The author introduces a profit projection model for crypto mining. With the model as the foundation, the author presents a hedging strategy that significantly reduces risks.
{"title":"Crypto Mining: Profit Projection and Risk Hedging","authors":"Jake Fan","doi":"10.3905/joi.2023.1.255","DOIUrl":"https://doi.org/10.3905/joi.2023.1.255","url":null,"abstract":"The author introduces a profit projection model for crypto mining. With the model as the foundation, the author presents a hedging strategy that significantly reduces risks.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"89 - 98"},"PeriodicalIF":0.6,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46751575","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}
Prior to the publication of this article, the author, Kathleen Moriarty, passed away on December 20, 2022. Kathleen was a pioneer in the investment management community, best known for her role in shepherding the first exchange-traded fund, the SPDR S&P 500 ETF, to launch, which earned her the moniker “Spider Woman.” Throughout her career, Kathleen was a humble and thoughtful colleague who was universally recognized as a kind-hearted and beloved individual. She will be deeply missed by friends, family, colleagues, and clients, and her contributions to the industry will not be soon forgotten.
{"title":"Insider Trading at a Cryptocurrency Exchange","authors":"K. Moriarty","doi":"10.3905/joi.2023.1.254","DOIUrl":"https://doi.org/10.3905/joi.2023.1.254","url":null,"abstract":"Prior to the publication of this article, the author, Kathleen Moriarty, passed away on December 20, 2022. Kathleen was a pioneer in the investment management community, best known for her role in shepherding the first exchange-traded fund, the SPDR S&P 500 ETF, to launch, which earned her the moniker “Spider Woman.” Throughout her career, Kathleen was a humble and thoughtful colleague who was universally recognized as a kind-hearted and beloved individual. She will be deeply missed by friends, family, colleagues, and clients, and her contributions to the industry will not be soon forgotten.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"99 - 103"},"PeriodicalIF":0.6,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41769976","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 : 2023-01-31DOI: 10.3905/joi.2023.32.2.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/joi.2023.32.2.001","DOIUrl":"https://doi.org/10.3905/joi.2023.32.2.001","url":null,"abstract":"","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":" ","pages":"1"},"PeriodicalIF":0.6,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47627588","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 : 2023-01-31DOI: 10.3905/joi.2023.32.2.024
Kevin J. DiCiurcio, Boyu Wu, Qian Wang
Earnings growth measures the change in a company’s reported net income through time. It is arguably the most widely observed measure of the growth and the profitability of a business, and a critical driver of equity returns. Though many practitioners have relied on historical averages to inform earnings growth expectations, research has found a relationship between earnings growth and prevailing economic growth. Building on prior research that connects earnings growth with real GDP growth, the authors split the earnings growth into two parts—revenue growth and changes in profit margins—and identifies multiple macroeconomic factors that have historical relationships separately with each. The authors find that revenue growth can historically be explained by world GDP growth, US GDP growth and payout ratios, and changes in profit margins can be explained by labor costs trade intensity. Upon conducting the out of sample test, this article offers a robust solution on predicting future earnings growth with macroeconomic factors and provides an important framework for understanding a key component of equity returns.
{"title":"From Economics to Earnings: A Macro-Based Equity Earnings Growth Forecasting Model","authors":"Kevin J. DiCiurcio, Boyu Wu, Qian Wang","doi":"10.3905/joi.2023.32.2.024","DOIUrl":"https://doi.org/10.3905/joi.2023.32.2.024","url":null,"abstract":"Earnings growth measures the change in a company’s reported net income through time. It is arguably the most widely observed measure of the growth and the profitability of a business, and a critical driver of equity returns. Though many practitioners have relied on historical averages to inform earnings growth expectations, research has found a relationship between earnings growth and prevailing economic growth. Building on prior research that connects earnings growth with real GDP growth, the authors split the earnings growth into two parts—revenue growth and changes in profit margins—and identifies multiple macroeconomic factors that have historical relationships separately with each. The authors find that revenue growth can historically be explained by world GDP growth, US GDP growth and payout ratios, and changes in profit margins can be explained by labor costs trade intensity. Upon conducting the out of sample test, this article offers a robust solution on predicting future earnings growth with macroeconomic factors and provides an important framework for understanding a key component of equity returns.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"24 - 33"},"PeriodicalIF":0.6,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48901620","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}
Mutual fund selection is a notoriously difficult task, because past performance is a poor predictor of future performance. We propose a fund performance measure that incorporates a simple idea: shrinkage, in the sense of Bayes-James-Stein, should be applied to gross return parameters, but not to fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves the prediction of out-of-sample performance relative to existing methods. The best prediction is obtained when fees are weighed five times heavier than sample returns.
