From Ideas to Practice, Pilots to Strategy: Practical Solutions and Actionable Insights on How to Do Impact Investing

This report is the second publication in the World Economic Forum's Mainstreaming Impact Investing Initiative. The report takes a deeper look at why and how asset owners began to include impact investing in their portfolios and continue to do so today, and how they overcame operational and cultural constraints affecting capital flow. Given that impact investing expertise is spread among dozens if not hundreds of practitioners and academics, the report is a curation of some -- but certainly not all -- of those leading voices. The 15 articles are meant to provide investors, intermediaries and policy-makers with actionable insights on how to incorporate impact investing into their work.The report's goals are to show how mainstream investors and intermediaries have overcome the challenges in the impact investment sector, and to democratize the insights and expertise for anyone and everyone interested in the field. Divided into four main sections, the report contains lessons learned from practitioner's experience, and showcases best practices, organizational structures and innovative instruments that asset owners, asset managers, financial institutions and impact investors have successfully implemented

Solutions for Impact Investors: From Strategy to Implementation

In writing this monograph, our main goal is to provide impact investors with tools to tighten the link between their investment decisions and impact creation. Our intent is threefold: to attract more capital to impact investing; to assist impact investors as they move from organizational change to executing and refining their impact investment decision-making process; and to narrow the gap within foundations between program professionals and investment professionals thereby contributing to a mutual understanding and implementation of a portfolio approach to impact investing.Additionally, we intend to help break down the barriers making it difficult to identify opportunities in impact investing. To this end, we provide examples throughout the monograph and at www.rockpa.org/impactinvesting of impact investment opportunities in most major asset classes.While we understand the important role that impact investors can play in providing financial capital, we also want to acknowledge the wide range of non-financial resources needed to address the world's problems. Our intent with this monograph is not to provide a comprehensive list of investments across asset classes nor any type of investment advice with regard to the selected profiles. We strongly encourage the reader to conduct their own assessment and evaluation for risk and suitability before considering any investment

Steering Capital: Optimizing Financial Support for Innovation in Public Education

Examines efforts to align capital to education innovation and calls for clarity and agreement on problems, goals, and metrics; an effective R&D system; an evidence-based culture of continuous improvement; and transparent, comparable, and useful data

Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis

This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bidirectional LSTM) BiLSTM model which is a variation of the LSTM model to predict stock closing price. The stock prediction is applied to a selected company from the Indonesian stock market using historical prices. The model is then evaluated using metrics Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). A graphical comparison between the actual price and predicted price of the stock is charted to study the stock price movement. To study the impact during COVID-19 on the stock prices, an intervention analysis is conducted along with the Wilcoxon model. The stock price prediction model can forecast the price of stocks before and during the financial crisis with minimal error. The intervention analysis result showed that health sectors have a positive effect while other sectors such as transportation, finance, information technology, and entertainment have a negative effect during the financial crisis of COVID-19. Being able to analyze and study the stock price movement of stocks is beneficial to investors in understanding the impact of the financial crisis on some industries and the behavior of certain stocks or industries under the circumstances which can lead to alternate investment strategies and decision making

Adaptation of domestic state governance to international governance models

The purpose of the article is to provide the evolving international trends of modern management models and authorial vision of model of state governance system in Ukraine, its subsystems, in particular, the system of provision of administrative services that is appropriate for the contemporary times. Methodology. On the basis of scientific and theoretical approaches to the definitions of terms “state governance” and “public governance”, there was an explanation of considerable difference between them and, taking into consideration, the mentality of Ukrainian society and peculiar weak side in self-organization, the authors offered to form authorial model of governance on the basis of historically traditional for Ukraine model of state governance and to add some elements of management concepts that proved their significance, efficiency and priority in practice. Results. The authors emphasized the following two prevailing modern management models in the international practice: “new state management” and “good governance”. The first concept offered for consideration served as a basis for the semantic content of state activity that reflects more the state of administrative reformation. Practical meaning. A practical introduction of management to the domestic model of governance creates the range of contradictions that do not allow implementing herein concept. Pursuant to authors, the second one allows in considerable measure to reform state governance, considering historically developed peculiarities of this model. Moreover, the involvement of concept herein into introduction of informational and communicational technologies in the process of governance eliminates the necessity of power decentralization, it allows to form real net structure and, at the same, to keep vertical power structure, to involve citizens for formation and taking of management decisions, to form electronic communicational channel of feedback, to provide citizens with electronic administrative services. All indicated advantages of the concept certify about the necessity to reform state governance exactly in this field. Meaning/ Distinction. This article raises a question about the significance of formation and sequence of state policy in Ukraine aimed at creating an information-oriented society, space, as well as informational and technological infrastructure

A Fresh Green Index in the World: Building and optimizing a Vegan and Sustainable Index Fund using a Genetic Algorithm and a Heuristic Local Search

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementThe curiosity of investors regarding Environmental, Social and Governance (ESG) factors has seen a growth in the last few years (Alcoforado, 2016), as the world faces some of its biggest problems to date, such as Climate Change and Ecological Collapse. As these issues are not to be taken lightly, individuals have started to act, in the hopes of creating a ‘greener’ world. As individuals hope to align with principles such as Sustainability and Veganism, the proposed project hopes to build a Vegan and Sustainable Index Fund, as “An investment is not an investment if it is destroying our planet.” (Shiva, 2017). The aim of the proposed work is, consequently, to build and optimize an Industry and Geographical diversified Index Fund, using a Genetic Algorithm (GA), demonstrating this through the incorporation of Vegan and Sustainable companies, in addition to the global top-50 ESG ranked firms. Index Funds, which are mutual or Exchange-Traded Funds (ETF), are known to be passively managed portfolios, which have been broadly used in hedge trading (Orito, Inoguchi, & Yamamoto, 2008). This study uses historical data from Vegan, Sustainable and ESG-ranked companies as sample data, replacing traditional optimization methods using a Genetic Algorithm. The GA method was applied to a sample of 61 assets, regarding vegan and sustainable companies, further obtaining a well-diversified and non-centred asset allocation. The obtained results confirm the possible efficiency of genetic algorithms, given their high-speed convergence towards a better solution. A few functions were presented in the algorithm, for example the penalty function method, to perform portfolio optimization which expects to maximize profits and minimize risks. Some flaws have been identified in regard to the method applied

