We extend prior work on economic value added (EVA) style analysis in the context of dynamic changes in EVA style. We find that rotations of EVA style provide traders and investors with opportunistic returns. For value-creating growth companies, the status quo strategy outperformed the consolidated annual average return of companies that moved out of Quadrant II (QII) to other EVA styles by 7.45%. For value-destroying growth companies, the status quo position underperformed the consolidated average return of companies that moved out of QIII to other EVA styles by 11.24%. For almost every year from 2000–2020, the status quo strategy for value-creating growth companies outperformed the consolidated annual returns from moving out of QII to other EVA styles. For most every year over the 21-year reporting period, the status quo strategy for value-destroying growth companies underperformed the consolidated returns from moving out of QIII. Future research on the dynamics of EVA style points to opportunities to build out trading systems across multiple starting points.
{"title":"Rotational Dynamics of EVA Style Analysis: Implications for Traders and Investors","authors":"A. Chakraborty, J. Grant, E. Trahan, B. Varma","doi":"10.3905/joi.2022.1.244","DOIUrl":"https://doi.org/10.3905/joi.2022.1.244","url":null,"abstract":"We extend prior work on economic value added (EVA) style analysis in the context of dynamic changes in EVA style. We find that rotations of EVA style provide traders and investors with opportunistic returns. For value-creating growth companies, the status quo strategy outperformed the consolidated annual average return of companies that moved out of Quadrant II (QII) to other EVA styles by 7.45%. For value-destroying growth companies, the status quo position underperformed the consolidated average return of companies that moved out of QIII to other EVA styles by 11.24%. For almost every year from 2000–2020, the status quo strategy for value-creating growth companies outperformed the consolidated annual returns from moving out of QII to other EVA styles. For most every year over the 21-year reporting period, the status quo strategy for value-destroying growth companies underperformed the consolidated returns from moving out of QIII. Future research on the dynamics of EVA style points to opportunities to build out trading systems across multiple starting points.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"70 - 88"},"PeriodicalIF":0.6,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49572567","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 : 2022-09-30DOI: 10.3905/joi.2022.31.6.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/joi.2022.31.6.001","DOIUrl":"https://doi.org/10.3905/joi.2022.31.6.001","url":null,"abstract":"","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"252 1","pages":"1"},"PeriodicalIF":0.6,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91322565","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}
The authors examine Morningstar information changes stemming from changes in their 5-Star stock-rating system. Converse to many studies of other second-hand information effects, Morningstar rating changes lead to abnormal returns (ARs) that persist for 30 days subsequent to the information release. This information may be used to derive alpha-generating trading strategies. Statistically significant positive ARs persist for 30 days postannouncement for upgrades to 4 and 5 stars, while significant negative ARs persist for 30 days postannouncement for upgrades to 2 and 3 stars and for downgrades to 4, 3, 2, or 1 star. Traders can use this information to devise long, short, and long–short trading strategies. Morningstar’s analysis has high credibility, particularly with retail investors. Morningstar changes a stock’s rating when the price of the stock moves farther from the intrinsic value estimate derived by Morningstar’s model, suggesting that its analysis of the current stock price relative to intrinsic value and the timing of the rating change provide some relevant information to the market.
{"title":"Trading Opportunities around Morningstar Stock Rating Changes","authors":"P. Bolster, E. Trahan, Mahboubeh Ebrahimi","doi":"10.3905/joi.2022.1.243","DOIUrl":"https://doi.org/10.3905/joi.2022.1.243","url":null,"abstract":"The authors examine Morningstar information changes stemming from changes in their 5-Star stock-rating system. Converse to many studies of other second-hand information effects, Morningstar rating changes lead to abnormal returns (ARs) that persist for 30 days subsequent to the information release. This information may be used to derive alpha-generating trading strategies. Statistically significant positive ARs persist for 30 days postannouncement for upgrades to 4 and 5 stars, while significant negative ARs persist for 30 days postannouncement for upgrades to 2 and 3 stars and for downgrades to 4, 3, 2, or 1 star. Traders can use this information to devise long, short, and long–short trading strategies. Morningstar’s analysis has high credibility, particularly with retail investors. Morningstar changes a stock’s rating when the price of the stock moves farther from the intrinsic value estimate derived by Morningstar’s model, suggesting that its analysis of the current stock price relative to intrinsic value and the timing of the rating change provide some relevant information to the market.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"89 - 103"},"PeriodicalIF":0.6,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48406265","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}
In investing, there can sometimes be a tension between long-term goals and short-term risks. The investor might have a specific end goal in mind when structuring an investment portfolio, but the realization of a short-term risk in the interim can force a stop out before the end goal is achieved. One can think of margin calls, solvency triggers, or even behavioral effects. These are situations where it is not only the end goal that matters, but also the journey toward it. In this article, the author derives the probability of the investor reaching a target hurdle value at the end of the investment horizon without breaching a lower barrier value during the investment horizon. The addition of an intrahorizon loss constraint can lead to meaningfully different investment behavior. The article describes three stylized examples to demonstrate the practical consequences and provides some tools on how to manage the trade-off.
