Mutual fund and ETF savings plans (SPs) are becoming an increasingly important part of retail investor portfolios and many investors now adopt robo-advisors to obtain guidance on SP choices. Using data from a large online bank that introduced a robo-advising tool, I explore how robo-advice changes investors’ SP choices and document three main results. First, default options improve robo-advice users’ fund choices towards lower-cost and more diversified funds. Second, many investors – also those who previously held all-equity portfolios – adhere to the default asset allocation that is associated with a 50% equity exposure, although they could construct riskier SP portfolios through the robo-advisor. Third, I document considerable heterogeneity in longer-term adherence to robo-advisor recommendations. First-time SP users are more inert and stick to the robo-advisor’s proposed asset allocation while experienced SP users quickly readjust their equity exposure away from the robo-advisor’s recommendation. My results emphasize the power of defaults in all-digital robo-advisory services and highlight how they can improve fund choices while at the same time push investors into unsuitable asset allocations.
{"title":"Nudged into Better Portfolios and Lower Risk: Robo-Advice and Savings Decisions","authors":"Konstantin Bräuer","doi":"10.2139/ssrn.3927860","DOIUrl":"https://doi.org/10.2139/ssrn.3927860","url":null,"abstract":"Mutual fund and ETF savings plans (SPs) are becoming an increasingly important part of retail investor portfolios and many investors now adopt robo-advisors to obtain guidance on SP choices. Using data from a large online bank that introduced a robo-advising tool, I explore how robo-advice changes investors’ SP choices and document three main results. First, default options improve robo-advice users’ fund choices towards lower-cost and more diversified funds. Second, many investors – also those who previously held all-equity portfolios – adhere to the default asset allocation that is associated with a 50% equity exposure, although they could construct riskier SP portfolios through the robo-advisor. Third, I document considerable heterogeneity in longer-term adherence to robo-advisor recommendations. First-time SP users are more inert and stick to the robo-advisor’s proposed asset allocation while experienced SP users quickly readjust their equity exposure away from the robo-advisor’s recommendation. My results emphasize the power of defaults in all-digital robo-advisory services and highlight how they can improve fund choices while at the same time push investors into unsuitable asset allocations.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944962","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 paper investigates the performance and characteristics of survivor stocks in the S&P 500 index. Using both in-sample and out-of-sample comparisons, survivor stocks outperformed this market index by a considerable margin. Relative to other S&P 500 index companies, survivor stocks tend to be small-value stocks that exhibit high profitability and invest conservatively. Surprisingly, survivor stocks tend to be loser stocks with negative exposure to the momentum factor. Further analyses show that the volatility of the survivor stocks portfolio is less exposed to tail risks and responds less to shocks in the innovation process.
{"title":"On Survivor Stocks in the S&P 500 Stock Index","authors":"Klaus Grobys, J. Kolari","doi":"10.2139/ssrn.3862318","DOIUrl":"https://doi.org/10.2139/ssrn.3862318","url":null,"abstract":"This paper investigates the performance and characteristics of survivor stocks in the S&P 500 index. Using both in-sample and out-of-sample comparisons, survivor stocks outperformed this market index by a considerable margin. Relative to other S&P 500 index companies, survivor stocks tend to be small-value stocks that exhibit high profitability and invest conservatively. Surprisingly, survivor stocks tend to be loser stocks with negative exposure to the momentum factor. Further analyses show that the volatility of the survivor stocks portfolio is less exposed to tail risks and responds less to shocks in the innovation process.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133291925","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}
Spanish abstract: El desarrollo de las tecnologías financieras puede dividirse en tres etapas, la tercera, llamada Fintech 3.0 se refiere a la incursión de empresas emergentes (startups) que ingresan a competir con las grandes empresas financieras tradicionales. Por supuesto, este fenómeno tecnológico global impone nuevos retos respecto de la regulación y la supervisión de los mercados financieros. Sin embargo, para el caso de los países de Latinoamérica, lograr un mayor desarrollo de Fintech 3.0 supone una serie de desafíos especiales por los obstáculos que impone la alta concentración de los mercados financieros de la región y la manera cómo está diseñada la regulación en cada país. En este orden, el artículo sugiere algunas recomendaciones regulatorias que faciliten un mayor desarrollo y penetración de Fintech 3.0. en la región. English abstract: The development of financial technologies can be divided into three stages, the third, called Fintech 3.0, refers to the incursion of emerging companies that are entering to compete with large traditional financial companies. Of course, this global technological phenomenon imposes new challenges with respect to the regulation and the supervision of financial markets. However, in the case of Latin American countries, achieving greater development of Fintech 3.0 involves a series of special challenges due to the obstacles imposed by the high concentration of the region's financial markets and the way in which regulation is designed in each country. In this order, the article suggests some regulatory recommendations to facilitate greater development and penetration of Fintech 3.0. in the region.
