This project presents a sophisticated approach to investment decision-making under uncertainty, utilizing First- and Second-degree Stochastic Dominance (FSD and SSD) criteria. The primary objective is to optimize the selection of investments in high-risk scenarios.
The innovative aspect of this project lies in the development of a method that combines or convolves two distinct investment functions. This approach aims to balance the trade-off between risk and return, striving to maximize returns while minimizing risk. This is particularly applicable in cases where one investment has a continuous function and the other has a discrete function.
The image provided illustrates the application of this approach. It presents a graph with three plotted lines labelled “Cumulative f”, “Cumulative g”, and “Cumulative Convolution”. The “Cumulative f” line represents a discrete data set or events, while the “Cumulative g” line represents a continuous data set or function. The red “Cumulative Convolution” line appears as a combination of both the blue and green lines’ patterns, indicating the integration of both discrete and continuous data sets or functions.
Overall, this project contributes to the field of investment decision-making by providing a robust method for handling risky investments, potentially leading to more resilient and profitable investment strategies. This approach is especially beneficial in situations where investments have both continuous and discrete functions. The convolution of these functions allows for a more comprehensive understanding of potential investment outcomes, thereby aiding in the decision-making process.