Pub Date : 2023-08-28DOI: 10.1080/0013791x.2023.2245828
Tom Arnold, T. Crack, C. Marshall, Adam Schwartz
{"title":"Introducing a real option framework for EVA/MVA analysis","authors":"Tom Arnold, T. Crack, C. Marshall, Adam Schwartz","doi":"10.1080/0013791x.2023.2245828","DOIUrl":"https://doi.org/10.1080/0013791x.2023.2245828","url":null,"abstract":"","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45876482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/0013791X.2023.2229620
Xiaoshi Guo, S. Ryan
Abstract In occasions called momentum crashes, the usually effective cross-sectional momentum strategy for financial asset allocation produces drastically negative returns. We develop a stochastic mean-risk optimization model featuring CVaR to control the risk, dynamically adjusted CVaR tail probability and objective function weight, and return scenarios generated by hybrid moment-matching. In a 95-year backtest, portfolios rebalanced by our method provide higher returns and lower risk than those rebalanced by a cross-sectional momentum heuristic, while avoiding momentum crashes.
{"title":"Avoiding momentum crashes using stochastic mean-CVaR optimization with time-varying risk aversion","authors":"Xiaoshi Guo, S. Ryan","doi":"10.1080/0013791X.2023.2229620","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2229620","url":null,"abstract":"Abstract In occasions called momentum crashes, the usually effective cross-sectional momentum strategy for financial asset allocation produces drastically negative returns. We develop a stochastic mean-risk optimization model featuring CVaR to control the risk, dynamically adjusted CVaR tail probability and objective function weight, and return scenarios generated by hybrid moment-matching. In a 95-year backtest, portfolios rebalanced by our method provide higher returns and lower risk than those rebalanced by a cross-sectional momentum heuristic, while avoiding momentum crashes.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47588894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-13DOI: 10.1080/0013791X.2023.2209080
Liang-Hong Wu, Khire Rushikesh Ulhas, K. Tan, Liang-Chuan Wu
Abstract Previous Enterprise Resource Planning (ERP) evaluation methods using closed-form solutions have ignored the unique characteristics of ERP’s life cycle. We propose a lifecycle model based on simulation and stochastic processes to capture and evaluate ERP’s value, including additional key factors. Our results show that accounting for the life cycle, ERP value differs from traditional wisdom and supports decision making from various factors and scopes, considering ERP’s implementation’s unique characteristics.
{"title":"The S curve: A dynamic view of in ERP evaluation","authors":"Liang-Hong Wu, Khire Rushikesh Ulhas, K. Tan, Liang-Chuan Wu","doi":"10.1080/0013791X.2023.2209080","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2209080","url":null,"abstract":"Abstract Previous Enterprise Resource Planning (ERP) evaluation methods using closed-form solutions have ignored the unique characteristics of ERP’s life cycle. We propose a lifecycle model based on simulation and stochastic processes to capture and evaluate ERP’s value, including additional key factors. Our results show that accounting for the life cycle, ERP value differs from traditional wisdom and supports decision making from various factors and scopes, considering ERP’s implementation’s unique characteristics.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47692238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-08DOI: 10.1080/0013791X.2023.2209562
Yuehuan He, R. Kwon
Abstract In this paper, we present a mixed risk-return optimization framework for selecting long put option positions for hedging the tail risk of investments in the S&P 500 index. A tractable formulation is developed by constructing hypothetical portfolios that are constantly rolling put options. Variance and sample CVaR are used as risk measures. The models are tested against out-of-sample historical S&P 500 index values as well as the values of the index paired with long put options of varying strike prices. The optimized hedged portfolio could provide sufficient protection in market downturns while not losing significant return the long horizons. This is achieved by dynamically adjusting the put option compositions to market trends in a timely manner. Allocations to different put options are analyzed in various market trends and investor risk aversion levels. The strategy overcomes the traditional drawbacks of protective put strategies and outperforms both directly investing in the underlying asset and holding a constant long position in a particular put option.