{"title":"Retos y recomendaciones regulatorias respecto de Fintech 3.0 para países Latinoamericanos (Challenges and Regulatory Guidelines Regarding Fintech 3.0 for Latin American Countries)","authors":"Daniel A Monroy","doi":"10.2139/ssrn.3889750","DOIUrl":"https://doi.org/10.2139/ssrn.3889750","url":null,"abstract":"Spanish abstract: El desarrollo de las tecnologías financieras puede dividirse en tres etapas, la tercera, llamada Fintech 3.0 se refiere a la incursión de empresas emergentes (startups) que ingresan a competir con las grandes empresas financieras tradicionales. Por supuesto, este fenómeno tecnológico global impone nuevos retos respecto de la regulación y la supervisión de los mercados financieros. Sin embargo, para el caso de los países de Latinoamérica, lograr un mayor desarrollo de Fintech 3.0 supone una serie de desafíos especiales por los obstáculos que impone la alta concentración de los mercados financieros de la región y la manera cómo está diseñada la regulación en cada país. En este orden, el artículo sugiere algunas recomendaciones regulatorias que faciliten un mayor desarrollo y penetración de Fintech 3.0. en la región. English abstract: The development of financial technologies can be divided into three stages, the third, called Fintech 3.0, refers to the incursion of emerging companies that are entering to compete with large traditional financial companies. Of course, this global technological phenomenon imposes new challenges with respect to the regulation and the supervision of financial markets. However, in the case of Latin American countries, achieving greater development of Fintech 3.0 involves a series of special challenges due to the obstacles imposed by the high concentration of the region's financial markets and the way in which regulation is designed in each country. In this order, the article suggests some regulatory recommendations to facilitate greater development and penetration of Fintech 3.0. in the region.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123899780","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 Ergodicity Economics this paper shows that terminal wealth maximizing portfolios have betas that are substantially higher than the market portfolio (beta = 1). Simulations indicate that uncertainty about the future distribution of returns and the high cost of over-betting could be limiting factors to implementing such high beta portfolios. Another possibility is that investors do care about risk and are trying to maximize some form of risk adjusted growth rate.
{"title":"Ergodicity Economics and the High Beta Conundrum","authors":"Gustavo Harckbart","doi":"10.2139/ssrn.3783852","DOIUrl":"https://doi.org/10.2139/ssrn.3783852","url":null,"abstract":"Using Ergodicity Economics this paper shows that terminal wealth maximizing portfolios have betas that are substantially higher than the market portfolio (beta = 1). Simulations indicate that uncertainty about the future distribution of returns and the high cost of over-betting could be limiting factors to implementing such high beta portfolios. Another possibility is that investors do care about risk and are trying to maximize some form of risk adjusted growth rate.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133851285","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}
After long being one of the main puzzles in asset pricing, momentum has ironically become a case of observational equivalence. It can now be explained both by factors proxying for mispricing and by the investment CAPM. On top of this, q-factor theory also explains the related 52-week-high anomaly. We note that all these recent tests are unconditional exercises while the bulk of momentum profits are predictable and occur after periods of low-volatility. Comparing asset pricing models conditionally, when the strategies actually work, we find the unconditional fit is misleading. The models fit well most of the time but not when the profits are produced. The investment CAPM implies time-varying loadings that are inconsistent with the data. We proxy underreaction with earnings announcement returns and analyst forecast errors and find that it markedly decreases with volatility. This supports an underreaction channel as closer to the heart of both anomalies.