{"title":"Balancing Long-Term Goals versus Short-Term Risks","authors":"Ronald J. M. van Loon","doi":"10.3905/joi.2022.1.242","DOIUrl":"https://doi.org/10.3905/joi.2022.1.242","url":null,"abstract":"In investing, there can sometimes be a tension between long-term goals and short-term risks. The investor might have a specific end goal in mind when structuring an investment portfolio, but the realization of a short-term risk in the interim can force a stop out before the end goal is achieved. One can think of margin calls, solvency triggers, or even behavioral effects. These are situations where it is not only the end goal that matters, but also the journey toward it. In this article, the author derives the probability of the investor reaching a target hurdle value at the end of the investment horizon without breaching a lower barrier value during the investment horizon. The addition of an intrahorizon loss constraint can lead to meaningfully different investment behavior. The article describes three stylized examples to demonstrate the practical consequences and provides some tools on how to manage the trade-off.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"132 - 149"},"PeriodicalIF":0.6,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45570390","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}
Using a dataset unique to China, the authors provide evidence that large trades earn excess returns in the China A-shares market. Stocks with net flows through large trades earn positive excess returns in the subsequent month, while stocks with net flows through small trades earn negative excess returns. The predictive power lasts up to two years. Large trades are correlated with institutional holdings by Qualified Foreign Institutional Investors (QFII) and Northbound Stock Connect investors. Moreover, mutual fund returns negatively load on the small net flows factor, suggesting that mutual funds earn their alpha partially from trading against uninformed small retail investors. Small net flows gravitate toward unprofitable, high-valuation, and low-momentum stocks, matching what the literature has found for small retail trader preference. Lastly, small- and medium-sized net flows predict negative subsequent profitability, while large and extra-large net flows predict positive subsequent profitability.
{"title":"In China A-Shares, Big Money Is Smart Money","authors":"X. Liu, V. Viswanathan, Yingfan Xia","doi":"10.3905/joi.2022.1.240","DOIUrl":"https://doi.org/10.3905/joi.2022.1.240","url":null,"abstract":"Using a dataset unique to China, the authors provide evidence that large trades earn excess returns in the China A-shares market. Stocks with net flows through large trades earn positive excess returns in the subsequent month, while stocks with net flows through small trades earn negative excess returns. The predictive power lasts up to two years. Large trades are correlated with institutional holdings by Qualified Foreign Institutional Investors (QFII) and Northbound Stock Connect investors. Moreover, mutual fund returns negatively load on the small net flows factor, suggesting that mutual funds earn their alpha partially from trading against uninformed small retail investors. Small net flows gravitate toward unprofitable, high-valuation, and low-momentum stocks, matching what the literature has found for small retail trader preference. Lastly, small- and medium-sized net flows predict negative subsequent profitability, while large and extra-large net flows predict positive subsequent profitability.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"115 - 131"},"PeriodicalIF":0.6,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45001340","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":"LAST PAGE: Trading Insights: Revisiting Intrinsic Value and the EMH","authors":"J. Grant","doi":"10.3905/joi.2022.1.239","DOIUrl":"https://doi.org/10.3905/joi.2022.1.239","url":null,"abstract":"","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"150 - 151"},"PeriodicalIF":0.6,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46647832","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}
While there is significant evidence of a positive historical realized equity risk premium (ERP), it is less clear how equity returns have varied in different bond yield environments (the expected ERP). This paper explores historical expected ERPs across 16 countries from 1870 to 2015 leveraging the Jordà-Schularick-Taylor Macrohistory database. We find evidence that while future equity returns have been lower during periods of lower bond yields, the decline is less than would be implied by a constant expected ERP. The predictive significance of bond yields varies significantly depending on the future return metric considered (nominal return versus real return, as well as total return versus price return), as well as whether dividend yields and recent inflation are considered. Overall, these results suggest that while equity returns are likely to be lower in a low bond yield environment, they are not likely to be as low as a constant ERP would suggest, and that the overall relation is relatively noisy.
