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A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization

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Abstract

Stock market trading has been a subject of interest to investors, academicians, and researchers. Analysis of the inherent non-linear characteristics of stock market data is a challenging task. A large number of learning algorithms are developed to study market behaviours and enhance the prediction accuracy; they have been optimized using swarm and evolutionary computation such as particle swarm optimization (PSO); its global optimization ability with continuous data has been exploited in financial domains. Limitations in the existing approaches and potential future research directions for enhancing PSO-based stock market prediction are discussed. This article aims at balancing the economics and computational intelligence aspects; it also analyzes the superiority of PSO for stock portfolio optimization, stock price and trend prediction, and other related stock market aspects along with implications of PSO.

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Abbreviations

ABB:

Adaptive Bollinger Bands

ABC:

Artificial Bee Colony

ACC:

Acceleration

ACO:

Ant Colony Optimization

APSO:

Adaptive Particle Swarm Optimization

AMA:

Adaptive Moving Average

ANN:

Artificial Neural Network

BA:

Bat Algorithm

BAS:

Beetle Antennae Search

BB:

Bollinger Bands

BBPSO:

Bare-Bones Particle Swarm Optimization

BC:

Boundary Constraint

BEA:

Bat Echolocation Algorithm

BFO:

Bacterial Foraging Optimization

BiPSO:

Binary Particle Swarm Optimization

BP:

Back-Propagation

BR-ANN:

Bayesian-Regularized Artificial Neural Network

BSE:

Bombay Stock Exchange

BSO:

Beetle Swarm Optimization

CAPM:

Capital Asset Pricing Model

CARRX:

Conditional Autoregressive Range

CCEF:

Cardinality-Constrained Efficient Frontier

CCMV:

Cardinality-Constrained Mean-Variance

CCPSO:

Competitive Co-evolutionary Particle Swarm Optimization

CLPSO:

Comprehensive Learning Particle Swarm Optimization

CPSO:

Constriction factor-based Particle Swarm Optimization

CRPSO:

Cooperative Random learning Particle Swarm Optimization

CS:

Cuckoo Search

CSO:

Cat Swarm Optimization

CV:

Cross-Validation

CVaR:

Conditional Value-at-Risk

CV-PSO:

Continuous Velocity Particle Swarm Optimization

DDPSO:

Dimension-Decreasing Particle Swarm Optimization

DePSO:

Decimal Particle Swarm Optimization

DMA:

Dynamic Model Averaging

DMS:

Dynamic Multi-Swarm

DPSO:

Drift Particle Swarm Optimization

DRT:

Dynamic Random Topology

DSSPSO:

Dynamic Search Space Particle Swarm Optimization

EEMD:

Ensemble Empirical Mode Decomposition

EHO:

Elephant Herd Optimization

EMA:

Exponential Moving Average

EO:

External Optimization

EPSO:

Evolutionary Particle Swarm Optimization

ETF:

Exchange Traded Fund

EUA:

European Union Allowance

FCM:

Fuzzy C-Means

FMOPSO:

Fuzzy simulation-based Multi-Objective Particle Swarm Optimization

FPSO:

Fuzzy clustering-based Particle Swarm Optimization

FTB:

Financial Tool-Box

FWNN:

Fuzzy Wavelet Neural Network

GA:

Genetic Algorithm

GARCH:

Generalized Autoregressive Conditional Heteroskedasticity

GC:

Granular Computing

GD:

Gradient Descent

GRNN:

Generalized Regression Neural Network

GSA:

Gravitational Search Algorithm

GSM:

Global Stock Market

HFLANN:

Heuristic Functional Link Artificial Neural Network

HGSA:

Hybrid Gravitational Search Algorithm

HMOPSO:

Hybrid constraint-handling Multi-Objective Particle Swarm Optimization

HS:

Harmony Search

IA:

Immune Algorithm

ICS:

Improved Cuckoo Search

IMF:

Intrinsic Mode Function

IPO:

