Portfolio selection strategies and cognitive psychology biases: a behavioral evidence from the Nigerian equity market

This paper focuses on classical portfolio strategies applied to five countries, which are Brazil, Russia, India, China and South Africa. These five countries form the so-called BRICS group. In particular, the authors investigate their corporate and sovereign bond market and evaluate whether these markets can represent a profitable investment for non-satiable and risk-averse investors. Two-step optimization is proposed to control price risk and market risk. For price risk management, classical immunization strategies and are obtained funds of bond are obtained that share the same risk measure. For market risk control, the previously found funds are used and a performance measure optimization commonly used in stock markets is applied to define the best portfolio of funds. Therefore, the resulting optimal portfolio controls the price risk and jointly maximizes a desired performance measure that includes the market risk. Finally, the authors propose an empirical analysis to evaluate the profitability of the suggested two-step optimization for the five BRICS countries and compare the ex-post sample paths of the obtained portfolios for testing the stochastic dominance relations.

The article analyzes some indicators of Ukraine’s financial security in the context of transnationalization and national interests. On the basis of the obtained results, the role of financial security in the context of transnationalization and national interests has been determined, directions for strengthening the financial security of the country have been proposed.
The main objective of the study is to determine the role of Ukraine’s financial security in the context of transnationalization and national interests, based on the study and evaluation of some financial security indicators. The methodology of the research is based on the early prevention of threats to financial security of Ukraine in the context of transnationalization and national interests, based on both macroeconomic and financial data from the entire financial system. The regression models were used, because they work dynamically and change as information changes. Diagrams can be used by financial analysts to understand the investment attractiveness of the country at the current time with the current level of financial security. Standard models are designed to predict the financial stress and threats to financial security. Financial security models must be built with the support of macroeconomic knowledge, assessment of other risks and expert interpretations, used to get the highest value in the research.
The procedure of simulation of financial security indicators of the country has been given. The use of correlation and regression analysis for forecasting the financial security indicators in Ukraine has been substantiated. The links between the indicator variables have been analyzed. The general view of model relations between financial security indicators in Ukraine has been determined, their statistical analysis has been carried out and the necessity of forecasting the financial security indicators of Ukraine has been substantiated.

This paper uses a Multiple Attribute Decision Making (MADM) model to improve the out-of-sample performance of a naïve asset allocation model. Under certain conditions, the naïve model has out-performed other portfolio optimization models, but it also has been shown to increase the tail risk. The MADM model uses a set of attributes to rank the assets and is flexible with the attributes that can be used in the ranking process. The MADM model assigns weights to each attribute and uses these weights to rank assets in terms of their desirability for inclusion in a portfolio. Using the MADM model, assets are ranked based on the attributes, and unlike the naïve model, only the top 50 percent of assets are included in the portfolio at any point in time. This model is tested using both developed and emerging market stock indices. In the case of developed markets, the MADM model had 24.04 percent higher return and 53.66 percent less kurtosis than the naïve model. In the case of emerging markets, the MADM model return is 90.16 percent higher than the naïve model, but with almost similar kurtosis.