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Advances in Investment Portfolio Management: November 12, 2020

Portfolio management refers to the selection of securities and their continuous shifting in the portfolio for optimizing the return and maximizing the wealth of an investor by analyzing the strengths, weaknesses, opportunities, and challenges for performing a wide range of activities related to a portfolio (Joseph & Varghese, 2017). Investing in the stock market effectively is possible only when based on scientifically sound methods for analyzing investment instruments (Wielki et al., 2019). For maximum possible gain, the portfolio needs to be optimally managed which involves selecting the best possible investment opportunities and avoiding the highly volatile and high-risk stocks (Nazarova & Levichev, 2017; Pandey et al., 2019).

Joseph and Varghese (2017) lighted up the influential factors which could affect the decisions taken in the context of a portfolio and tried to emphasize the dire need for having an investment portfolio. Sunchalin et al. (2019)studied the methodology of construction and management of the investment portfolio and discussed the models for constructing an investment portfolio as well as its practical realization. Sunchalin et al. also reviewed the methods of artificial intelligence, including neural networks and genetic algorithms, based on which the researchers built the model of forecasting the return on assets. Wielki et al. (2019) developed a method for selecting financial instruments for the formation of an investment portfolio of securities. Based on the analysis of the competitiveness of joint-stock companies by identifying the market’s capacity, the share price, and the company’s share in of the stock market turnover, Wielki et al. argued that the proposed method had the potential to become a scientific basis for effective long-term investment on the stock market.

Fussy-Based Models 

In recent times, the use of soft computing-based methods of fuzzy and neuro-fuzzy models has become increasingly popular for optimal selection and rejection of the portfolio elements to maximize the investor’s profit (Pandey et al., 2019). Since a lot of investment and trading is still carried out based on an individual or a group of decision-makers’ verbal instructions which are opaque in nature, the role of fuzzy in transforming these vague sentences into the language and the machines can understand is phenomenal where the traditional mean-variance models are lagging (Pandey et al., 2019). 

The self-learning feature of computing-based models is better than the statistical modeling-based optimization models (Pandey et al., 2019). Besides, the generation of a large amount of vague data in the digital world today has given the fuzzy-based methods a decisive edge over the statistical models, in which the problems related to volatility are dealt with probabilistic fuzzy c-means clustering and functional fuzzy rule-based models (Pandey et al., 2019). Overall, applying fuzzy-based systems for portfolio optimization and managing is a novel approach with improved performance.

Nazarova and Levichev (2017) investigated the effective management of the investment portfolio, including various types of assets. Through an integrated approach, combining the selection of assets with the help of fuzzy clustering, the Markowitz classical model, and rebalancing, Nazarova and Levichev (2017) managed to reduce the portfolio management problem to the problem of maximizing the Sharpe ratio at a given level of risk, i.e., a comprehensive model for evaluating the effectiveness of investment portfolio management with functions of profit maximization, constraints, levels of risk, and the constancy of the weighting factors. Besides, Nazarova and Levichev (2017) resulted in a mathematical model, which provided a significant increase in the effectiveness of portfolio management compared to conventional approaches. Further, Nazarova and Levichev (2017) proposed a modified algorithm for rebalancing over time, which allowed to combine all the advantages of active management with a reduction in transaction costs., and carried out the choice of control method incorporating the investment horizon. The most promising part is the development of an algorithm-based special software that can be used by both private investors and managers of investment funds.

Behavioral Finance and Portfolio Management 

Crowd psychology and cognitive biases are the outcomes of irrational behaviors (Antony, 2020). Identifying the irrationality in the behavioral patterns can reduce the market anomalies that we are facing in the stock market operations (Antony, 2020). Application of behavioral finance will help in policymaking process by designing optimal portfolio and strategies to minimize the risk by controlling the emotions of the investors (Antony, 2020). Researchers have developed the behavioral portfolio model as an extension of the capital asset pricing model (CAPM), which is a prescriptive model by incorporating behavioral biases (Antony, 2020). The behavioral portfolio model explains why the investors invest with multiple objectives such as the future requirements of family, retirement savings, and funding for meeting emergencies (Antony, 2020). 