Sustainable Investing: Navigating the Inefficiencies of an Inefficient Market

Over the past decade, sustainable investing, also known as socially responsible investing, ethical investing, or responsible investing, has experienced heightened popularity worldwide. This popularity reflects the increasing awareness of investors of social, environmental, ethical, and corporate governance issues. However, while retail investors\u27 interest has increased, their actual participation has been nominal. This paper explores the question: How do individual investors incorporate sustainability-related experiences, information, learning, or a combination of these in deciding to invest in sustainable investments? This study aims to identify the barriers and enablers that may inhibit or facilitate participation in sustainable investments. The study follows a grounded theory approach to construct theory from data, a method appropriate for this situation given the paucity of research involving investors\u27 intentions but lack of execution in sustainable investing. Furthermore, the study uses Behavioral Decision Theory and Nudge Theory as conceptual frameworks to structure the collection and analysis of data. The study entailed an extensive review of extant literature and promoted data collection through an intensive interview process involving knowledgeable investing and sustainability professionals. The findings identified several uncertainty drivers involving investors’ attitudes towards rating and reporting agencies, the financial merits of sustainable investing, and concerns about greenwashing. Each of these contributes to inefficiencies surrounding sustainable investing. These inefficiencies include asymmetric information, market power, market friction, and externalities. These uncertainty drivers and market inefficiencies promote investor responses through options unavailable to traditional investors. Contributions to theory include confirmation and extension of extant literature, enhanced function of behavioral decision theory and nudge theory, and extended application of market inefficiencies. Contribution to practice involves a conceptual model around strategic option theory for sustainable investing and the application of BDT and nudge. From these, individual investors, investment advisors, and investment companies can make more insightful decisions in their investment strategy to increase participation in sustainable investments

Net structure of subject-to-subject relations in the management of the system of administrative services provision

The purpose of the work is to form the net structure of management of the system of administrative services provision on the basis of implementation of subject-to-subject interactions between state sector and civil society. Methodology. The methodology basis for the investigation is the abstract-logical analysis of theoretical and methodological backgrounds for management of relations and interactions. For the theoretical generalization and formation of net structure, there are used scientific recommendations of Ukrainian scientists regarding the necessity to implement subject-to-subject relations in the system of administrative services provision. Results. The investigations allowed confirming that the hierarchical structure of the state governance system does not give an opportunity to implement equal interaction between a subject of provision and a subject of an appeal as these relations have one – way communication and the feedback channel has a formal character. Moreover, the civil society is not considered by state sector to be a source of methods and ways to develop the system of state governance, in particular, the management system of administrative services provision. Practical meaning. The net structure of management will allow implementing the subject-subject relations in the system, under which the actions of the subject of provision – that means state sector – will be directed to the realization of rights and interests of the subjects of appeal. In their turn, apart from the performance of all legislative responsibilities that they should perform, they can carry out activities directed to the development of management activity in the system of administrative services provision and the whole system of state governance as an integral system of management. Meaning/Distinction. The provided model of the net structure will allow involving citizens in the processes of state governance and increasing the impact of the civil sector during the making of state and management decisions and, as a result, to confirm subject-to-subject positions in the relations

Banking on Shared Value: How Banks Profit by Rethinking Their Purpose

This paper articulates a new role for banks in society using the lens of shared value. It is intended to help bank leaders, their partners, and industry regulators seize opportunities to create financial value while addressing unmet social and environmental needs at scale. The concepts included here apply across different types of banking, across different bank sizes, and across developed and emerging economies alike, although their implementation will naturally differ based on context

Machine Learning methods in climate finance: a systematic review

Evitar la materialización del cambio climático es uno de los principales retos de nuestro tiempo. En esta tarea, el sector financiero desempeña un papel fundamental, motivando a economistas académicos a desarrollar un nuevo campo de investigación, las finanzas climáticas. A la vez, el uso de tecnologías de aprendizaje automático (ML, por sus siglas en inglés) se ha popularizado para analizar problemas relacionados con las finanzas climáticas, debido principalmente a la necesidad de gestionar un volumen elevado de datos relacionados con el clima, y para modelizar relaciones no lineales entre variables climáticas y económicas. De esta manera, proponemos una revisión de la literatura académica para explorar cómo esta tecnología está posibilitando el crecimiento de las finanzas climáticas. Para ello, primero realizamos una búsqueda sistemática de estudios en esta materia en tres bases de datos científicas. Luego, usando un modelo de identificación automática de temas (Latent Dirichlet Allocation), identificamos estadísticamente siete áreas del conocimiento donde el ML está desempeñando un papel relevante: catástrofes naturales, biodiversidad, riesgo agrícola, mercados de carbono, energía, inversión responsable y datos climáticos. Para finalizar, hacemos un análisis de las principales tendencias de publicación, así como una clasificación de los modelos estadísticos utilizados en función del área de estudio. La principal contribución de este artículo es la provisión de una estructura de temas o problemas solventados gracias al uso del ML en finanzas climáticas, lo cual esperamos que facilite a expertos en esta tecnología la comprensión de las principales fortalezas y limitaciones de dicha tecnología aplicada en este campo de investigación.Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area