Abstract Both cryptocurrencies and gold are scarce, expensive for extraction, and less affected by money supply. We focus on these similarities and investigate whether cryptocurrency network affects impact on expected return on gold. Our results show that the number of cryptocurrency wallet users is positively related to the expected return on gold. Moreover, we employed a machine-learning approach and considered the interactions among predictors. We reveal that network factors have a greater impact on gold than returns on Bitcoin and other macroeconomic and financial variables.
{"title":"Cryptocurrency Network Factors and Gold","authors":"Kei Nakagawa, Ryuta Sakemoto","doi":"10.2139/ssrn.3846614","DOIUrl":"https://doi.org/10.2139/ssrn.3846614","url":null,"abstract":"Abstract Both cryptocurrencies and gold are scarce, expensive for extraction, and less affected by money supply. We focus on these similarities and investigate whether cryptocurrency network affects impact on expected return on gold. Our results show that the number of cryptocurrency wallet users is positively related to the expected return on gold. Moreover, we employed a machine-learning approach and considered the interactions among predictors. We reveal that network factors have a greater impact on gold than returns on Bitcoin and other macroeconomic and financial variables.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"19 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133072731","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}
Berk and Green (2004) argue that, in equilibrium, mutual fund performance is not informative about the skill of the fund managers. Hence, instead of studying mutual fund performance, we study stock recommendations of prominent money managers made at the prestigious Barron’s Roundtable. The 3,472 buy recommendations, from 1968 to 2019 (52 years), on average, earn economically meaningful and statistically significant 4.1% excess returns over the 30 trading days immediately following the Roundtable meetings. To study the skill level across decades, we divide the 52-year-long time-series into four equal 13-year-long periods and find that excess returns are similar for each of the four periods and are statistically significant. Overall, we conclude that money managers are skillful, and that skill has not diminished over the decades.
{"title":"Yes, Virginia, there are Superstar Money Managers","authors":"V. Dimitrov, Prem C. Jain","doi":"10.2139/ssrn.3885288","DOIUrl":"https://doi.org/10.2139/ssrn.3885288","url":null,"abstract":"Berk and Green (2004) argue that, in equilibrium, mutual fund performance is not informative about the skill of the fund managers. Hence, instead of studying mutual fund performance, we study stock recommendations of prominent money managers made at the prestigious Barron’s Roundtable. The 3,472 buy recommendations, from 1968 to 2019 (52 years), on average, earn economically meaningful and statistically significant 4.1% excess returns over the 30 trading days immediately following the Roundtable meetings. To study the skill level across decades, we divide the 52-year-long time-series into four equal 13-year-long periods and find that excess returns are similar for each of the four periods and are statistically significant. Overall, we conclude that money managers are skillful, and that skill has not diminished over the decades.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133094096","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}
I employ the seeded Latent Dirichlet Process (sLDA) model in natural language processing to extract the narratives discussed by Shiller (2019) from nearly seven million New York Times articles over the past 150 years. The estimation scheme is designed to avoid any look-ahead bias in constructing the monthly narrative weights. Among the narratives considered, the most important one is Panic, which encompasses various stress- and anxiety-related themes including economic downturns, wars, political tensions, and epidemics. I find that Panic and a narrative index that loads heavily on Panic are strong positive predictors of excess U.S. market return and negative predictors of both realized and implied market volatility. I document empirical support for Panic as a proxy for time-varying risk aversion, consistent with a univariate version of the intertemporal capital asset pricing model (ICAPM). The predictability of narratives over market returns holds at both market and portfolio level and at both monthly and daily interval, and importantly is increasing over time.
