Review of “Antitrading” by O. Plastun. Fabula Publishing, 2025

The biggest challenge facing countries, including India, is creating and managing an LCR (low carbon resilient) economy, which balances the need for high growth rates and is environmentally sustainable. The green bond market provides investors the means to help change the economy into an LCR economy. The study was undertaken to understand the key drivers and the factors influencing the individual retail investor’s decision to invest in green bonds. A survey instrument was designed and administered through the snowball sampling technique to 125 Indian respondents of various age groups who were eligible to invest in the Indian bond market. SPSS software was used to conduct a descriptive analysis followed by regression and conjoint analyses. The study results suggest that the Environmental, Social, and Governance (ESG) rating and credit rating of the green bond issuers are the key factors that influence an individual’s investment decision. The findings also highlight that incentives such as tax exemptions and awareness of green bonds also affect an investor’s decision. This research stands out as one of the first attempts to understand the Indian retail investors’ perception of a green bond.

The impact of news on individual investor decision is explicit as investors need to update, adapt and forecast returns with constraints of time, uncertainty and resources to be successful. The aim is to understand and review the influence of news on individual investor’s decision making in stock markets and identify the impact of different type of news on individual investor’s decision making in stock markets, assess the behavioral reaction and investment decisions made by investors before and after there is news item, identify the linking effect on behavioral theories and biases, develop a generalized decision making conceptual model to understand the impact of news on investor’s reaction, decision and its linkages along with the behavioral bias. Theoretical basis/methodology for processing of news by investors is assumed to be based on Broadbent’s filter theory (1958) and due to cognitive informational inefficiency of investors it assesses the attention and the investor’s reaction of overreaction and underreaction, which do not comply with efficient market hypothesis theory. The reasons for its noncompliance are found by relating it with behavioral theories. The results explain how investor screens with filters and give attention to news only when it affects their portfolio or investment objective and strategies. It is concluded that investor’s decision making depends on degree of information penetration, information content, information influence, specific internal factors and generic external and on investors prevailing at that given circumstances. This gives us the solution to comprehend the investor’s reaction, decision and unresolved reversals, short- and long-term overreaction.

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.