{"title":"What Explains Price Momentum and 52-Week High Momentum When They Really Work?","authors":"Pedro Barroso, Haoxu Wang","doi":"10.2139/ssrn.3716786","DOIUrl":"https://doi.org/10.2139/ssrn.3716786","url":null,"abstract":"After long being one of the main puzzles in asset pricing, momentum has ironically become a case of observational equivalence. It can now be explained both by factors proxying for mispricing and by the investment CAPM. On top of this, q-factor theory also explains the related 52-week-high anomaly. We note that all these recent tests are unconditional exercises while the bulk of momentum profits are predictable and occur after periods of low-volatility. Comparing asset pricing models conditionally, when the strategies actually work, we find the unconditional fit is misleading. The models fit well most of the time but not when the profits are produced. The investment CAPM implies time-varying loadings that are inconsistent with the data. We proxy underreaction with earnings announcement returns and analyst forecast errors and find that it markedly decreases with volatility. This supports an underreaction channel as closer to the heart of both anomalies.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114947242","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}
Our research explores the link between tokens post Initial Coin Offering (ICO) market performance in terms of returns, volumes and volatility, and the role of social media activity (namely Twitter) as information disseminator vis-a-vis project endorser. We look at a sample of token negotiated for at least two year using data from HitBTC and Twitter. Results show that twitter has mainly an endorsement role: the activity in terms of likes, replies and retweets (endorsement of the project) is associated to higher returns, higher average volume traded per day and lower return’s volatility; the activity linked to original tweets and tweets with quotes, weblinks, images and videos (information dissemination) has a very much marginal effect on post ICO tokens’ market performance. The results obtained are consistent when a different trading platform (Huobi Global) is used and robust to alternative estimation technique.
{"title":"Inform or Endorse? Twitter and the Post Initial Coin Offering Token Performance","authors":"Andrea Moro, TheVinh Truong","doi":"10.2139/ssrn.3777350","DOIUrl":"https://doi.org/10.2139/ssrn.3777350","url":null,"abstract":"Our research explores the link between tokens post Initial Coin Offering (ICO) market performance in terms of returns, volumes and volatility, and the role of social media activity (namely Twitter) as information disseminator vis-a-vis project endorser. We look at a sample of token negotiated for at least two year using data from HitBTC and Twitter. Results show that twitter has mainly an endorsement role: the activity in terms of likes, replies and retweets (endorsement of the project) is associated to higher returns, higher average volume traded per day and lower return’s volatility; the activity linked to original tweets and tweets with quotes, weblinks, images and videos (information dissemination) has a very much marginal effect on post ICO tokens’ market performance. The results obtained are consistent when a different trading platform (Huobi Global) is used and robust to alternative estimation technique.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567924","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}
It is well recognized that institutional municipal portfolio managers prefer premium bonds to those selling near par. We show that such aversion to par bonds is justified, because they are expected to underperform comparable premium or discount bonds in the near term. The extent of the underperformance depends on the shape of the yield curve, and it is positively correlated with the level of expected interest rate volatility.
The underperformance is due to tax considerations. When a municipal bond is purchased below par, the resulting gain is taxed at maturity, and the price is depressed by the present value of this tax. Due to this tax effect, the interest rate sensitivity of discount munis is amplified. Munis selling near par are also negatively convex; the potential decline due to higher interest rates exceeds the increase due to commensurately lower rates. The underperformance of near-par munis relative to those selling at a high premium or at a deep discount is due to the resulting combination of extended duration and negative convexity.
The changing value of tax liabilities creates a unique challenge in determining interest rate sensitivity and expected return, which conventional analytics fail to recognize. The tax-neutral analytics used in this paper incorporate the value of future tax costs, and provide an accurate method for predicting municipal bond price changes and investment returns.
