{"title":"Performance attribution for multifactorial equity portfolios","authors":"F. Abergel, T. Heckel","doi":"10.21314/jois.2021.014","DOIUrl":"https://doi.org/10.21314/jois.2021.014","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"1 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67707246","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-82711-3
Bill Jiang
{"title":"Investment Strategies: A Practical Approach to Enhancing Investor Returns","authors":"Bill Jiang","doi":"10.1007/978-3-030-82711-3","DOIUrl":"https://doi.org/10.1007/978-3-030-82711-3","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"25 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89776237","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}
{"title":"Is volatility a friend or enemy of your stock and fund investments?","authors":"Long Chen, Jun Gao, Sheng Zhu","doi":"10.21314/jois.2022.011","DOIUrl":"https://doi.org/10.21314/jois.2022.011","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"4 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87503064","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}
{"title":"Forecasting volatility and market returns using the CBOE Volatility Index and its options","authors":"Spencer Stanley, W. Trainor","doi":"10.21314/jois.2021.013","DOIUrl":"https://doi.org/10.21314/jois.2021.013","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"1 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67707149","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}
{"title":"Portfolio rebalancing, conflicts of interest of delegated investment management and seasonality in Canadian financial markets","authors":"George Athanassakos","doi":"10.21314/jois.2022.002","DOIUrl":"https://doi.org/10.21314/jois.2022.002","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"7 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67706922","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}
This study is, to the best of the authors’ knowledge, the first attempt to comprehensively examine and explain the January effect in the illiquidity premium. Using data from 23 major international stock markets for the years 1991–2019, we demonstrate a strong and pervasive calendar seasonality in liquidity pricing across different geographical regions. The entire illiquidity premium is realized almost solely in January. Further, we show that this seasonal pattern is driven by a parallel phenomenon in small firms; exposure to the size factor thoroughly explains the January seasonality in the illiquidity premium.
{"title":"What Drives the January Seasonality in the Illiquidity Premium? Evidence from International Stock Markets","authors":"Adam Zaremba, Nusret Cakici","doi":"10.21314/JOIS.2021.008","DOIUrl":"https://doi.org/10.21314/JOIS.2021.008","url":null,"abstract":"This study is, to the best of the authors’ knowledge, the first attempt to comprehensively examine and explain the January effect in the illiquidity premium. Using data from 23 major international stock markets for the years 1991–2019, we demonstrate a strong and pervasive calendar seasonality in liquidity pricing across different geographical regions. The entire illiquidity premium is realized almost solely in January. Further, we show that this seasonal pattern is driven by a parallel phenomenon in small firms; exposure to the size factor thoroughly explains the January seasonality in the illiquidity premium.","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"56 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74629881","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}
Intuitively, the model prediction signs matter a lot in finance, especially for investment strategy constructions. This paper proposes an approach in which the loss function regularizes the errors in prediction in different ways. In particular, the loss function considers simultaneously errors in prediction signs and the sizes and signs of the residuals in the model prediction. Less weight is given to the residuals with correct prediction signs but more weight is assigned to the residuals with wrong prediction signs. This is important because agents make decisions according to model predictions, especially the signs of the predictions. Simultaneously, the residuals of larger size are also penalized more and the ones of smaller size are penalized less. Also, the signs of the residuals are considered in the loss function because they also affect decision making processes. For these reasons, training models by weights varying with the correctness of the prediction signs and the sizes and signs of the residuals is significant for decision making. This paper proposes a new approach termed as Sign regression which takes into account of these considerations. The simulation results show that Sign regression consistently performs better than the ordinary least squares method and least absolute deviations method out-of-sample. An application on Fama and French three factor model also shows good performance of Sign regression.
{"title":"Sign prediction and sign regression","authors":"Weige Huang","doi":"10.2139/ssrn.3695594","DOIUrl":"https://doi.org/10.2139/ssrn.3695594","url":null,"abstract":"Intuitively, the model prediction signs matter a lot in finance, especially for investment strategy constructions. This paper proposes an approach in which the loss function regularizes the errors in prediction in different ways. In particular, the loss function considers simultaneously errors in prediction signs and the sizes and signs of the residuals in the model prediction. Less weight is given to the residuals with correct prediction signs but more weight is assigned to the residuals with wrong prediction signs. This is important because agents make decisions according to model predictions, especially the signs of the predictions. Simultaneously, the residuals of larger size are also penalized more and the ones of smaller size are penalized less. Also, the signs of the residuals are considered in the loss function because they also affect decision making processes. For these reasons, training models by weights varying with the correctness of the prediction signs and the sizes and signs of the residuals is significant for decision making. This paper proposes a new approach termed as Sign regression which takes into account of these considerations. The simulation results show that Sign regression consistently performs better than the ordinary least squares method and least absolute deviations method out-of-sample. An application on Fama and French three factor model also shows good performance of Sign regression.","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"1 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43431417","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}
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