We assess the impact of media sentiment on international equity prices using more than 4.5 million Reuters articles published across the globe between 1991 and 2015. News sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news-sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. We also find evidence that news sentiment affects mainly foreign – rather than local – investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment.

We assess the impact of media sentiment on international equity prices using more than 4.5 million Reuters articles published across the globe between 1991 and 2015. News sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news-sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. We also find evidence that news sentiment affects mainly foreign – rather than local – investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

This book presents a series of contributions on key issues in the decision-making behind the management of financial assets. It provides insight into topics such as quantitative and traditional portfolio construction, performance clustering and incentives in the UK pension fund industry, pension fund governance, indexation, and tracking errors. Markets covered include major European markets, equities, and emerging markets of South-East and Central Asia.

This paper reviews the literature on financial crises focusing on three specific aspects. First, what are the main factors explaining financial crises? Since many theories on the sources of financial crises highlight the importance of sharp fluctuations in asset and credit markets, the paper briefly reviews theoretical and empirical studies on developments in these markets around financial crises. Second, what are the major types of financial crises? The paper focuses on the main theoretical and empirical explanations of four types of financial crises—currency crises, sudden stops, debt crises, and banking crises—and presents a survey of the literature that attempts to identify these episodes. Third, what are the real and financial sector implications of crises? The paper briefly reviews the short- and medium-run implications of crises for the real economy and financial sector. It concludes with a summary of the main lessons from the literature and future research directions.

An interesting disconnect has taken shape between local currency- and hard currency-denominated bonds in emerging markets with respect to their portfolio flows and prices since the start of the recovery from the COVID-19 pandemic. Emerging market assets have recovered sharply from the COVID-19 sell-off in 2020, but the post-pandemic recovery in 2021 has been highly uneven. This note seeks to answer why. Yields of local currency-denominated bonds have risen faster and are approaching their pandemic highs, while hard currency bond yields are still near their post-pandemic lows. Portfolio flows to local currency debt have similarly lagged flows to hard currency bonds. This disconnect is closely linked to the external environment and fiscal and inflationary pressures. Its evolution remains a key consideration for policymakers and investors, since local markets are the main source of funding for emerging markets. This note draws from the methodology developed in earlier Global Financial Stability Reports on fundamentals-based asset valuation models for funding costs and forecasting models for capital flows (using the at-risk framework). The results are consistent across models, indicating that local currency assets are significantly more sensitive to domestic fundamentals while hard currency assets are dependent on the external risk sentiment to a greater extent. This suggests that the post-pandemic, stressed domestic fundamentals have weighed on local currency bonds, partially offsetting the boost from supportive global risk sentiment. The analysis also highlights the risks emerging markets face from an asynchronous recovery and weak domestic fundamentals.

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

The October 2019 Global Financial Stability Report (GFSR) identifies the current key vulnerabilities in the global financial system as the rise in corporate debt burdens, increasing holdings of riskier and more illiquid assets by institutional investors, and growing reliance on external borrowing by emerging and frontier market economies. The report proposes that policymakers mitigate these risks through stricter supervisory and macroprudential oversight of firms, strengthened oversight and disclosure for institutional investors, and the implementation of prudent sovereign debt management practices and frameworks for emerging and frontier market economies.

An investor's guide to understanding the most elusive (yet most important) aspect of successful investing - yourself. Why is it that the investing performance of so many smart people reliably and predictably falls short? The answer is not that they know too little about the markets. In fact, they know too little about themselves. Combining the latest findings from the academic fields of behavioral finance and experimental psychology with the down-and-dirty real-world wisdom of successful investors, Drs. Richard Peterson and Frank Murtha guide both new and experienced investors through the psychological learning process necessary to achieve their financial goals. In an easy and entertaining style that masks the book’s scientific rigor, the authors make complex scientific insights readily understandable and actionable, shattering a number of investing myths along the way. You will gain understanding of your true investing motivations, learn to avoid the unseen forces that subvert your performance, and build your investor identity - the foundation for long-lasting investing success. Replete with humorous games, insightful self-assessments, entertaining exercises, and concrete planning tools, this book goes beyond mere education. MarketPsych: How to Manage Fear and Build Your Investor Identity functions as a psychological outfitter for your unique investing journey, providing the tools, training and equipment to help you navigate the right paths, stay on them, and see your journey through to success.