{"title":"Economic Narratives and Market Outcomes: A Semi-supervised Topic Modeling Approach","authors":"Dat Mai","doi":"10.2139/ssrn.3883747","DOIUrl":"https://doi.org/10.2139/ssrn.3883747","url":null,"abstract":"I employ the seeded Latent Dirichlet Process (sLDA) model in natural language processing to extract the narratives discussed by Shiller (2019) from nearly seven million New York Times articles over the past 150 years. The estimation scheme is designed to avoid any look-ahead bias in constructing the monthly narrative weights. Among the narratives considered, the most important one is Panic, which encompasses various stress- and anxiety-related themes including economic downturns, wars, political tensions, and epidemics. I find that Panic and a narrative index that loads heavily on Panic are strong positive predictors of excess U.S. market return and negative predictors of both realized and implied market volatility. I document empirical support for Panic as a proxy for time-varying risk aversion, consistent with a univariate version of the intertemporal capital asset pricing model (ICAPM). The predictability of narratives over market returns holds at both market and portfolio level and at both monthly and daily interval, and importantly is increasing over time.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127244901","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}
Financial Technologies have transformed banking and financial services operations worldwide over the last decade. It has simplified the customer's and banking authorities' lives significantly. The paper reviews briefly the most commonly used financial technologies (Fintech) – Artificial Intelligence, Machine Learning, Blockchain, Big Data and its applications in Banking and Financial services industry (BFSI). Fintech applications such as - Fraud detection and prevention, Portfolio management (Robo-advisors), Loan underwriting, Robotic Process Automation (RPA), Digital Payments, Insuretech and Regtech etc. have been explored in the paper in the context of BFSI. It also studies the impact, opportunities and challenges of financial technologies for BFSI. Further, the paper studies udy the experiences of leading Indian Banks and Financial Services organizations in respect of adoption of financial technologies. The study presents the experiences of Fintech adaptations by three leading Indian Banks – ICICI, HDFC and SBI. The study highlights that the revolution Fintech set in motion has had a profound impact on traditional banking and financial service sectors in terms of operations. Originally used for backend services, it is currently applied in many additional applications such as online payments, mobile payments, fund management, and stock trading. The study concludes that Fintech has changed the financial ecosystem and transformed the basic process of payment methods via the use of digital technology.
{"title":"Fintech Applications in Banking and Financial Services Industry in India","authors":"Rashmi Dabbeeru, D. Rao","doi":"10.2139/ssrn.3881967","DOIUrl":"https://doi.org/10.2139/ssrn.3881967","url":null,"abstract":"Financial Technologies have transformed banking and financial services operations worldwide over the last decade. It has simplified the customer's and banking authorities' lives significantly. The paper reviews briefly the most commonly used financial technologies (Fintech) – Artificial Intelligence, Machine Learning, Blockchain, Big Data and its applications in Banking and Financial services industry (BFSI). Fintech applications such as - Fraud detection and prevention, Portfolio management (Robo-advisors), Loan underwriting, Robotic Process Automation (RPA), Digital Payments, Insuretech and Regtech etc. have been explored in the paper in the context of BFSI. It also studies the impact, opportunities and challenges of financial technologies for BFSI. Further, the paper studies udy the experiences of leading Indian Banks and Financial Services organizations in respect of adoption of financial technologies. The study presents the experiences of Fintech adaptations by three leading Indian Banks – ICICI, HDFC and SBI. The study highlights that the revolution Fintech set in motion has had a profound impact on traditional banking and financial service sectors in terms of operations. Originally used for backend services, it is currently applied in many additional applications such as online payments, mobile payments, fund management, and stock trading. The study concludes that Fintech has changed the financial ecosystem and transformed the basic process of payment methods via the use of digital technology.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116862314","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}
Policing Cyberspace in South Africa and Nigeria: Challenges and Opportunities The banking sector, as a centralized aspect of contemporary digital finance systems, is a vulnerable target. Not surprisingly, recent data indicate that cyberattacks have augmented by 300% over the past three years in Africa (Open Access Government). As per Kris Budnik and Kent Kirkwood, leaders in Cybersecurity services for PwC South Africa, this dramatic rise roots in “the tremendous value of information financial services they hold” (Budnik and Kirkwood). This trend was facilitated by the integration of “internet-based” transactions in the banking system, and ramping up internet penetration rates in countries like South Africa and Nigeria (Open Access Government). According to a 2020 McKinsey report, banks seek to expand market share by advertising the accessibility and popularity of their online services. Banks offer Mobile Financial Services (MFS), which include a range of services from payments, loans, investments, to insurance (Kola-Oyeneyin et al.). As evidenced in Exhibit 1, after South Africa reached the 100 million threshold, mobile money deployments have comprised the largest share of accounts (Kola-Oyeneyin et al.). The complexity of cyberspace and the sophistication of criminal actors creates numerous opportunities for potential abuse. As a result, it is in a society’s best interests to ensure that effective regulations are in place. When a cybercrime is committed, financial institutions suffer theft of funds, weakening of intellectual property, and loss of other valuable assets along with legitimacy. To combat cybercrime, emerging cashless economies need to invest in education and technology to tailor cybersecurity strategies to the local, regional, and international contexts. Since 2014, South Africa and Nigeria have taken a regional lead in developing and implementing financial technology. Unsurprisingly, South Africa is now considered “a new cybercrime harbor” (Open Access Government). Both countries are highly vulnerable to cyberattacks, which creates an interesting trend to monitor.
