Portfolio optimization using fundamental indicators based on multi-objective EA

Silva, Antônio Augusto Moura da; Neves, Rui; Horta, Nuno

doi:10.1109/cifer.2014.6924068

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the company's informativeness can be used to determine a firm's stock price (Bennett et al, 2019). The different criteria's which can be used to study the relative dominance of each stock are Sharpe ratio, ordered modular averages, Book value per share, value at risk (VaR), mean-variance, compound annual growth rate (CAGR), and Price to earn ratio (Hota et al, 2018;Li et al, 2019;Silva et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A novel methodology for perception-based portfolio management

et al. 2022

View full text Add to dashboard Cite

Selecting and investing in stock market with right proportions is one of the major challenges. Majority of the investors end up losing their invested equity capital due to uncertainty in the market. The present study provides a novel framework for novice investors to construct portfolio based on multicriteria decision making techniques under fuzzy environment. The scores obtained from these techniques were used to introduce two non-dimensional parameters for categorization of risky and non-risky assets. Three perceptions portfolios were constructed based on the proposed non-dimensional parameters along with fractional lion clustering algorithm. In order to demonstrate the proposed framework, an illustrative application is included in equity portfolio selection. The returns and risks of these perception based portfolios are compared to major Index funds for validating the efficiency and are found to overpower the Index funds with significant margins by maintaining the risk comparable to Index funds. Further, Markowitz based efficient frontier is plotted for better understanding of optimal returns and risk for perception based investment.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel methodology for perception-based portfolio management

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In real world, financial news, quotes, company earnings, pre-market and after-hours data, market research, company analysis are commonly used for price movement analysis. For example, researchers suggest to use trading systems which use fundamental indicators to select equity and technical indicators to produce trading signals [14].…”

Section: Introductionmentioning

confidence: 99%

Automating Transition Functions: A Way To Improve Trading Profits with Recurrent Reinforcement Learning

Zhang

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Part 2: Learning-Ensemble LearningInternational audienceThis paper presents an application of the logistic smooth transition function and recurrent reinforcement learning for designing financial trading systems. We propose a trading system which is an upgraded version of the regime-switching recurrent reinforcement learning (RS-RRL) trading system referred to in the literature. In our proposed system (RS-RRL 2.0), we use an automated transition function to model the regime switches in equity returns. Unlike the original RS-RRL trading system, the dynamic of the transition function in our trading system is driven by utility maximization, which is in line with the trading purpose. Volume, relative strength index, price-to-earnings ratio, moving average prices from technical analysis, and the conditional volatility from a GARCH model are considered as possible options for the transition variable in RS-RRL type trading systems. The significance of Sharpe ratios, the choice of transition variables, and the stability of the trading system are examined by using the daily data of 20 Swiss SPI stocks for the period April 2009 to September 2013. The results from our experiment show that our proposed trading system outperforms the original RS-RRL and RRL trading systems suggested in the literature in terms of better Sharpe ratios recorded in three consecutive out-of-sample periods

show abstract

An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems

Balande

Shrimankar

2017

Oper Res Int J

View full text Add to dashboard Cite

Portfolio optimization using fundamental indicators based on multi-objective EA

Cited by 5 publications

References 29 publications

A novel methodology for perception-based portfolio management

A novel methodology for perception-based portfolio management

Automating Transition Functions: A Way To Improve Trading Profits with Recurrent Reinforcement Learning

An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems

Contact Info

Product

Resources

About