Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 4, 2024

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization allocation. With advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, black-box nature these renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning align with users' expected outcomes. This study presents structured prompt framework specifically designed for stock selection, aiming provide direct interpretable stock-selecting investors various levels. By creating representative scenarios combining them into different cases experimentation, we can explore how construction prompts influences responses generated by AI tools. Additionally, this paper proposes novel algorithm that combines Nonlinear-Activated Beetle Antennae Search strategy Egret Swarm Optimization Algorithm (NBESOA) address Mean-Variance Portfolio Selection problem Transaction Costs Cardinality Constraints (MVPS-TCCC), utilizing real market data construct based on recommendations. Simulation results indicate that, compared other algorithms, NBESOA prefers optimizing portfolio configurations achieve highest Sharpe Ratio strictest constraints, bringing outcomes closer portfolio's frontier.

Language: Английский

A Predictive Model for Software Cost Estimation Using ARIMA Algorithm

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(7)

Published: Jan. 1, 2024

Technology is a differentiator in business today. It plays different and decisive role by providing programs that contribute to this. To build this software while avoiding risks during the implementation construction process, it necessary estimate cost. The cost estimation process of estimating effort, time, resources needed project. crucial as provides good planning reduces you may be exposed to. Therefore, previous studies sought models methods this, but they were not accurate enough complete process. study seeks model using Autoregressive integrated moving average (ARIMA) algorithm. Five datasets COCOMO81, COCOMONasaV1, COCOMONasaV2, Desharnais, China used. dataset was processed remove noise missing values, visualized understand it, linked time series predict future values data. will then trained on ARIMA ensure effectiveness efficiency for use, four famous evaluation criteria used: mean magnitude relative error (MMRE), root square (RMSE), (MdMRE), prediction accuracy (PRED). This experiment showed impressive results, with MMRE, RMSE, MdMRE, PRED results being 0.07613, 0.04999, 0.03813, 95% COCOMO81 dataset, respectively. high COCOMONasaV1 reaching 0.02227, 0.02899, 0.01113, 97.1%. COCOMONasaV2 0.01035, 0.00650, 0.00517, 99.35%, 0.00001, 0.00430, 0.00008, 99.57%, promising Desharnais showing 0.00004, 0.0039, 0.00002, 99.6%. are distinctive compared recent studies, also risk reduction.

Language: Английский