Integrated Intellectual Investment Portfolio as an Efficient Instrument to Manage Personal Financial Investment

The redistribution of resources in global stock markets is prevalent: the capital is transferred from one investor to another. Sometimes, earning a substantial return in the stock market seems complicated to implement for an individual investor. Investing contributes to the welfare of society and the wealth of citizens. This is why people should look for efficient ways to invest. Investment should become a natural part of personal finance management in the majority of households. For this reason, an investment model is developed where stocks are selected based only on market intelligence using historical data. The model helps find one or several stocks that generate the highest return on a separate step. Applying this model, experiments were performed with daily data from German, US, and UK stock markets. The possibility of obtaining higher than average returns in these markets has been noticed. In the German market, during the 97-day period, the authors obtained a 1.46 return, which implies a 2.31 annual return: in the USA market, a 2.37 return (7.93 annual return), and in the UK market, a 1.90 return (4.09 annual return). Thus, the proposed investment decision-making system could be an efficient tool for forming a sustainable individual or household portfolio. It can generate higher investment returns for an investor and, moreover, make the market more efficient by applying market intelligence and related historical data.

Influence of world stock markets on the development of the stock market in Ukraine

Viewing the development of the stock market in Ukraine, the economy, which world financial organizations characterize as small and open, is largely determined by the trends formed by the global stock markets and leading stock exchanges. Therefore, the study aims to analyze Ukraine’s stock market, the world stock market, stock markets in the regions, and to assess their mutual influence. The study uses the data of the World Federation of Exchanges and National Securities and Stock Market Commission (Ukraine) from 2015 to 2020. Stock market performance forecasts are built using triple exponential smoothing. Based on pairwise correlation coefficients, the existence of a significant dependence in the development of the world stock market on the development of the American stock market was determined. Regarding the Ukrainian stock exchanges, only SE “PFTS” demonstrated its dependence on the US stock market. The results of the regression model based on an exponentially smoothed series of trading volumes in all markets showed that variations in the volume of trading on the world stock market are due to the situation on the US stock markets. Trading volume dynamics on Ukrainian stock exchanges such as SE “PFTS” and SE “Perspektiva” is almost 50% determined by the development of stock markets in the American region. Although Ukraine is geographically located in Europe, the results show a lack of significant links and the impacts of stock markets in this region on the major Ukrainian stock exchanges and the stock market as a whole.

The 1987 Stock Exchange Crash in Historical Perspective

This chapter aims to explore the different facets of the collective memory of the 1987 Crash in the US, which represented an unprecedented collapse of prices on the global stock markets. The 22.3% fall of the Dow Jones on Black Monday (19 October 1987) represents the biggest single-day stock market collapse in history—even greater than that of 24 October 1929. The crash spread to other major financial markets over the world, but was quickly resolved thanks to the central banks’ intervention on the capital markets. In the context of Reaganomics, the crash can be seen as the first financial crisis of the second globalization wave in the strictest sense of the term ‘financial’, without taking into consideration the banking crises of the 1970s and the debt crisis in the early 1980s. However, unlike other financial crises, memories of this market break remained either vague or inexistent in public opinion, or fragmented and partial for economists and historians—until the subprime crisis. Since then, the 1987 warning and the potential dangers of uncontrolled markets were brought to light. The final lesson to be learned from this example of an evolving memory is about using the past.

Multiple-step value-at-risk forecasts based on volatility-filtered MIDAS quantile regression: Evidence from major investment assets

Forecasting multiple-step value-at-risk (VaR) consistently across asset classes is hindered by the limited sample size of low-frequency returns and the potential model misspecification when assuming identical return distributions over different holding periods. This paper hence investigates the predictive power for multi-step VaR of a framework that models separately the volatility component and the error term of the return distribution. The proposed model is illustrated with ten asset returns series including global stock markets, commodity futures, and currency exchange products. The estimation results confirm that the volatility-filter residuals demonstrate distinguished tail dynamics to that of the return series. The estimation results suggest that volatility-filtered residuals may have either negative or positive tail dependence, unlike the unanimous negative tail dependence in the return series. By comparing the proposed model to several alternative approaches, the results from both the formal and informal tests show that the specification under concern performs equivalently well if not better than its top competitors at the 2.5% and 5% risk level in terms of accuracy and validity. The proposed model also generates more consistent VaR forecasts under both the 5-step and 10-step setup than the MIDAS-Q model. AcknowledgmentThe authors are grateful to the editor and an anonymous referee. This research is sponsored by the National Natural Science Foundation of China (Award Number: 71501117). All remaining errors are our own.

DYNAMICS OF DIGITAL MARKETS

The focus of this panel is the material, organizational, and cultural conditions of digital markets. While the notion of economy refers to the more general production, distribution and allocation in society, the idea of markets represents specific contexts of economic exchange typical of capitalist economies (Carruthers & Babb, 2013). A more elaborate understanding of digital markets and their relationships with digital platforms can expand our understanding of the economic implications that specific types of platform architectures have at the level of economic interaction. The discussion takes as a starting point perspectives from economic sociology that emphasize how markets are embedded into broader social and societal structures (Granovetter, 2017) and conditioned upon cultural norms and conventions (Beckert, 2009). In addition, the panel is informed by the way economic sociology and STS have approached the material conditions of markets (Garcia-Parpet, 2007; MacKenzie, 2018) and the way these conditions frame and transform power relations and interaction patterns on specific markets. The panel consists of four papers that approach this issue from a range of perspectives: The relationship between platform architectures, open market strategies and the formation of ‘commodity money’ in the case of Steam, the relationship between platforms, markets, and state regulation in the case of Alibaba, the role of narratives, imagined futures, and collective action that frame patterns of buying and selling in global stock markets in the case of Gamestop shares and, finally, how the online engagement industry is organized in practice in the case of “click farms”.

COVID-19 Pandemic and Investor Herding in International Stock Markets

The aim of this study is to understand the effect of the recent novel coronavirus pandemic on investor herding behavior in global stock markets. Utilizing a daily newspaper-based index of financial uncertainty associated with infectious diseases, we examine the association between pandemic-induced market uncertainty and herding behavior in a set of 49 global stock markets. More specifically, we study the pattern of cross-sectional market behavior and examine whether the pandemic-induced uncertainty drives directional similarity across the global stock markets that cannot be explained by the standard asset pricing models. Utilizing a time-varying variation of the static herding model, we first identify periods during which herding is detected. We then employ probit models to examine the possible association between pandemic-induced uncertainty and the formation of herding. Our findings show a strong association between herd formation in stock markets and COVID-19 induced market uncertainty. The herding effect of COVID-19 induced market uncertainty is particularly strong for emerging stock markets as well as European PIIGS stock markets that include some of the hardest hit economies in Europe by the pandemic. The findings establish a direct link between the recent pandemic and herd formation among market participants in global financial markets. Considering the evidence that herding behavior can drive security prices away from equilibrium values supported by fundamentals and further contribute to price fluctuations in financial markets, our findings have significant implications for policy makers and investors in their efforts to monitor investor sentiment and mitigate mis-valuations that might occur as a result. Furthermore, the evidence on the behavioral pattern of stock investors in relation to infectious diseases uncertainty can be useful in studying price discovery in stock markets and might help market participants in forming hedging strategies to mitigate downside risk in their investment portfolios.