{"title":"Par Munis -- Sub-Par Performance","authors":"A. Kalotay, Guy Davidson","doi":"10.2139/ssrn.3774232","DOIUrl":"https://doi.org/10.2139/ssrn.3774232","url":null,"abstract":"It is well recognized that institutional municipal portfolio managers prefer premium bonds to those selling near par. We show that such aversion to par bonds is justified, because they are expected to underperform comparable premium or discount bonds in the near term. The extent of the underperformance depends on the shape of the yield curve, and it is positively correlated with the level of expected interest rate volatility.<br><br>The underperformance is due to tax considerations. When a municipal bond is purchased below par, the resulting gain is taxed at maturity, and the price is depressed by the present value of this tax. Due to this tax effect, the interest rate sensitivity of discount munis is amplified. Munis selling near par are also negatively convex; the potential decline due to higher interest rates exceeds the increase due to commensurately lower rates. The underperformance of near-par munis relative to those selling at a high premium or at a deep discount is due to the resulting combination of extended duration and negative convexity.<br><br>The changing value of tax liabilities creates a unique challenge in determining interest rate sensitivity and expected return, which conventional analytics fail to recognize. The tax-neutral analytics used in this paper incorporate the value of future tax costs, and provide an accurate method for predicting municipal bond price changes and investment returns.<br>","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867560","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}
Problem Definition: Instant return credit (in short, return-credit) is a new FinTech service that offers a store credit immediately upon a return claim, without requiring the return to be received. Improving consumers’ budget, this helps convert online returns into new sales, but it also results in costly fake returns from consumers with low credit-rating. We study whether or not and how retailers should adopt return-credit and its implications on their optimal pricing strategy and profit and consumers’ welfare. Academic/Practice Relevance: This return-credit service is recently launched by a startup FinTech company, Returnly, and adopted by many retailers with impressive results. This paper is the first to provide some necessary guidance for this new practice. Methodology: We build a stylized economic model to capture the basic dynamics and tradeoff of return-credit. For a retailer selling two horizontally differentiated products, we study and compare three different cases: no, unconditional and conditional (on consumer-type) return-credit. For each case, we first optimize the consumers’ sequential decisions involving orders and return and then optimize the retailer’s strategy on pricing and return-credit. Results: The retailer should adopt the return-credit service; when adopt, she should make the return-credit conditional if no additional cost required, regardless of the product category. She should also prepare to switch to an asymmetric pricing, although the two products are symmetric (in cost and consumer valuation). This adoption, however, may hurt all consumers for low-cost products due to the retailer’s elevated pricing power by return-credit. But, making the return- credit conditional on consumer-type will almost always reward high credit-rating consumers, but penalize low credit-rating consumers. Managerial Implications: Our results provide retailers with an initial guidance and insights on how to implement return-credit and its major implications on the retailer (significant strategy change and what product-category to start implement), and consumers (both types of return-credit may hurt, but the conditional one is fairer).
{"title":"E-Tailing with FinTech: Instant Return Credit","authors":"Rong Li, Duo Shi","doi":"10.2139/ssrn.3756627","DOIUrl":"https://doi.org/10.2139/ssrn.3756627","url":null,"abstract":"Problem Definition: Instant return credit (in short, return-credit) is a new FinTech service that offers a store credit immediately upon a return claim, without requiring the return to be received. Improving consumers’ budget, this helps convert online returns into new sales, but it also results in costly fake returns from consumers with low credit-rating. We study whether or not and how retailers should adopt return-credit and its implications on their optimal pricing strategy and profit and consumers’ welfare. Academic/Practice Relevance: This return-credit service is recently launched by a startup FinTech company, Returnly, and adopted by many retailers with impressive results. This paper is the first to provide some necessary guidance for this new practice. Methodology: We build a stylized economic model to capture the basic dynamics and tradeoff of return-credit. For a retailer selling two horizontally differentiated products, we study and compare three different cases: no, unconditional and conditional (on consumer-type) return-credit. For each case, we first optimize the consumers’ sequential decisions involving orders and return and then optimize the retailer’s strategy on pricing and return-credit. Results: The retailer should adopt the return-credit service; when adopt, she should make the return-credit conditional if no additional cost required, regardless of the product category. She should also prepare to switch to an asymmetric pricing, although the two products are symmetric (in cost and consumer valuation). This adoption, however, may hurt all consumers for low-cost products due to the retailer’s elevated pricing power by return-credit. But, making the return- credit conditional on consumer-type will almost always reward high credit-rating consumers, but penalize low credit-rating consumers. Managerial Implications: Our results provide retailers with an initial guidance and insights on how to implement return-credit and its major implications on the retailer (significant strategy change and what product-category to start implement), and consumers (both types of return-credit may hurt, but the conditional one is fairer).","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133679005","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}
