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A Novel Stock Trading Model based on Reinforcement Learning and Technical Analysis 一种基于强化学习和技术分析的股票交易模型
Annals of Data Science Pub Date : 2023-05-11 DOI: 10.1007/s40745-023-00469-1
Zahra Pourahmadi, Dariush Fareed, Hamid Reza Mirzaei
{"title":"A Novel Stock Trading Model based on Reinforcement Learning and Technical Analysis","authors":"Zahra Pourahmadi,&nbsp;Dariush Fareed,&nbsp;Hamid Reza Mirzaei","doi":"10.1007/s40745-023-00469-1","DOIUrl":"10.1007/s40745-023-00469-1","url":null,"abstract":"<div><p>This study investigates the potential of using reinforcement learning (RL) to establish a financial trading system (FTS), taking into account the main constraint imposed by the stock market, e.g., transaction costs. More specifically, this paper shows the inferior performance of the pure reinforcement learning model when it is applied in a multi-dimensional and noisy stock market environment. To solve this problem and to get a practical and reasonable trading strategies process, a modified RL model is proposed based on the actor-critic method where we have amended the actor by incorporating three metrics from technical analysis. The results show significant improvement compared with traditional trading strategies. The reliability of the model is verified by experimental results on financial data (S&amp;P500 index) and a fair evaluation of the proposed method and pure RL and three benchmarks is demonstrated. Statistical analysis proves that a combination of a) technical analysis (role-based strategies) and b) RL (machine learning strategies) and c) restricting the action of the RL policy network with a few realistic conditions results in trading decisions with higher investment return rates.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1653 - 1674"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49174695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm 基于分支定界和遗传算法的平台资源调度方法
Annals of Data Science Pub Date : 2023-05-11 DOI: 10.1007/s40745-023-00470-8
Yanfen Zhang, Jinyao Ma, Haibin Zhang, Bin Yue
{"title":"Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm","authors":"Yanfen Zhang,&nbsp;Jinyao Ma,&nbsp;Haibin Zhang,&nbsp;Bin Yue","doi":"10.1007/s40745-023-00470-8","DOIUrl":"10.1007/s40745-023-00470-8","url":null,"abstract":"<div><p>Platform resource scheduling is an operational research optimization problem of matching tasks and platform resources, which has important applications in production or marketing arrangement layout, combat task planning, etc. The existing algorithms are inflexible in task planning sequence and have poor stability. Aiming at this defect, the branch-and-bound algorithm is combined with the genetic algorithm in this paper. Branch-and-bound algorithm can adaptively adjust the next task to be planned and calculate a variety of feasible task planning sequences. Genetic algorithm is used to assign a platform combination to the selected task. Besides, we put forward a new lower bound calculation method and pruning rule. On the basis of the processing time of the direct successor tasks, the influence of the resource requirements of tasks on the priority of tasks is considered. Numerical experiments show that the proposed algorithm has good performance in platform resource scheduling problem.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"10 5","pages":"1421 - 1445"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43159033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model 自回归（MC-AR）模型多协变量的贝叶斯估计
Annals of Data Science Pub Date : 2023-05-04 DOI: 10.1007/s40745-023-00468-2
Jitendra Kumar, Ashok Kumar, Varun Agiwal
{"title":"Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model","authors":"Jitendra Kumar,&nbsp;Ashok Kumar,&nbsp;Varun Agiwal","doi":"10.1007/s40745-023-00468-2","DOIUrl":"10.1007/s40745-023-00468-2","url":null,"abstract":"<div><p>In present scenario, handling covariate/explanatory variable with the model is one of most important factor to study with the models. The main advantages of covariate are it’s dependency on past observations. So, study variable is modelled after explaining both on own past and past and future observation of covariates. Present paper deals estimation of parameters of autoregressive model with multiple covariates under Bayesian approach. A simulation and empirical study is performed to check the applicability of the model and recorded the better results.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1291 - 1301"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47960675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Bayes Analysis of Random Walk Model Under Different Error Assumptions 不同误差假设下随机漫步模型的贝叶斯分析
Annals of Data Science Pub Date : 2023-04-22 DOI: 10.1007/s40745-023-00465-5
Praveen Kumar Tripathi, Manika Agarwal