众所周知，选择共同基金是一项困难的任务，因为过去的业绩对未来的业绩预测很差。我们提出了一个基金业绩指标，其中包含了一个简单的想法：Bayes James Stein意义上的收缩应该应用于总回报参数，而不是已知的费用。相对于现有方法，所提出的收缩调整夏普比（SAS）显著改进了对样本外性能的预测。当费用比样本回报重五倍时，可以获得最佳预测。
{"title":"The Shrinkage Adjusted Sharpe Ratio: An Improved Method for Mutual Fund Selection","authors":"Moshe Levy, Richard Roll","doi":"10.3905/joi.2022.1.252","DOIUrl":"https://doi.org/10.3905/joi.2022.1.252","url":null,"abstract":"Mutual fund selection is a notoriously difficult task, because past performance is a poor predictor of future performance. We propose a fund performance measure that incorporates a simple idea: shrinkage, in the sense of Bayes-James-Stein, should be applied to gross return parameters, but not to fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves the prediction of out-of-sample performance relative to existing methods. The best prediction is obtained when fees are weighed five times heavier than sample returns.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"7 - 23"},"PeriodicalIF":0.6,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41767158","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}
Empirical evidence has shown that modern portfolio theory relating to diversification had failed investors in the recent financial crises, times when investors would hope that diversification is an effective tool to sustain portfolio performance. Almost all markets around the world declined, with varying degrees, at the 2008 financial crisis and 2020 COVID-19 market crisis. Correlation-based diversification optimized portfolios were not spared, generating significant losses. Recent research on an unsupervised machine learning method of time-series clustering using Dynamic Time Warping (DTW) as a distance measure have shown research promise as a financial portfolio diversification method and shown prospects of overcoming correlation convergence issues during periods of downside event risks. This research validates the applicability of DTW cluster diversification to achieve persistent portfolio performance in international developed markets, even across periods of market weakness. Results showed outperformance of mean and median return and Sharpe metrics of optimally weighted DTW cluster diversification, against correlation-based diversification methods. The findings will augment existing literature in the use of data science approach to portfolio diversification.
{"title":"Financial Portfolio Management Based on Shaped-Based Unsupervised Machine Learning: A Dynamic Time Warping Baycenter Averaging Approach to International Markets and Periods of Downside Event Risks","authors":"Tristan Lim, Heber Ng","doi":"10.3905/joi.2022.1.251","DOIUrl":"https://doi.org/10.3905/joi.2022.1.251","url":null,"abstract":"Empirical evidence has shown that modern portfolio theory relating to diversification had failed investors in the recent financial crises, times when investors would hope that diversification is an effective tool to sustain portfolio performance. Almost all markets around the world declined, with varying degrees, at the 2008 financial crisis and 2020 COVID-19 market crisis. Correlation-based diversification optimized portfolios were not spared, generating significant losses. Recent research on an unsupervised machine learning method of time-series clustering using Dynamic Time Warping (DTW) as a distance measure have shown research promise as a financial portfolio diversification method and shown prospects of overcoming correlation convergence issues during periods of downside event risks. This research validates the applicability of DTW cluster diversification to achieve persistent portfolio performance in international developed markets, even across periods of market weakness. Results showed outperformance of mean and median return and Sharpe metrics of optimally weighted DTW cluster diversification, against correlation-based diversification methods. The findings will augment existing literature in the use of data science approach to portfolio diversification.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"74 - 96"},"PeriodicalIF":0.6,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47895124","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 article analyzes the effect of geographical diversification on global private equity (PE) fund returns. We find that there is a negative correlation between geographical diversification and PE fund returns. To establish the causality between geographical diversification and PE fund returns, we employ an instrumental variable analysis where the instrument used is the stock market capitalization of the host country where the PE fund is based. Our results apply to Net IRR, TVPI, and DPI as dependent variables used to proxy for PE fund returns in the main regression model. A one standard deviation increase in geographical diversification results in an 18.8 percent reduction in PE fund returns from a Net IRR perspective in the main regression model. Fund age and industry diversification mitigate the negative correlation between geographical diversification and fund returns. The relationship between geographical diversification and PE fund returns follows an inverted U shape function. Additional robustness tests further reinforce the findings.
{"title":"Impact of Geographical Diversification and Limited Attention on Private Equity Fund Returns","authors":"V. Ong","doi":"10.3905/joi.2022.1.248","DOIUrl":"https://doi.org/10.3905/joi.2022.1.248","url":null,"abstract":"This article analyzes the effect of geographical diversification on global private equity (PE) fund returns. We find that there is a negative correlation between geographical diversification and PE fund returns. To establish the causality between geographical diversification and PE fund returns, we employ an instrumental variable analysis where the instrument used is the stock market capitalization of the host country where the PE fund is based. Our results apply to Net IRR, TVPI, and DPI as dependent variables used to proxy for PE fund returns in the main regression model. A one standard deviation increase in geographical diversification results in an 18.8 percent reduction in PE fund returns from a Net IRR perspective in the main regression model. Fund age and industry diversification mitigate the negative correlation between geographical diversification and fund returns. The relationship between geographical diversification and PE fund returns follows an inverted U shape function. Additional robustness tests further reinforce the findings.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"34 - 52"},"PeriodicalIF":0.6,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44689759","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 article investigates the equity cross-section of real estate investment trusts (REITs) both when REITs are added as a separate portfolio to the cross-section of industries and when individual REITs are studied in isolation. A nine-factor asset pricing model which critically relies on the bankruptcy risk factor of Neumann (2021b) produces REIT portfolios which outperform the REIT market in terms of Sharpe ratio and the S&P 500 index in terms of absolute returns. The decrease in adjusted R2 of an asset pricing model when REITs are included as a separate portfolio is presented as an alternative quantification of the temporally dynamic correlation between REITs and other equity assets.
