A novel approach to dynamic portfolio trading system using multitree genetic programming

Mousavi, Somayeh; Esfahanipour, Akbar; Zarandi, M. H. Fazel

doi:10.1016/j.knosys.2014.04.018

Cited by 46 publications

(20 citation statements)

References 53 publications

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“…Hsu's [2011] research findings show that the SOM-GP procedure generates accurate forecasts. Mousavi et al [2014] develop a multi-tree GP forest approach for dynamic portfolio trading with transaction costs in the stock market of both developed (e.g., Toronto Stock Exchange) and emerging (e.g., Tehran Stock Exchange) countries. Numerical experiments performed by Mousavi et al [2014] demonstrate that the proposed model significantly outperforms other traditional portfolio selection models in terms of portfolio return and risk adjusted return.…”

Section: Asset Selection and Market Timingmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

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Section: Asset Selection and Market Timingmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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“…The sliding window approach was used to determine the training and testing periods. In this approach, the data of the most recent time window is used for training and the best trained system is then tested on the next time window [11,14]. In this work, four time windows were selected with the window sizes and the window shifts of six months.…”

Section: Datamentioning

confidence: 99%

“…After step-wise learning, GP learning is continued with all training instances until the maximum number of GP generations is reached. The step-wise learning approach is described in detail in [14,65].…”

Section: Training Periodmentioning

confidence: 99%

“…They used two distinct models for single stock price forecasting and evaluation and portfolio management. Recently, a portfolio trading system has been proposed that directly induces portfolio weights from time series of asset prices and volumes [14]. The multitree GP is used to learn the portfolio trading system considering transaction cost and risk.…”

Section: Introductionmentioning

confidence: 99%

“…GP has been used to learn the consequent part of TSK models with unusual structures [30] as well as compact and accurate fuzzy rule based classification systems for high dimensional problems [31]. In the context of financial forecasting, GP has been applied successfully in single stock trading system development [32][33][34] as well as portfolio trading system development [14]. Therefore, we will pay special attention to the GFSs which learn fuzzy rule bases by GP.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi

Esfahanipour

Zarandi

2015

Applied Soft Computing

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The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

Aloud

2020

Intell Sys Acc Fin Mgmt

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SUMMARY In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.

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A novel approach to dynamic portfolio trading system using multitree genetic programming

Cited by 46 publications

References 53 publications

A Survey on Financial Applications of Metaheuristics

A Survey on Financial Applications of Metaheuristics

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

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