Algorithmic trading for online portfolio selection under limited market liquidity

Ha, Youngmin; Hai, Zhang

doi:10.1016/j.ejor.2020.03.050

Cited by 18 publications

(5 citation statements)

References 37 publications

(35 reference statements)

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“…Currently, many approaches have been proposed for the TS optimization, and they can be divided into two categories that are the TS optimization without and with SLTPs. For the TS optimization without SLTPs, many approaches have been proposed to solve the TS parameter optimization [14,26,34], incorporating TS in stock trading [1,3,20,25,33,36], and the TSP optimization [4,5,32,24].…”

Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

“…In their approach, the deep Q-learning is employed to train the intelligent agent and identify the optimal trading action. Ha et al proposed an optimal intraday trading algorithm for reducing overall transaction costs when an online portfolio selection method rebalances the portfolio, and the results indicated the algorithm is significant to reduce the transaction costs when the liquidity is limited [20].…”

Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

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RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Chen

Díaz

et al. 2021

Electron Commer Res

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It is always difficult and challenge to obtain suitable trading signals for the desired securities in financial markets. The popular way to deal with it is through the use of trading strategies (TSs) made up of technical or fundamental indicators. Due to the different properties of TSs, an algorithm was proposed to find trading signals by obtaining the group trading strategy portfolio (GTSP), which is composed of strategy groups that can be employed to generate various TS portfolios (TSP) instead of a single TS. The stop-loss and take-profit points (SLTP) are widely utilized by shareholders to avoid massive losses. However, the appropriate SLTP is hard to set by users. Therefore, in this paper, the algorithm, namely GTSP-SLTP algorithm, is proposed to not only obtain a reliable GTSP but also find appropriate SLTP using the grouping genetic algorithm. A chromosome is encoded by the generated SLTP and GTSP along with the weights for strategy groups that are the SLTP, grouping, weight, and strategy parts. To assess the goodness of a chromosome, the evaluation function that consists of the group balance, weight balance, risk factor, and profit factor, is employed. Genetic operators are then performed to produce new solutions for next population. The genetic process is performed iteratively until the stop conditions have achieved. Last but not the least, empirical experiments were conducted on three financial datasets with different trends and a case study is also given to reveal the effectiveness and robustness of the designed GTSP-SLTP algorithm.

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Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

Section: Review Of Trading Strategy Optimizationmentioning

confidence: 99%

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Chen

Díaz

et al. 2021

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…There are automated asset management systems that select a person's investment portfolio based on his or her age, income, and level of risk aversion. Using such techniques can reduce transaction fees (Gao et al 2016;Ha and Zhang 2020), increase portfolio efficiency, better manage risk, and use market information and technology for successful decision-making (Wang et al 2009a). However, these algorithmic solutions also have drawbacks: they do not consider human capital, available financial assets, liabilities, and real estate (Scherer 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Intellectual Investment Portfolio as an Efficient Instrument to Manage Personal Financial Investment

Rutkauskas

Stasytytė

2022

JRFM

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The redistribution of resources in global stock markets is prevalent: the capital is transferred from one investor to another. Sometimes, earning a substantial return in the stock market seems complicated to implement for an individual investor. Investing contributes to the welfare of society and the wealth of citizens. This is why people should look for efficient ways to invest. Investment should become a natural part of personal finance management in the majority of households. For this reason, an investment model is developed where stocks are selected based only on market intelligence using historical data. The model helps find one or several stocks that generate the highest return on a separate step. Applying this model, experiments were performed with daily data from German, US, and UK stock markets. The possibility of obtaining higher than average returns in these markets has been noticed. In the German market, during the 97-day period, the authors obtained a 1.46 return, which implies a 2.31 annual return: in the USA market, a 2.37 return (7.93 annual return), and in the UK market, a 1.90 return (4.09 annual return). Thus, the proposed investment decision-making system could be an efficient tool for forming a sustainable individual or household portfolio. It can generate higher investment returns for an investor and, moreover, make the market more efficient by applying market intelligence and related historical data.

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“…Optimisation has gradually become a hot research topic for scholars, and investment portfolio problem has been further studied. So many papers examine the portfolio selection problem, see Alexander et al (2017), Kitapbayev and Leung (2016), Omidi et al (2016), Yang et al ( , 2020, Jung and Kim (2017), Huang et al (2018), Ha and Zhang (2020) and other references.…”

Section: Introductionmentioning

confidence: 99%

Optimal portfolio selection with transaction costs in compound binomial model

Jin¹,

Ou²

2022

IJADS

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Investors consider investing in an investment market including a risk-free asset plus a risky asset to get maximum profit. At the same time, the insurer has a proportional transaction cost every time they invest in risky assets in an actual situation. In this paper a compound binomial model with investment costs as well as considering proportion investment costs was studied. We want to get the optimal investment strategy to maximise the expected utility of terminal wealth in the financial market. In order to solve this problem, we use the analytical method and construct the no-transaction region at time t to help us solve the problem. Finally, the expressions of the optimal investment strategy and the value function are derived, respectively. In addition, on the basis of this paper, we can consider introducing other influencing factors, such as: random time exit, Markovian environmental impact and so on.

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Algorithmic trading for online portfolio selection under limited market liquidity

Cited by 18 publications

References 37 publications

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Integrated Intellectual Investment Portfolio as an Efficient Instrument to Manage Personal Financial Investment

Optimal portfolio selection with transaction costs in compound binomial model

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