{"title":"Optimization-based tail risk hedging of the S&P 500 index","authors":"Yuehuan He, R. Kwon","doi":"10.1080/0013791X.2023.2209562","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2209562","url":null,"abstract":"Abstract In this paper, we present a mixed risk-return optimization framework for selecting long put option positions for hedging the tail risk of investments in the S&P 500 index. A tractable formulation is developed by constructing hypothetical portfolios that are constantly rolling put options. Variance and sample CVaR are used as risk measures. The models are tested against out-of-sample historical S&P 500 index values as well as the values of the index paired with long put options of varying strike prices. The optimized hedged portfolio could provide sufficient protection in market downturns while not losing significant return the long horizons. This is achieved by dynamically adjusting the put option compositions to market trends in a timely manner. Allocations to different put options are analyzed in various market trends and investor risk aversion levels. The strategy overcomes the traditional drawbacks of protective put strategies and outperforms both directly investing in the underlying asset and holding a constant long position in a particular put option.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41611298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/0013791X.2023.2208505
H. Nachtmann
Applying engineering methods such as machine learning and optimization to financial analyses can lead to powerful outcomes as presented in the first two articles published in this issue. We also feature a case analysis demonstrating major capital investment analysis within a unique industry. I would like to thank the reviewers who contributed to the peer-review process as well as area editors David Enke, Roy Kwon, and Karen Bursic for their contributions to this issue. The issue begins with an article entitled “Intraday Trend Prediction of Stock Indices with Machine Learning Approaches” by Tang, Tang, and Yu. The authors predict price movements of Shanghai Securities 50 Index and propose three kinds of trading strategies based on machine learning approaches. Specifically, decision tree analysis, eXtreme Gradient Boosting, random forest, support vector machines (SVM), and long short-term memory are constructed and compared at different time frequencies. The results show that SVM outperforms others at the same time frequency, and overall, the findings of this article further enrich quantitative trading strategies. In “Cash Holding Management for Self-Financing Phase-able and Non-Phase-able Project Portfolio Selection and Scheduling Problems” by Langaroudi, Khosravi, Davoodi, and Movahedifar, a novel mathematical model for managing the level of cash reserves for the self-financing phase-able project portfolio selection and scheduling problem is presented. An example demonstrating their mixed-integer programming approach illustrates the applicability and performance of their model. Amir, Efendy, and Hidayat coauthored “Economic Feasibility of Developing a Mini-Salt Industry Plant A Case Study of the University of Trunojoyo Madura.” Their case study provides an economic analysis of the development of a mini-salt industry plant at the University of Trunojoyo Madura. Demonstrating net present value, internal rate of return, benefit-cost ratio, and return on investment, the project is found to be economically attractive and a feasible option for mitigating Indonesia’ salt-import dependency. The Engineering Economist journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment. For questions or inquiries, please contact me at hln@uark.edu.
{"title":"Letter from the editor","authors":"H. Nachtmann","doi":"10.1080/0013791X.2023.2208505","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2208505","url":null,"abstract":"Applying engineering methods such as machine learning and optimization to financial analyses can lead to powerful outcomes as presented in the first two articles published in this issue. We also feature a case analysis demonstrating major capital investment analysis within a unique industry. I would like to thank the reviewers who contributed to the peer-review process as well as area editors David Enke, Roy Kwon, and Karen Bursic for their contributions to this issue. The issue begins with an article entitled “Intraday Trend Prediction of Stock Indices with Machine Learning Approaches” by Tang, Tang, and Yu. The authors predict price movements of Shanghai Securities 50 Index and propose three kinds of trading strategies based on machine learning approaches. Specifically, decision tree analysis, eXtreme Gradient Boosting, random forest, support vector machines (SVM), and long short-term memory are constructed and compared at different time frequencies. The results show that SVM outperforms others at the same time frequency, and overall, the findings of this article further enrich quantitative trading strategies. In “Cash Holding Management for Self-Financing Phase-able and Non-Phase-able Project Portfolio Selection and Scheduling Problems” by Langaroudi, Khosravi, Davoodi, and Movahedifar, a novel mathematical model for managing the level of cash reserves for the self-financing phase-able project portfolio selection and scheduling problem is presented. An example demonstrating their mixed-integer programming approach illustrates the applicability and performance of their model. Amir, Efendy, and Hidayat coauthored “Economic Feasibility of Developing a Mini-Salt Industry Plant A Case Study of the University of Trunojoyo Madura.” Their case study provides an economic analysis of the development of a mini-salt industry plant at the University of Trunojoyo Madura. Demonstrating net present value, internal rate of return, benefit-cost ratio, and return on investment, the project is found to be economically attractive and a feasible option for mitigating Indonesia’ salt-import dependency. The Engineering Economist journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment. For questions or inquiries, please contact me at hln@uark.edu.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41921641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/0013791X.2023.2205858
M. Efendy, M. Syarif, Nizar Amir, R. Hidayat
Abstract Indonesia is a net salt importer with plans to eliminate salt import dependency through the industry’s development and collaboration with higher education institutions. Considering net present value (NPV), internal rate of return (IRR), benefit–cost ratio (B/C ratio), and return on investment (ROI), this case study analyzes the economic feasibility of developing a mini salt production plant. All the economic indices suggest that the project is feasible, specifically NPV, IRR, net B/C ratio, and ROI are IDR 5.3 billion, 42.83%, 1.61, and 395% respectively over 10 years. Thus, the project is economically feasible and can help eliminate import dependency and the higher educational institution’s outstanding contribution.
