Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data.

A simple, fast, and ecofriendly spectrofluorometric method was developed and validated for determination of mono sodium glutamate (MSG). The method depended on the reaction between MSG and iron (III) salicylate based on ligand exchange mechanism. Addition of MSG turned-on the fluorescent response of iron (III) salicylate at λ_em 411 nm. Reaction conditions including reagent concentration, pH, and time were optimized. The method was validated regarding the ICH guidelines. The method determined MSG within the linearity range of 25—250 µM with a coefficient of determination of 0.9967 and a calculated limit of detection of 1.73 µM. Furthermore, the developed method was successfully applied for the determination of MSG in food preparation (instant noodles). The results were compared to those obtained by a published HPLC method using t-test and F-test at 95% confidence interval; no statistically significant difference was found. Based on the analytical Eco-scale and the green analytical procedure index (GAPI), the developed method was assessed to be greener than the published HPLC method. The developed method offered advantages over other spectrophotometric reported methods and was convenient for routine determination of MSG in foodstuffs.