{"title":"Cybersecurity Challenges and Governance Issues in the Cyberspace 'When Stronger Passwords Are not Enough: Governing Cyberspace in Contemporary African Nations' Case Study: Can South Africa and Nigeria Secure Cyberspace without a Lock?","authors":"A. Gaillard","doi":"10.2139/ssrn.3877526","DOIUrl":"https://doi.org/10.2139/ssrn.3877526","url":null,"abstract":"Policing Cyberspace in South Africa and Nigeria: Challenges and Opportunities The banking sector, as a centralized aspect of contemporary digital finance systems, is a vulnerable target. Not surprisingly, recent data indicate that cyberattacks have augmented by 300% over the past three years in Africa (Open Access Government). As per Kris Budnik and Kent Kirkwood, leaders in Cybersecurity services for PwC South Africa, this dramatic rise roots in “the tremendous value of information financial services they hold” (Budnik and Kirkwood). This trend was facilitated by the integration of “internet-based” transactions in the banking system, and ramping up internet penetration rates in countries like South Africa and Nigeria (Open Access Government). According to a 2020 McKinsey report, banks seek to expand market share by advertising the accessibility and popularity of their online services. Banks offer Mobile Financial Services (MFS), which include a range of services from payments, loans, investments, to insurance (Kola-Oyeneyin et al.). As evidenced in Exhibit 1, after South Africa reached the 100 million threshold, mobile money deployments have comprised the largest share of accounts (Kola-Oyeneyin et al.). The complexity of cyberspace and the sophistication of criminal actors creates numerous opportunities for potential abuse. As a result, it is in a society’s best interests to ensure that effective regulations are in place. When a cybercrime is committed, financial institutions suffer theft of funds, weakening of intellectual property, and loss of other valuable assets along with legitimacy. To combat cybercrime, emerging cashless economies need to invest in education and technology to tailor cybersecurity strategies to the local, regional, and international contexts. Since 2014, South Africa and Nigeria have taken a regional lead in developing and implementing financial technology. Unsurprisingly, South Africa is now considered “a new cybercrime harbor” (Open Access Government). Both countries are highly vulnerable to cyberattacks, which creates an interesting trend to monitor.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125502901","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 follows directly from the paper “BigFintechs and their Impact on Sustainable Development”, which examines the positive and negative impacts of BigFintech (BFT) activities across the full spectrum of the Sustainable Development Goals (SDGs), particularly with regard to Least Developed Countries (LDCs). This paper serves as an extension of the analysis, specifically on the findings with regard to SDG 16 (peace, justice and strong institutions) to focus on the macroeconomic impact of BFT actors and activities on LDCs. To accomplish the extended analysis, we first address the limitations in bridging BFT activity, SDG indicators and LDC macroeconomic policy impacts. We draw upon the outline of the complex and opaque supply chains, expanding service offerings across multiple business verticals, and the complex ecosystem models that amplify BFT impacts for LDCs. We discuss the key barriers in advancing the analysis including the limitations of the frameworks, tools, indicators and data, to measure the macroeconomic impacts, particularly within the LDC context. Our findings demonstrate that the current narrative ‘digital economy’ sees digital growth, maturity, and market penetration in LDCs largely as positive developments. However, it fails to address the potential for adverse impacts on LDCs specifically owing to BFTs’ complex models and business activities. We then outline the regulatory challenges related to BFT across multiple factors including the cross-border nature of BFT ecosystems and activities, the narrow scope of those digital services, the limitations of foreign exchange rules and taxation classification.
In this paper, we set out the positive impacts of BFTs on reducing inequalities, access to capital, increased employment and entrepreneurship and GDP growth, as well as more complex negative implications including potential tax avoidance, crowding out of local businesses and SMEs, evolving ecosystems of interdependence with single points of failure, the potential for draining liquidity from local financial systems and for currency substitution. We discuss the regulatory capacity to address these macroeconomic impacts as well as the new potential points of failure being introduced. The paper then addresses the limitations of current governance parameters and structures that are struggling to keep up with the power, size and complex business model evolution of BFTs generally.
