Portfolio Optimization Based on Funds Standardization and Genetic Algorithm

Yao–Hsin Chou · Shu–Yu Kuo · Yi-Tzu Lo

61 citations0/0 files storedIEEE Access

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Risk and Portfolio Optimization Financial Analysis and Corporate Governance Stock Market Forecasting Methods

Abstract

When investing in the stock market, the first problem and one of paramount importance which investors have to face is making the proper stock selection. Selecting the stocks that simultaneously offer high return and low risk is a difficult problem that is worth investigating. However, the traditional risk calculation based on the modern portfolio theory (MPT) of portfolios has some defects. The MPT method requires the calculations of every relationship between each pair of stocks in the portfolio, entailing high computation complexity, which grows exponentially with the increased number of stocks. Besides, the traditional calculation is unable to calculate the coefficient of variation, and merely considers the relationship between each pair of stocks, so it cannot accurately assess portfolio risk. Therefore, this paper proposes a novel method, funds standardization, and utilizes it to represent the portfolio return and calculate portfolio risk. The fluctuation of portfolio funds standardization shows not only the relationships between each pair of stocks, but also the interactions among all stocks. Hence, utilizing funds standardization can accurately assess portfolio risk and completely represent the mood swings of investors. Compared with the traditional method, the proposed method significantly reduces the computation complexity because the complexity does not increase when the portfolios stock number increases. We combine the genetic algorithm, Sharpe ratio and funds standardization to find the optimal portfolio. In addition, we utilize the sliding window to avoid the over-fitting problem, which is common in this field, and test the effect of all kinds of training and testing periods. The experimental results show that the portfolio can spread the risk effectively, and that the portfolio risk can be assessed accurately by utilizing the funds standardization. Comparing with the traditional method, our method can identify the optimal portfolio efficiently and establish a portfolio that has lower risk and stable return.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 61 citations exist upstream.

References

1952PORTFOLIO SELECTION*1990Stock market prediction system with modular neural networks 2006Minimum-Variance Portfolios in the U.S. Equity Market 2010Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting 2007The Volatility Effect 2006Algorithms for portfolio management based on the Newton method 2003Generating trading rules on the stock markets with genetic programming 2010Portfolio Theory: As I Still See It

Cited by

2018A Novel Portfolio Optimization Model Based on Trend Ratio and Evolutionary Computation 2019An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm 2020A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations 2023Real-Time Portfolio Management System Utilizing Machine Learning Techniques 2021Portfolio Optimization in Both Long and Short Selling Trading Using Trend Ratios and Quantum-Inspired Evolutionary Algorithms

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.