{"title":"Are missing values important for earnings forecast? a machine learning perspective.","authors":"Ajim Uddin, Xinyuan Tao, Chia-Ching Chou, Dantong Yu","doi":"10.1080/14697688.2021.1963825","DOIUrl":"https://doi.org/10.1080/14697688.2021.1963825","url":null,"abstract":"<p><p>Analysts' forecast is one of the most common and important estimators for firms' future earnings. However, it is challenging to fully utilize because of the missing values. This study applies machine learning techniques to impute missing values in individual analysts' forecasts and subsequently to predict firms' future earnings based on both imputed and observed forecasts. After imputing missing values, the forecast error is reduced by 41% compared to the mean forecast, suggesting that missing values after imputation indeed useful for earnings forecast. We analyze multiple imputation methods and show that the out-performance of matrix factorization (MF) is consistent using different evaluation measures and across firms. Finally, we propose a stochastic gradient descent based coupled matrix factorization (CMF) to augment the imputation quality of missing values with multiple datasets. CMF further reduces the error of earnings forecast by 19% compared to the MF with a single dataset.</p>","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"22 6","pages":"1113-1132"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246338/pdf/nihms-1739019.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10541697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Philipp J Kremer, Damian Brzyski, Małgorzata Bogdan, Sandra Paterlini
{"title":"Sparse Index Clones via the sorted <i>ℓ</i> <sub>1</sub> - Norm.","authors":"Philipp J Kremer, Damian Brzyski, Małgorzata Bogdan, Sandra Paterlini","doi":"10.1080/14697688.2021.1962539","DOIUrl":"https://doi.org/10.1080/14697688.2021.1962539","url":null,"abstract":"<p><p>Index tracking and hedge fund replication aim at cloning the return time series properties of a given benchmark, by either using only a subset of its original constituents or by a set of risk factors. In this paper, we propose a model that relies on the <i>Sorted ℓ</i> <sub>1</sub> <i>Penalized Estimator</i>, called SLOPE, for index tracking and hedge fund replication. We show that SLOPE is capable of not only providing sparsity, but also to form groups among assets depending on their partial correlation with the index or the hedge fund return times series. The grouping structure can then be exploited to create individual investment strategies that allow building portfolios with a smaller number of active positions, but still comparable tracking properties. Considering equity index data and hedge fund returns, we discuss the real-world properties of SLOPE based approaches with respect to state-of-the art approaches.</p>","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"22 2","pages":"349-366"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9031478/pdf/nihms-1760168.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10807215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}