Focusing on the factors affecting individual investors’ behavior and their portfolio, Seetharaman et al. (2017) modeled the survey with partial least squares (PLS) structural equation modeling, and supported the conventional views on the influence of the independent variables of investment objective, risk profile, and asset familiarity on the perceived extent of investor behavior. Seetharaman et al. also examined how this perceived extent of investor behavior might predict the individual choice of portfolio and its performance. As the extent of investor behavior was also an intervening variable in the study, Seetharaman et al. made an attempt to assess its mediating effect on the investment objective, risk profile, and asset familiarity in the overall model. Besides, Seetharaman et al. examined the goodness of the measures and assessed them by looking at the validity and reliability of the measures using the PLS approach and showed that the measures used exhibited both convergent and discriminant validity. Next, Seetharaman et al. proceeded to assess the reliability of the measures by looking at the Cronbach’s alpha values and composite reliability values and found that both the Cronbach’s alpha values and composite reliability values were on a par with the criteria set up by other established researchers. Seetharaman et al. also found that investment objective and asset familiarity exerted an impact on investor behavior, with asset familiarity having the strongest impact. Investor behavior, in turn, influences the choice of a portfolio of the investors. In another word, asset familiarity introduced the bias and created the confidence that the returns were guaranteed, which might prevent individuals from diversifying their portfolio, and hence there was a need to create awareness (Seetharaman et al., 2017). Seetharaman et al. provided useful insights and information regarding the factors that investment planners, financial advisers, and individuals needed to improve their choice of the portfolio and its performance, while failed to test people’s investment decisions and hence their portfolio. 

Currency Hedge

Investment management on the international financial market necessitates a special approach to foreign currency hedging (Borochkin, 2017). The majority of international investors fully eliminate the risk associated with their foreign-exchange holdings, seeking profits only from stock price differentials (Borochkin, 2017). In certain circumstances, a correlation between the local currency exchange rate and local stock index may provide additional opportunities for profit generation (Borochkin, 2017).

Borochkin (2017) conducted a study to test the hypothesis that partial currency risk-taking might reduce the total portfolio risk and increase return on international investment. Borochkin (2017) applied the global optimization approach to calculate investment portfolios for 11 countries of the world, whereby each portfolio included shares of 20–25 highly capitalized companies and assessed investment strategy efficiency based on the Sharpe Ratio, Sortino Ratio, Treynor Ratio, and Omega Ratio. Borochkin (2017) found that currency hedge position at the rate of about 14% of the total portfolio value might increase investment yield by 2% annually on the 10-year time span. Borochkin (2017) suggested that a total currency risk hedge was necessary for investment in developed and developing countries that pursued the policy of regular devaluation of their national currency. If the return on the stock market is lower than that of risk-free instruments, market regulators inside a particular country should consider that a sudden devaluation of national currency may be needed (Borochkin, 2017).

Keywords: investment portfolio, international portfolio investment, global optimization, foreign currency hedging, digital technologies, neural networks, genetic algorithm, financial investment, investment decisions, investor factors


Antony, A. (2020). Behavioral finance and portfolio management: Review of theory and literature. Journal of Public Affairs, 20(2), e1996.

Borochkin, A. A. (2017). Investment portfolio forex risk hedging in the international stock market. Economic Analysis: Theory and Practice, 6(465), 1022-1042.

Joseph, A., & Varghese, J. (2017). A study on factors affecting investment decision making in the context of portfolio management. PESQUISA, 3(1), 52-56.

Nazarova, V., & Levichev, I. (2017). Development of the model of improving the effectiveness of investment portfolio. HSE Economic Journal, National Research University Higher School of Economics, 21(3), 451-481.

Pandey, M., Singh V., Verma, N. K. (2019). Fuzzy based investment portfolio management. Applying Fuzzy Logic for the Digital Economy and Society, 73-95.

Seetharaman, A., Niranjan, I., Patwa, N., & Kejriwal, A. (2017). A study of the factors affecting the choice of investment portfolio by individual investors in Singapore. Accounting and Finance Research, 6(3), 153. doi:10.5430/afr.v6n3P153 

Sunchalin, A. M., Ivanyuk, V.A., Sunchalina, A. L. (2019). Investment portfolio management and forecasting the return on assets based on artificial intelligence methods (neural analysis and genetic algorithm. 1st International Scientific Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2019).

Trindade, B. C., Reed, P. M., & Characklis, G. W. (2019). Deeply uncertain pathways: Integrated multi-city regional water supply infrastructure investment and portfolio management. Advances in Water Resources, 134, 103442.

Wielki, J., Stopochkin, A., & Sytnik, I. (2019). Investment portfolio management based on the study of the competitiveness of joint-stock companies. Quality-Access to Success, 20(S1), 387-392.


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