The paper points to a widening gap in terms of primary or targeted regulatory focus for LDCs, which are not generally within the scope or mandate of regulators and legislators in more developed markets where most BFTs are headquartered. A summary of the potential macroeconomic impacts and regulatory challenges is followed by a series of conclusions indicating that rather than expanding their current value proposition of financial inclusion, employment and economic growth, BFTs can actually bypass or exploit segments of communities in LDCs with
{"title":"BigFintechs and Their Impacts on Macroeconomic Policies","authors":"K. Foster, Sofie Blakstad, Sangita Gazi, M. Bos","doi":"10.2139/ssrn.3871371","DOIUrl":"https://doi.org/10.2139/ssrn.3871371","url":null,"abstract":"This paper follows directly from the paper “BigFintechs and their Impact on Sustainable Development”, which examines the positive and negative impacts of BigFintech (BFT) activities across the full spectrum of the Sustainable Development Goals (SDGs), particularly with regard to Least Developed Countries (LDCs). This paper serves as an extension of the analysis, specifically on the findings with regard to SDG 16 (peace, justice and strong institutions) to focus on the macroeconomic impact of BFT actors and activities on LDCs. To accomplish the extended analysis, we first address the limitations in bridging BFT activity, SDG indicators and LDC macroeconomic policy impacts. We draw upon the outline of the complex and opaque supply chains, expanding service offerings across multiple business verticals, and the complex ecosystem models that amplify BFT impacts for LDCs. We discuss the key barriers in advancing the analysis including the limitations of the frameworks, tools, indicators and data, to measure the macroeconomic impacts, particularly within the LDC context. Our findings demonstrate that the current narrative ‘digital economy’ sees digital growth, maturity, and market penetration in LDCs largely as positive developments. However, it fails to address the potential for adverse impacts on LDCs specifically owing to BFTs’ complex models and business activities. We then outline the regulatory challenges related to BFT across multiple factors including the cross-border nature of BFT ecosystems and activities, the narrow scope of those digital services, the limitations of foreign exchange rules and taxation classification.<br><br>In this paper, we set out the positive impacts of BFTs on reducing inequalities, access to capital, increased employment and entrepreneurship and GDP growth, as well as more complex negative implications including potential tax avoidance, crowding out of local businesses and SMEs, evolving ecosystems of interdependence with single points of failure, the potential for draining liquidity from local financial systems and for currency substitution. We discuss the regulatory capacity to address these macroeconomic impacts as well as the new potential points of failure being introduced. The paper then addresses the limitations of current governance parameters and structures that are struggling to keep up with the power, size and complex business model evolution of BFTs generally. <br><br>The paper points to a widening gap in terms of primary or targeted regulatory focus for LDCs, which are not generally within the scope or mandate of regulators and legislators in more developed markets where most BFTs are headquartered. A summary of the potential macroeconomic impacts and regulatory challenges is followed by a series of conclusions indicating that rather than expanding their current value proposition of financial inclusion, employment and economic growth, BFTs can actually bypass or exploit segments of communities in LDCs with ","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131008984","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 this research I empirically study the effects of information acquisition by investors or traders on analysts' forecast bias. Based on the theoretical literature on sell-side analysts, I argue that forecast bias is correlated to investors' information gathering, in two opposite directions. On the one hand, higher levels of reading activities about individual firms by investors induce analysts to issue more optimistic forecasts if the potential for trading is higher. On the other hand, higher levels of reading activities about individual firms by investors help them identify opportunistic behaviors and thus to discipline analysts. I find that investors' information acquisition is positively related to analysts' optimism when the potential for trading is larger, and negatively related to optimism when investors are more likely to identify inflated forecasts. Together, these results suggest that information acquisition is not only correlated to analysts' optimism but also that its effect does not work trivially and solely in one direction but it activates two different incentives in analysts' decisions.