{"title":"The Shape of the Expected Equity Risk Premium","authors":"David Blanchett","doi":"10.3905/joi.2022.1.238","DOIUrl":"https://doi.org/10.3905/joi.2022.1.238","url":null,"abstract":"While there is significant evidence of a positive historical realized equity risk premium (ERP), it is less clear how equity returns have varied in different bond yield environments (the expected ERP). This paper explores historical expected ERPs across 16 countries from 1870 to 2015 leveraging the Jordà-Schularick-Taylor Macrohistory database. We find evidence that while future equity returns have been lower during periods of lower bond yields, the decline is less than would be implied by a constant expected ERP. The predictive significance of bond yields varies significantly depending on the future return metric considered (nominal return versus real return, as well as total return versus price return), as well as whether dividend yields and recent inflation are considered. Overall, these results suggest that while equity returns are likely to be lower in a low bond yield environment, they are not likely to be as low as a constant ERP would suggest, and that the overall relation is relatively noisy.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"31 1","pages":"81 - 91"},"PeriodicalIF":0.6,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48329937","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}
Christopher P. N. Woodcock, Alesi Rowland, Snežana Pejić
In this article, the objective is to validate and better understand an effect of return generation at the position level that has long been assumed but never demonstrated: that return generation has a life cycle—a beginning, middle, and end—and that investors often hold on to positions too long, potentially diminishing whatever excess returns they were able to generate early in the life cycle. This analysis examines roughly 10,000 episodes (i.e., full cycles of a given position from first entry to last exit) across 43 active equity portfolios over 14 years.
{"title":"The Alpha Life Cycle: New Insight into Investment Alpha and How Portfolio Managers Can Sustain It","authors":"Christopher P. N. Woodcock, Alesi Rowland, Snežana Pejić","doi":"10.3905/joi.2022.1.237","DOIUrl":"https://doi.org/10.3905/joi.2022.1.237","url":null,"abstract":"In this article, the objective is to validate and better understand an effect of return generation at the position level that has long been assumed but never demonstrated: that return generation has a life cycle—a beginning, middle, and end—and that investors often hold on to positions too long, potentially diminishing whatever excess returns they were able to generate early in the life cycle. This analysis examines roughly 10,000 episodes (i.e., full cycles of a given position from first entry to last exit) across 43 active equity portfolios over 14 years.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"31 1","pages":"27 - 35"},"PeriodicalIF":0.6,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46340431","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}
Measuring the exposure to limited partnership funds investing in private assets is a key challenge to multi-asset class investors. It arises from the long time lags between milestone events: the commitment to the fund, the capital calls, and returning capital to the fund’s investors. The task is further complicated by the dual perspective of the portfolio of the fund as seen through the prism of the end investor or the manager. This article explores the variety of existing and potential measures that address this challenge, comparing their appropriate usage and potential adverse effects. The goal is to provide a neutral and multi-faceted view to improve investment decision-making.
{"title":"Measuring Exposure for Limited Partnership Funds","authors":"Emilian Belev, Thomas Meyer","doi":"10.3905/joi.2022.1.236","DOIUrl":"https://doi.org/10.3905/joi.2022.1.236","url":null,"abstract":"Measuring the exposure to limited partnership funds investing in private assets is a key challenge to multi-asset class investors. It arises from the long time lags between milestone events: the commitment to the fund, the capital calls, and returning capital to the fund’s investors. The task is further complicated by the dual perspective of the portfolio of the fund as seen through the prism of the end investor or the manager. This article explores the variety of existing and potential measures that address this challenge, comparing their appropriate usage and potential adverse effects. The goal is to provide a neutral and multi-faceted view to improve investment decision-making.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"31 1","pages":"36 - 53"},"PeriodicalIF":0.6,"publicationDate":"2022-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47583322","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}
Raúl Gómez-Martínez, Carmen Orden-Cruz, maRía lUISa meDRanO-GaRCía
Currently, algorithmic trading systems are one of the biggest challenges for machine learning (ML) and artificial intelligence (AI). In this article, an AI model is proposed using predictor variables based on trend-following momentum indicators. Using a data sample of highly traded futures contracts and their technical indicators, the results show a predictive capacity greater than 50% of the market trend of the next session. However, ML did not allow a profitable algorithmic trading system during the testing process.
{"title":"Quantitative Trading Using Artificial Intelligence on Trend-Following Indicators: An Example in 2020","authors":"Raúl Gómez-Martínez, Carmen Orden-Cruz, maRía lUISa meDRanO-GaRCía","doi":"10.3905/joi.2022.1.235","DOIUrl":"https://doi.org/10.3905/joi.2022.1.235","url":null,"abstract":"Currently, algorithmic trading systems are one of the biggest challenges for machine learning (ML) and artificial intelligence (AI). In this article, an AI model is proposed using predictor variables based on trend-following momentum indicators. Using a data sample of highly traded futures contracts and their technical indicators, the results show a predictive capacity greater than 50% of the market trend of the next session. However, ML did not allow a profitable algorithmic trading system during the testing process.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"35 - 49"},"PeriodicalIF":0.6,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43357750","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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