{"title":"Bankruptcy Risk and the Cross-Section of REITs","authors":"Jesse Neumann","doi":"10.3905/joi.2022.1.247","DOIUrl":"https://doi.org/10.3905/joi.2022.1.247","url":null,"abstract":"This article investigates the equity cross-section of real estate investment trusts (REITs) both when REITs are added as a separate portfolio to the cross-section of industries and when individual REITs are studied in isolation. A nine-factor asset pricing model which critically relies on the bankruptcy risk factor of Neumann (2021b) produces REIT portfolios which outperform the REIT market in terms of Sharpe ratio and the S&P 500 index in terms of absolute returns. The decrease in adjusted R2 of an asset pricing model when REITs are included as a separate portfolio is presented as an alternative quantification of the temporally dynamic correlation between REITs and other equity assets.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"120 - 131"},"PeriodicalIF":0.6,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49428251","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}
M. Gomes, Thi Ngoc Mai Le, Benjamin Williams-Rambaud
In this article, we assess the safe-haven, hedging, and diversifying properties of gold for investors located in various countries and under various economic scenarios. Specifically, we focus on G7 countries plus China and India over the 20-year period ranging from 2000 to 2020. Our empirical results show that gold is a safe haven in five out of nine countries, namely Canada, Germany, Italy, the UK, and the US. We also show that the benefits of gold depend on the existing market environment as proxied by market volatility and interest rates dynamics. Overall, our results show that gold is relevant for strategic asset allocation as it may offer investors in some countries protection against significant equity market corrections. Our empirical analyses are also relevant for tactical asset allocation as we show that the safe-haven properties of gold are time-varying and may depend upon volatility state and interest rates dynamics.
{"title":"Gold in A Portfolio: Why, When, and Where?","authors":"M. Gomes, Thi Ngoc Mai Le, Benjamin Williams-Rambaud","doi":"10.3905/joi.2022.1.246","DOIUrl":"https://doi.org/10.3905/joi.2022.1.246","url":null,"abstract":"In this article, we assess the safe-haven, hedging, and diversifying properties of gold for investors located in various countries and under various economic scenarios. Specifically, we focus on G7 countries plus China and India over the 20-year period ranging from 2000 to 2020. Our empirical results show that gold is a safe haven in five out of nine countries, namely Canada, Germany, Italy, the UK, and the US. We also show that the benefits of gold depend on the existing market environment as proxied by market volatility and interest rates dynamics. Overall, our results show that gold is relevant for strategic asset allocation as it may offer investors in some countries protection against significant equity market corrections. Our empirical analyses are also relevant for tactical asset allocation as we show that the safe-haven properties of gold are time-varying and may depend upon volatility state and interest rates dynamics.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"95 8 1","pages":"108 - 119"},"PeriodicalIF":0.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91151118","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 article shows how advanced trading analytics can help asset managers deliver improved investment outcomes for client portfolios using both indexing and alpha-seeking strategies. For alpha-seeking managers, trading analytics help right-size trades by balancing alpha capture and transaction costs optimally. For indexing managers, these tools enable more-precise and lower-cost transactions, especially around index rebalancing. Advances in data science can help overcome traditional trading challenges in fixed-income markets, such as the lack of transparency and proliferation of distinct securities.
{"title":"How Trading Analytics and Data Science Can Improve Investment Outcomes","authors":"Ananth Madhavan, S. Pasquali, P. Sommer","doi":"10.3905/joi.2022.1.245","DOIUrl":"https://doi.org/10.3905/joi.2022.1.245","url":null,"abstract":"This article shows how advanced trading analytics can help asset managers deliver improved investment outcomes for client portfolios using both indexing and alpha-seeking strategies. For alpha-seeking managers, trading analytics help right-size trades by balancing alpha capture and transaction costs optimally. For indexing managers, these tools enable more-precise and lower-cost transactions, especially around index rebalancing. Advances in data science can help overcome traditional trading challenges in fixed-income markets, such as the lack of transparency and proliferation of distinct securities.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"104 - 114"},"PeriodicalIF":0.6,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47462228","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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