{"title":"A Bayes Analysis of Random Walk Model Under Different Error Assumptions","authors":"Praveen Kumar Tripathi,&nbsp;Manika Agarwal","doi":"10.1007/s40745-023-00465-5","DOIUrl":"10.1007/s40745-023-00465-5","url":null,"abstract":"<div><p>In this paper, the Bayesian analyses for the random walk models have been performed under the assumptions of normal distribution, log-normal distribution and the stochastic volatility model, for the error component, one by one. For the various parameters, in each model, some suitable choices of informative and non-informative priors have been made and the posterior distributions are calculated. For the first two choices of error distribution, the posterior samples are easily obtained by using the gamma generating routine in R software. For a random walk model, having stochastic volatility error, the Gibbs sampling with intermediate independent Metropolis–Hastings steps is employed to obtain the desired posterior samples. The whole procedure is numerically illustrated through a real data set of crude oil prices from April 2014 to March 2022. The models are, then, compared on the basis of their accuracies in forecasting the true values. Among the other choices, the random walk model with stochastic volatile errors outperformed for the data in hand.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1635 - 1652"},"PeriodicalIF":0.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47611888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data 零膨胀过分散计数数据的计数回归和机器学习技术:在生态数据中的应用
Annals of Data Science Pub Date : 2023-04-13 DOI: 10.1007/s40745-023-00464-6
Bonelwa Sidumo, Energy Sonono, Isaac Takaidza
{"title":"Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data","authors":"Bonelwa Sidumo,&nbsp;Energy Sonono,&nbsp;Isaac Takaidza","doi":"10.1007/s40745-023-00464-6","DOIUrl":"10.1007/s40745-023-00464-6","url":null,"abstract":"<div><p>The aim of this study is to investigate the overdispersion problem that is rampant in ecological count data. In order to explore this problem, we consider the most commonly used count regression models: the Poisson, the negative binomial, the zero-inflated Poisson and the zero-inflated negative binomial models. The performance of these count regression models is compared with the four proposed machine learning (ML) regression techniques: random forests, support vector machines, <span>(k-)</span>nearest neighbors and artificial neural networks. The mean absolute error was used to compare the performance of count regression models and ML regression models. The results suggest that ML regression models perform better compared to count regression models. The performance shown by ML regression techniques is a motivation for further research in improving methods and applications in ecological studies.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"803 - 817"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00464-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Inferences Based on Correlated Randomly Censored Gumbel’s Type-I Bivariate Exponential Distribution 基于相关随机截尾GumbelⅠ型双变量指数分布的推断
Annals of Data Science Pub Date : 2023-01-31 DOI: 10.1007/s40745-023-00463-7
Hare Krishna, Rajni Goel
{"title":"Inferences Based on Correlated Randomly Censored Gumbel’s Type-I Bivariate Exponential Distribution","authors":"Hare Krishna,&nbsp;Rajni Goel","doi":"10.1007/s40745-023-00463-7","DOIUrl":"10.1007/s40745-023-00463-7","url":null,"abstract":"<div><p>The formal random censoring plan has been extensively studied earlier in statistical literature by numerous researchers to deal with dropouts or unintentional random removals in life-testing experiments. All of them considered failure time and censoring time to be independent. But there are several situations in which one observes that as the failure time of an item increases, the censoring time decreases. In medical studies or especially in clinical trials, the occurrence of dropouts or unintentional removals is frequently observed in such a way that as the treatment (failure) time increases, the dropout (censoring) time decreases. No work has yet been found that deals with such correlated failure and censoring times. Therefore, in this article, we assume that the failure time is negatively correlated with censoring time, and they follow Gumbel’s type-I bivariate exponential distribution. We compute the maximum likelihood estimates of the model parameters. Using the Monte Carlo Markov chain methods, the Bayesian estimators of the parameters are calculated. The expected experimental time is also evaluated. Finally, for illustrative purposes, a numerical study and a real data set analysis are given.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1185 - 1207"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49343107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh 孟加拉国COVID-19病例的贝叶斯分层空间模型
Annals of Data Science Pub Date : 2023-01-22 DOI: 10.1007/s40745-022-00461-1
Md. Rezaul Karim,  Sefat-E-Barket
{"title":"Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh","authors":"Md. Rezaul Karim,&nbsp; Sefat-E-Barket","doi":"10.1007/s40745-022-00461-1","DOIUrl":"10.1007/s40745-022-00461-1","url":null,"abstract":"<div><p>This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh’s 64 districts. Moran <i>I</i> and Geary <i>C</i> are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city’s high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1581 - 1607"},"PeriodicalIF":0.0,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47950849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series 尼日利亚新冠肺炎疫苗接种干预分析：天真的解决方案与中断的时间序列