{"title":"The Effects of Investors' Information Acquisition On Sell-Side Analysts Forecast Bias","authors":"J. Astaiza-Gómez","doi":"10.2139/ssrn.3870796","DOIUrl":"https://doi.org/10.2139/ssrn.3870796","url":null,"abstract":"In this research I empirically study the effects of information acquisition by investors or traders on analysts' forecast bias. Based on the theoretical literature on sell-side analysts, I argue that forecast bias is correlated to investors' information gathering, in two opposite directions. On the one hand, higher levels of reading activities about individual firms by investors induce analysts to issue more optimistic forecasts if the potential for trading is higher. On the other hand, higher levels of reading activities about individual firms by investors help them identify opportunistic behaviors and thus to discipline analysts. I find that investors' information acquisition is positively related to analysts' optimism when the potential for trading is larger, and negatively related to optimism when investors are more likely to identify inflated forecasts. Together, these results suggest that information acquisition is not only correlated to analysts' optimism but also that its effect does not work trivially and solely in one direction but it activates two different incentives in analysts' decisions.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130763124","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}
We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test advanced reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. The reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The Carhart four-factor analysis also indicates that the diversified reward-risk parity allocations gain superior performance.
{"title":"Diversified Reward-Risk Parity in Portfolio Construction","authors":"Jaehyung Choi, H. Kim, Y. S. Kim","doi":"10.2139/ssrn.3871944","DOIUrl":"https://doi.org/10.2139/ssrn.3871944","url":null,"abstract":"We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test advanced reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. The reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The Carhart four-factor analysis also indicates that the diversified reward-risk parity allocations gain superior performance.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117030293","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 idea behind the optimal ESG portfolio (OESGP) is to expand the mean variance theory by adding the portfolio ESG value (PESGV) multiplied by the ESG strength parameter γ (which is investor’s choice) to the minimizing objective function (Pederson et al., 2019; Schmidt, 2020). PESGV is assumed to be the sum of portfolio constituents’ weighted ESG ratings that are offered by several providers. In this work we analyze the sensitivity of the OESGP based on the constituents of the Dow Jones Index to the ESG ratings provided by MSCI, S&P Global, and Sustainalytics. We describe discrepancies among various ESG ratings for the same securities and their effects on the OESGP performance. We found that the OESGP diversity decreases with growing γ. The dependence of the ESG tilted Sharpe ratio on γ may have two maximums. The 1st maximum exists at moderate values of γ and yields a moderately diversified OESGP. The 2nd maximum at large γ corresponds to a highly concentrated OESGP. It appears if portfolio has one or two securities with lucky combinations of high returns and high ESG ratings.
{"title":"Optimal ESG Portfolios: Which ESG Ratings to Use?","authors":"A. Schmidt, Xu Zhang","doi":"10.2139/ssrn.3859674","DOIUrl":"https://doi.org/10.2139/ssrn.3859674","url":null,"abstract":"The idea behind the optimal ESG portfolio (OESGP) is to expand the mean variance theory by adding the portfolio ESG value (PESGV) multiplied by the ESG strength parameter γ (which is investor’s choice) to the minimizing objective function (Pederson et al., 2019; Schmidt, 2020). PESGV is assumed to be the sum of portfolio constituents’ weighted ESG ratings that are offered by several providers. In this work we analyze the sensitivity of the OESGP based on the constituents of the Dow Jones Index to the ESG ratings provided by MSCI, S&P Global, and Sustainalytics. We describe discrepancies among various ESG ratings for the same securities and their effects on the OESGP performance. We found that the OESGP diversity decreases with growing γ. The dependence of the ESG tilted Sharpe ratio on γ may have two maximums. The 1st maximum exists at moderate values of γ and yields a moderately diversified OESGP. The 2nd maximum at large γ corresponds to a highly concentrated OESGP. It appears if portfolio has one or two securities with lucky combinations of high returns and high ESG ratings.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"R-32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126634268","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}
Apostolos G. Katsafados, George N. Leledakis, Emmanouil G. Pyrgiotakis, I. Androutsopoulos, Emmanouel Fergadiotis
This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.
{"title":"Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach","authors":"Apostolos G. Katsafados, George N. Leledakis, Emmanouil G. Pyrgiotakis, I. Androutsopoulos, Emmanouel Fergadiotis","doi":"10.2139/ssrn.3848854","DOIUrl":"https://doi.org/10.2139/ssrn.3848854","url":null,"abstract":"This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.","PeriodicalId":377322,"journal":{"name":"Investments eJournal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331472","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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