Using genetic algorithm to support clustering-based portfolio optimization by investor information

Cheong, Donghyun; Kim, Young Min; Byun, Hyun Woo; Oh, Kyong Joo; Kim, Tae Yoon

doi:10.1016/j.asoc.2017.08.042

Cited by 55 publications

(25 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sliding window method first divides each window into training and test periods. Then, it repeats the training and test by sliding the training and the test periods as shown in Figure 2 [23,45,46].…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

et al. 2021

Self Cite

View full text Add to dashboard Cite

Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

show abstract

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A genetic algorithm is an optimization methodology that is based on the principles of Darwinian evolution [31][32][33][34]. The algorithm is mainly used to find solutions to nonlinear optimization problems using evolutionary rules such as crossover, selection, and mutation.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

et al. 2020

Self Cite

View full text Add to dashboard Cite

There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio.

show abstract

“…The literature somewhat related to our research is portfolio selection and fleet size models, such as product portfolio management, facility location selection, supplier selection, and investment portfolio selection (e.g. Arabani and Farahani, 2012; Boera et al, 2001; Breaugh, 2009; Chand and Katou, 2012; Cheong et al, 2017; Eichner, 2011; Jiao et al, 2007; Wu and Barnes, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meta-heuristics such as genetic algorithms have been successfully utilized to deal with practical combinatorial optimization problems due to their significant computational efficiency (e.g. Cheong et al, 2017; Eihachloufi et al, 2012; Lee, 2018; Luan et al, 2019; Niknamfar and Niaki, 2018). To speed up computation, this article also proposes a hybrid heuristic based on a genetic algorithm to solve the proposed mathematical model.…”

Section: Literature Reviewmentioning

confidence: 99%

Travel agent’s tour selection and sightseeing bus schedule for group package tour planning

You

Chen

2019

Tourism Economics

View full text Add to dashboard Cite

Two problems plague the tourism industry: (1) a scheduled group package tour (GPT) can be canceled if the number of reserved passengers does not reach a minimum number and (2) a customer’s request for a scheduled GPT will be denied once the number of reserved passengers has reached its maximum. Neither instance is desirable, because of both results in lost sales. To generate higher profit over the course of a sightseeing season, travel agent managers must schedule GPTs on suitable dates and determine the number of sightseeing buses needed. Viewing this as a portfolio selection problem, this research develops an integer programming model to arrive at an equitable solution. We offer several numerical examples to illustrate the model’s performance and the problem’s characteristics. The results show that the proposed model significantly increases revenues.

show abstract

Using genetic algorithm to support clustering-based portfolio optimization by investor information

Cited by 55 publications

References 22 publications

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

Travel agent’s tour selection and sightseeing bus schedule for group package tour planning

Contact Info

Product

Resources

About