DecisionSciRN: Predictive Analytics (Sub-Topic)最新文献

Mutual fund performance: Shouldn’t clear winners outperform both, the benchmark and the peer-group? 共同基金的表现:大赢家的表现不应该同时优于基准和同行吗?

DecisionSciRN: Predictive Analytics (Sub-Topic) Pub Date : 2021-07-03 DOI: 10.2139/ssrn.3879461

Cesario Mateus, Irina B. Mateus, N. Todorovic

{"title":"Mutual fund performance: Shouldn’t clear winners outperform both, the benchmark and the peer-group?","authors":"Cesario Mateus, Irina B. Mateus, N. Todorovic","doi":"10.2139/ssrn.3879461","DOIUrl":"https://doi.org/10.2139/ssrn.3879461","url":null,"abstract":"Standard Fama-French-Carhart models define ‘winners’ as those funds that generate highest excess returns given the factor risks involved; however they do not provide information on whether such winners are outperforming their prospectus benchmark or their peer-group. In addition, existing literature relying on these models by and large does not find evidence of persistence in performance. In this paper, we argue that true (unbiased) winners should be defined differently: they are funds placed in the top-ranking group (quartile, quintile, etc.), which generate the highest factor-risk-adjusted performance relative to the benchmark and the peer-group simultaneously. We prove in this paper that using this definition and selecting true winner funds based on benchmark- and peer-group-adjusted alphas jointly lead to better performance than selecting the funds using either of these two alphas separately. Utilising both adjustments at the same time results in a strong predictive ability, leading to a selection of funds that persist in performance: our true winner funds have statistically significantly superior benchmark-adjusted alphas, peer-group adjusted alphas and Sharpe ratios one-year-ahead, which are significantly different from those generated by the true loser funds. The results are robust to extended investment horizon, and alpha estimation method, and they are not driven by outliers, size of fund-sorts, or any particular period within our sample.","PeriodicalId":201219,"journal":{"name":"DecisionSciRN: Predictive Analytics (Sub-Topic)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128545882","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 Growing Problem: Exploring Livestock Farm Resilience to Droughts in Unit Record Data. 一个日益严重的问题:在单位记录数据中探索畜牧业对干旱的适应能力。

DecisionSciRN: Predictive Analytics (Sub-Topic) Pub Date : 2020-11-30 DOI: 10.2139/ssrn.3739574

L. Timar, Eyal Apatov

引用次数: 1

Real Estate Price Prediction 房地产价格预测

DecisionSciRN: Predictive Analytics (Sub-Topic) Pub Date : 2020-11-01 DOI: 10.21276/IJIRCST.2020.8.6.1

G. Kumar, Ms Priyanka Makkar, Dr. Yojna Arora

引用次数: 8

Exploring Breaks in the Distribution of Stock Returns: Empirical Evidence from Apple Inc. 股票收益分布的突破:来自苹果公司的经验证据。

DecisionSciRN: Predictive Analytics (Sub-Topic) Pub Date : 2020-09-27 DOI: 10.2139/ssrn.3700419

Sébastien Lleo, W. Ziemba, J. Li

引用次数: 4

Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning 利用实时数据和机器学习预测机场中转客流

DecisionSciRN: Predictive Analytics (Sub-Topic) Pub Date : 2020-06-15 DOI: 10.2139/ssrn.3245609

Xiaojia Guo, Y. Grushka-Cockayne, B. D. Reyck

{"title":"Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning","authors":"Xiaojia Guo, Y. Grushka-Cockayne, B. D. Reyck","doi":"10.2139/ssrn.3245609","DOIUrl":"https://doi.org/10.2139/ssrn.3245609","url":null,"abstract":"Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted.","PeriodicalId":201219,"journal":{"name":"DecisionSciRN: Predictive Analytics (Sub-Topic)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116632633","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: 20