{"title":"Economic Feasibility Case Study of Developing a Salt Production Plant","authors":"M. Efendy, M. Syarif, Nizar Amir, R. Hidayat","doi":"10.1080/0013791X.2023.2205858","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2205858","url":null,"abstract":"Abstract Indonesia is a net salt importer with plans to eliminate salt import dependency through the industry’s development and collaboration with higher education institutions. Considering net present value (NPV), internal rate of return (IRR), benefit–cost ratio (B/C ratio), and return on investment (ROI), this case study analyzes the economic feasibility of developing a mini salt production plant. All the economic indices suggest that the project is feasible, specifically NPV, IRR, net B/C ratio, and ROI are IDR 5.3 billion, 42.83%, 1.61, and 395% respectively over 10 years. Thus, the project is economically feasible and can help eliminate import dependency and the higher educational institution’s outstanding contribution.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44624874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/0013791X.2023.2205841
Pan Tang, Xin Tang, Wentao Yu
Abstract In recent years, as research at the intersection of machine learning and finance has grown, predicting stock price movements has become a particularly intriguing issue. Current research focuses primarily on using historical data of the previous day to predict stock movements for the following day, whereas fewer studies use the trading day’s opening data to predict market movements for the current day. We predict intraday price movements of the SSE-50 (Shanghai Securities 50 Index) using stock market opening data as input. Specifically, decision tree, extreme gradient boosting (XGBoost), random forest, support vector machines (SVM), and long-short-term memory are developed to predict the movements of the SSE-50 index utilizing opening price data of various time intervals. We also design three trading strategies when different time frequencies of data are used. At the same time-frequency, the results demonstrate that SVM with Gaussian and linear kernels outperform others. The forecasting accuracy at 10-min frequency approaches 70%, which is close to the results at longer time intervals, indicating that intraday trend can be determined by opening price fluctuations and the first 10-min data contains sufficient information to predict the trend for the entire trading day. In addition, trading methods based on the forecast of daily, weekly, and monthly SSE-50 price movement outperform buy-and-hold strategies. Daily trading performs better than the other two strategies. The outcomes of this research can expand the use of machine learning in quantitative trading and enrich intraday trading techniques further.
{"title":"Intraday trend prediction of stock indices with machine learning approaches","authors":"Pan Tang, Xin Tang, Wentao Yu","doi":"10.1080/0013791X.2023.2205841","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2205841","url":null,"abstract":"Abstract In recent years, as research at the intersection of machine learning and finance has grown, predicting stock price movements has become a particularly intriguing issue. Current research focuses primarily on using historical data of the previous day to predict stock movements for the following day, whereas fewer studies use the trading day’s opening data to predict market movements for the current day. We predict intraday price movements of the SSE-50 (Shanghai Securities 50 Index) using stock market opening data as input. Specifically, decision tree, extreme gradient boosting (XGBoost), random forest, support vector machines (SVM), and long-short-term memory are developed to predict the movements of the SSE-50 index utilizing opening price data of various time intervals. We also design three trading strategies when different time frequencies of data are used. At the same time-frequency, the results demonstrate that SVM with Gaussian and linear kernels outperform others. The forecasting accuracy at 10-min frequency approaches 70%, which is close to the results at longer time intervals, indicating that intraday trend can be determined by opening price fluctuations and the first 10-min data contains sufficient information to predict the trend for the entire trading day. In addition, trading methods based on the forecast of daily, weekly, and monthly SSE-50 price movement outperform buy-and-hold strategies. Daily trading performs better than the other two strategies. The outcomes of this research can expand the use of machine learning in quantitative trading and enrich intraday trading techniques further.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46116708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-10DOI: 10.1080/0013791X.2023.2172242
Seyed Mahdi Mirkhorsandi Langaroudi, H. Khosravi, Alireza Davoodi, S. M. Movahedifar
Abstract This article presents a novel mathematical model for managing the level of cash reserves for the self-financing phase-able as well as non-phase-able project portfolio selection and scheduling problems. Practically, the executive managers of project-oriented organizations tend to keep cash within their organization to increase their decision-making power. Although this allows managers and investors to invest in future economic projects, it imposes opportunity costs on owners and investors, in which case maintaining cash reserves can turn out to be a major challenge between owners and executive managers. This issue becomes more acute when the project-based organization operates self-financing. Because the financing is limited to the revenues of the finished projects, money withdrawn from the project account without proper management exacerbates financial constraints. Consequently, managers will bypass some future valuable investment opportunities. In this article, from the point of view of cash holding, the expectations of managers and investors in a self-financing phase-able and non-phase-able project-based organization will be met simultaneously. The proposed model is a nonlinear integer program. After linearization, an example is provided to illustrate the applicability and performance of the proposed model.
