Technical analysis testing in forecasting Socially Responsible Investment Index in Indonesia Stock Exchange

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.

Degradation of marine ecosystems through, e.g., eutrophication and climate change is a concern for sustainable fishery management worldwide, but studies on associated impacts on fish populations are rare. This study examines the effects of eutrophying nutrient loads on the economic value of perch populations along the Swedish east coast by estimating the effects of nutrient loads on the population of perch and, then, simulates the harvest value of future perch population under the changes in nutrient loads. A modified Gordon-Schaefer logistic growth model was used for econometric estimation of perch populations based on annual time series data for the period of 1970-2014. Regression analysis using the fully modified ordinary least square (FMOLS) estimator revealed that phosphorus loads had significant effects on the perch population. A 40% decrease in phosphorus loads, as suggested by the international HELCOM agreement, could increase the steady state perch population by 50%. Simple calculations showed that this could increase the total discounted recreational and commercial harvest value of the perch by 30% over a 30 year period.

Value-at-risk (VaR) is the most common and widely used risk measure that enterprises, particularly major banking corporations and investment bank firms employ in their risk mitigation processes. The purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on BRICS (Brazil, Russia, India, China, South Africa) and US stock market indices. The study employs three different VaR estimation models, namely normal (N), historical (HS), exponential weighted moving average (EMWA) procedures, and eight backtesting models. The empirical analysis is conducted during three different periods: overall period (2006–2021), global financial crisis (GFC) period (2008–2009), and COVID-19 period (2020–2021). The results show that the EMWA model performs better compared to N and HS estimation models for all the six stock market indices during overall and crisis sample periods. The results found that VaR models perform poorly during crisis periods like GFC and COVID-19 compared to the overall sample period. Furthermore, the study result shows that the predictive accuracy of VaR methods is weak during the COVID-19 era when compared to the GFC period.