Tarek Issaoui, Romain Perchet, Olivier Retière, François Soupé, Chenyang Yin
Asset managers publish tactical asset allocation views regularly. The implementation of such (usually qualitative) views, in portfolios is often over-simplistic. We propose a robust framework to industrialize the construction of tailored portfolios consistent with the views. First, an unconstrained unique tactical portfolio is created by relating the conviction in each view to the allocation of risk budget to the assets underlying the view. Second, the tailored portfolios with investor-specific constraints and targets are constructed using robust portfolio optimization based on implied active returns derived from the unique unconstrained tactical portfolio. The implied returns are derived from reverse optimization using the same robust approach. Robust optimization is the core engine for the industrialization process. It produces portfolios consistent with the views while complying with constraints without requiring human intervention. Finally, a factor-based risk model endows the framework with transparency, by allowing for comparison of risk-factor exposures in portfolios with those in the original views’ exposures.
{"title":"Tailor-Made Asset Allocation: A Robust Framework to Implement Active Views","authors":"Tarek Issaoui, Romain Perchet, Olivier Retière, François Soupé, Chenyang Yin","doi":"10.2139/ssrn.3753412","DOIUrl":"https://doi.org/10.2139/ssrn.3753412","url":null,"abstract":"Asset managers publish tactical asset allocation views regularly. The implementation of such (usually qualitative) views, in portfolios is often over-simplistic. We propose a robust framework to industrialize the construction of tailored portfolios consistent with the views. First, an unconstrained unique tactical portfolio is created by relating the conviction in each view to the allocation of risk budget to the assets underlying the view. Second, the tailored portfolios with investor-specific constraints and targets are constructed using robust portfolio optimization based on implied active returns derived from the unique unconstrained tactical portfolio. The implied returns are derived from reverse optimization using the same robust approach. Robust optimization is the core engine for the industrialization process. It produces portfolios consistent with the views while complying with constraints without requiring human intervention. Finally, a factor-based risk model endows the framework with transparency, by allowing for comparison of risk-factor exposures in portfolios with those in the original views’ exposures.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129222895","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}
A new indicator of profitableness (DIRR) of the investor and recipient of the investment project is proposed, which is a generalization and development of the concept of internal rate of return (IRR). It is formed in the form of the sum of the cost of the participant's capital and the project's own profitableness, which is determined by the ratio of NPV to the value of the aggregate discounted loan debts of the investor and the recipient accumulated in the project. In projects with multiple IRR value the indicator DIRR is described by a continuous function of discount rate and smoothes the gaps in the real operating profitableness indicator. For projects with a single (simple or multiple) value of IRR, there is a complete coincidence of DIRR with the real operating profitableness of the participant. An example of building a DIRR in a project with three simple and one double IRR values is considered.
{"title":"Dynamical Internal Rate of Return for the Investment Project","authors":"A. Zhevnyak","doi":"10.2139/ssrn.3742401","DOIUrl":"https://doi.org/10.2139/ssrn.3742401","url":null,"abstract":"A new indicator of profitableness (DIRR) of the investor and recipient of the investment project is proposed, which is a generalization and development of the concept of internal rate of return (IRR). It is formed in the form of the sum of the cost of the participant's capital and the project's own profitableness, which is determined by the ratio of NPV to the value of the aggregate discounted loan debts of the investor and the recipient accumulated in the project. In projects with multiple IRR value the indicator DIRR is described by a continuous function of discount rate and smoothes the gaps in the real operating profitableness indicator. For projects with a single (simple or multiple) value of IRR, there is a complete coincidence of DIRR with the real operating profitableness of the participant. An example of building a DIRR in a project with three simple and one double IRR values is considered.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114142975","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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