Annals of Data Science Pub Date : 2023-01-19 DOI: 10.1007/s40745-023-00462-8
Desmond Chekwube Bartholomew, Chrysogonus Chinagorom Nwaigwe, Ukamaka Cynthia Orumie, Godwin Onyeka Nwafor
{"title":"Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series","authors":"Desmond Chekwube Bartholomew,&nbsp;Chrysogonus Chinagorom Nwaigwe,&nbsp;Ukamaka Cynthia Orumie,&nbsp;Godwin Onyeka Nwafor","doi":"10.1007/s40745-023-00462-8","DOIUrl":"10.1007/s40745-023-00462-8","url":null,"abstract":"<div><p>In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in Nigeria were collected and subjected to two models: the naïve solution and the interrupted time series (the intervention model). Based on the Alkaike Information Criterion (AIC), sigma<sup>2</sup>, and log likelihood values, the interrupted time series model outperformed the Naïve solution model. ARIMA (4, 1, 4) with exogenous variables was identified as the best model. It was observed that the intervention (vaccination) was not significant at the 5% level of significance in reducing the number of daily COVID-19 cases in Nigeria since the start of the vaccination on March 5, 2021, until March 28, 2022. Also, the ARIMA (4, 1, 4) forecasts indicated that there will be surge in the number of daily COVID-19 cases in Nigeria between January and April 2023. As a result, we recommend strict adherence to COVID-19 protocols as well as further vaccination and sensitization programs to educate people on the importance of vaccine uptake and avoid Corona virus spread in the year 2023 and beyond.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1609 - 1634"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45470057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exchange Rate Forecasting: Nonlinear GARCH-NN Modeling Approach 汇率预测:非线性GARCH-NN建模方法
Annals of Data Science Pub Date : 2023-01-03 DOI: 10.1007/s40745-022-00458-w
Fahima Charef
{"title":"Exchange Rate Forecasting: Nonlinear GARCH-NN Modeling Approach","authors":"Fahima Charef","doi":"10.1007/s40745-022-00458-w","DOIUrl":"10.1007/s40745-022-00458-w","url":null,"abstract":"<div><p>This paper targets the description of the fusion of modeling techniques, such as the GARCH model and the Artificial Neural Network (ANN), for the sake of predicting financial series and precisely the series of the exchange rate in Tunisia, namely the USD/TND, the EUR/TND and the YEN/TND, for a daily frequency extending from 2015 through 2019. To our knowledge, this is the only paper that focuses on the descriptions of the fusion of modeling techniques (GARCH-NN) or hybridization method that applied on Tunisian currency (TND). The empirical results show that the hybrid model (GARCH-NN) outperforms and it is more efficient than the two used models. In fact, this method combines the advantages of two approaches in order to obtain a result more satisfactory for the expectations of the policy-makers in the exchange market in order to take their decisions. The results showed that the model proposed gives better results, so, can be an alternative of standard linear autoregressive model. This result has been joined by many empirical studies that confirm the quality and performance of this methodology, which researchers advise to be retained in all areas.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"947 - 957"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43856920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Alternative to the Beta Regression Model with Applications to OECD Employment and Cancer Data β回归模型的替代选择及其在经合组织就业和癌症数据中的应用
Annals of Data Science Pub Date : 2022-12-27 DOI: 10.1007/s40745-022-00460-2
Idika E. Okorie, Emmanuel Afuecheta
{"title":"An Alternative to the Beta Regression Model with Applications to OECD Employment and Cancer Data","authors":"Idika E. Okorie,&nbsp;Emmanuel Afuecheta","doi":"10.1007/s40745-022-00460-2","DOIUrl":"10.1007/s40745-022-00460-2","url":null,"abstract":"<div><p>In regression analysis involving response variable on the bounded unit interval [0, 1], the beta regression model often suffice as a common choice, however, there are many alternatives to the beta regression model. In this article, we add yet another new alternative to the literature called the unit upper truncated Weibull (unit UTW) regression model. We introduce a novel unit UTW distribution as an alternative to the beta distribution and we present some of its mathematical properties. The unit UTW distribution is then extended to build the unit UTW regression model. Through an extensive Monte-Carlo simulation experiments, we show that the method of maximum likelihood can provide good estimate for each parameter in the new models. We give two practical examples were the proposed models performed better than the beta distribution and the beta regression model.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"887 - 908"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47540024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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