{"title":"Cash holding management for self-financing phase-able and non-phase-able project portfolio selection and scheduling problems","authors":"Seyed Mahdi Mirkhorsandi Langaroudi, H. Khosravi, Alireza Davoodi, S. M. Movahedifar","doi":"10.1080/0013791X.2023.2172242","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2172242","url":null,"abstract":"Abstract This article presents a novel mathematical model for managing the level of cash reserves for the self-financing phase-able as well as non-phase-able project portfolio selection and scheduling problems. Practically, the executive managers of project-oriented organizations tend to keep cash within their organization to increase their decision-making power. Although this allows managers and investors to invest in future economic projects, it imposes opportunity costs on owners and investors, in which case maintaining cash reserves can turn out to be a major challenge between owners and executive managers. This issue becomes more acute when the project-based organization operates self-financing. Because the financing is limited to the revenues of the finished projects, money withdrawn from the project account without proper management exacerbates financial constraints. Consequently, managers will bypass some future valuable investment opportunities. In this article, from the point of view of cash holding, the expectations of managers and investors in a self-financing phase-able and non-phase-able project-based organization will be met simultaneously. The proposed model is a nonlinear integer program. After linearization, an example is provided to illustrate the applicability and performance of the proposed model.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49077037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/0013791X.2023.2179709
Eda Boltürk, Elif Haktanır
Abstract Cost-benefit analysis is a benefit measurement method that compares the cost required to realize the product, service or result with the benefits to be obtained. It allows future earnings to be calculated with the present value of money and different projects can be compared. In this study, the uncertainties of decision maker inputs were reflected in a more realistic way by dealing with the cost-benefit analysis under decomposed fuzzy sets. The contribution of this study to the literature are the decomposed fuzzy linguistic term scale, which is proposed for the first time, and the new equations and formulations developed for cost-benefit analysis under fuzziness. This study also contributes to the successful applicability of engineering economics issues and financial analysis methods under fuzziness.
{"title":"Decomposed fuzzy cost-benefit analysis and an application on ophthalmologic robot selection","authors":"Eda Boltürk, Elif Haktanır","doi":"10.1080/0013791X.2023.2179709","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2179709","url":null,"abstract":"Abstract Cost-benefit analysis is a benefit measurement method that compares the cost required to realize the product, service or result with the benefits to be obtained. It allows future earnings to be calculated with the present value of money and different projects can be compared. In this study, the uncertainties of decision maker inputs were reflected in a more realistic way by dealing with the cost-benefit analysis under decomposed fuzzy sets. The contribution of this study to the literature are the decomposed fuzzy linguistic term scale, which is proposed for the first time, and the new equations and formulations developed for cost-benefit analysis under fuzziness. This study also contributes to the successful applicability of engineering economics issues and financial analysis methods under fuzziness.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45840845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/0013791X.2023.2179708
W. M. Grimm
Abstract The commonly used compound annual growth rate does not consider volatility, and its calculation fails for time series beginning or terminating with a zero or negative value, which may be the case for a company’s earnings history. Thus, a modification of the standard definition is proposed, derived from a covariance-invariant mapping of observations to a two-parameter exponential model. The novel growth rate is called “covariance-invariant “, which becomes for the special case of steady growth at a constant rate. It can be obtained using different options such as a chart, look-up table or formula. Further, the extension of the model by an additive constant may be used if negative values dominate. The approach is viewed as easy to apply as the log-linear model but with a superior performance. Compared to nonlinear least-squares regression, unique solutions can be obtained that allow a rather quick calculation.
{"title":"On volatile growth: Simple fitting of exponential functions taking into account values of every observation with any signs, applied to readily calculate a novel covariance-invariant CAGR","authors":"W. M. Grimm","doi":"10.1080/0013791X.2023.2179708","DOIUrl":"https://doi.org/10.1080/0013791X.2023.2179708","url":null,"abstract":"Abstract The commonly used compound annual growth rate does not consider volatility, and its calculation fails for time series beginning or terminating with a zero or negative value, which may be the case for a company’s earnings history. Thus, a modification of the standard definition is proposed, derived from a covariance-invariant mapping of observations to a two-parameter exponential model. The novel growth rate is called “covariance-invariant “, which becomes for the special case of steady growth at a constant rate. It can be obtained using different options such as a chart, look-up table or formula. Further, the extension of the model by an additive constant may be used if negative values dominate. The approach is viewed as easy to apply as the log-linear model but with a superior performance. Compared to nonlinear least-squares regression, unique solutions can be obtained that allow a rather quick calculation.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42378241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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