrepancies in premorbid weight and adjusted weight loss, AN and atypical AN overall share strong weight-specific interrelationships that merit etiological consideration. Epidemiological and genetic associations between AN and low body weight may reflect a skewed premorbid BMI distribution. In combination with prior findings for similar psychological and medical characteristics in AN and atypical AN, our findings support a homogenous illness conceptualization. We propose that diagnostic subcategorization based on premorbid BMI, rather than admission BMI, may improve clinical validity. Because body weights of patients with AN must drop below the 5th BMI-centile per DSM-5, they will inherently require greater weight loss than their counterparts with atypical AN of the same sex, age, height and premorbid weight. Indeed, patients with atypical AN had a 5.6 kg lower weight loss after controlling for these variables. In comparison to the reference population, we found a lower and higher mean premorbid weight in patients with AN and atypical AN, respectively. Considering previous psychological and medical comparisons showing little differences between AN and atypical AN, we view a single disorder as the most parsimonious explanation. Etiological models need to particularly account for the strong relationship between weight loss and premorbid body weight.

Intrusion Detection Model for Wireless Sensor Networks Based on FedAvg and XGBoost Algorithm

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm.

Feature Distribution Matching by Optimal Transport for Effective and Robust Coreset Selection

Training neural networks with good generalization requires large computational costs in many deep learning methods due to large-scale datasets and over-parameterized models. Despite the emergence of a number of coreset selection methods to reduce the computational costs, the problem of coreset distribution bias, i.e., the skewed distribution between the coreset and the entire dataset, has not been well studied. In this paper, we find that the closer the feature distribution of the coreset is to that of the entire dataset, the better the generalization performance of the coreset, particularly under extreme pruning. This motivates us to propose a simple yet effective method for coreset selection to alleviate the distribution bias between the coreset and the entire dataset, called feature distribution matching (FDMat). Unlike gradient-based methods, which selects samples with larger gradient values or approximates gradient values of the entire dataset, FDMat aims to select coreset that is closest to feature distribution of the entire dataset. Specifically, FDMat transfers coreset selection as an optimal transport problem from the coreset to the entire dataset in feature embedding spaces. Moreover, our method shows strong robustness due to the removal of samples far from the distribution, especially for the entire dataset containing noisy and class-imbalanced samples. Extensive experiments on multiple benchmarks show that FDMat can improve the performance of coreset selection than existing coreset methods. The code is available at https://github.com/successhaha/FDMat.

Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning

In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential. The value function is often trained using the least squares method, which implicitly assumes a Gaussian error distribution. However, a recent study suggested that the error distribution for training the value function is often skewed because of the properties of the Bellman operator, and violates the implicit assumption of normal error distribution in the least squares method. To address this, we proposed a method called Symmetric Q-learning, in which the synthetic noise generated from a zero-mean distribution is added to the target values to generate a Gaussian error distribution. We evaluated the proposed method on continuous control benchmark tasks in MuJoCo. It improved the sample efficiency of a state-of-the-art reinforcement learning method by reducing the skewness of the error distribution.