Initial Public Offering

IPSO:

Improved Particle Swarm Optimization

IVFCM:

Interval-Valued Fuzzy Cognitive Map

IWM:

Improved Wavelet Mutation

KOSPI:

Korea Composite Stock Price Index

lnMC:

Log of Market Capitalization

LS-SVM:

Least Square Support Vector Machine

LSSVR:

Least Squares Support Vector Regression

LSTM:

Long Short-Term Memory

MA:

Moving Average

MACD:

Moving Average Convergence/Divergence

MAD:

Mean Absolute Deviation

MF:

Mutual Fund

MLP:

Multi-Layer Perceptron

MM:

Minimax

MOEA/D:

Decomposition-based Multi-Objective Evolutionary Algorithm

MOLPSO:

Many Optimization Liaisons Particle Swarm Optimization

MOM:

Momentum

MOPSO:

Multi-Objective Particle Swarm Optimization

MSE:

Mean-Squared Error

MS-IDPSO:

Multi-Swarm of Improved self-adaptive Particle Swarm Optimization

M-V:

Mean-Variance

NARX:

Non-linear Autoregressive with Exogenous input

NBC:

Naïve Bayes Classifier

NN:

Neural Network

NPSO:

Normalized Particle Swarm Optimization

NSE:

National Stock Exchange

NSGA-II:

Non-dominated Sorting Genetic Algorithm-II

NYSE:

New York Stock Exchange

OBVA:

On-Balance Volume Average

PAA:

Piecewise Aggregate Approximation

PBMV:

Prediction-Based Mean-Variance

PLS:

Partial Least Squares

POCS:

Portfolio Optimization based on Clonal Selection

POCSPS:

Portfolio Optimization based on Clonal Selection integrated with Particle Swarm Optimization

PRS:

Performance-based Reward Strategy

PSO:

Particle Swarm Optimization

RBF:

Radial Basis Function

ROC:

Rate of Change

RRA:

Relative Risk Aversion

RRL:

Recurrent Reinforcement Learning

RSI:

Relative Strength Index

RT:

Random Topology

SAX:

Symbolic Aggregate Approximation

SD:

Standard Deviation

SET:

Stock Exchange of Thailand

SFIS:

Sequential Forward Input Selection

SMA:

Simple Moving Average

SML:

Security Market Line

SQP:

Sequential Quadratic Programming

SR-MOPSO:

Self-Regulating Multi-Objective Particle Swarm Optimization

SSE:

Shanghai Stock Exchange

SSO:

Simplified Swarm Optimization

STO:

Stochastic Oscillator

S-V:

Semi-Variance

SVM:

Support Vector Machine

SV-PSO:

Sparse Velocity Particle Swarm Optimization

SVR:

Support Vector Regression

TMA:

Triangular Moving Average

TPSO:

Turbulent Particle Swarm Optimization

TRB:

Trading Range Breakout

TVPSO:

Time Variant Particle Swarm Optimization

UC:

Unconstrained

UEF:

Unconstrained Efficient Frontier

VaR:

Value-at-Risk

VMD:

Variational Mode Decomposition

VR:

Variable Ranking

VwS:

Variance with Skewness

WM:

Wavelet Mutation

WMA:

Weighted Moving Average

WNN:

Wavelet Neural Network

WPSO:

Inertia Weight-based Particle Swarm Optimization

WRS:

Weight Reward Strategy

References

  1. Matvos G, Seru A, Silva RC (2018) Financial market frictions and diversification. J Financ Econ 127(1):21

    Google Scholar 

  2. Pramanaswari ASI, Yasa GW (2018) Graham & Dodd theory in stock portfolio performance in LQ 45 index at Indonesia stock exchange. Int Res J Manag IT Soc Sci 5(6):52

    Google Scholar 

  3. Jordan BD, Miller TW, Dolvin SD (2015) Fundamentals of investments: valuation and management. McGraw-Hill Education, New York

    Google Scholar 

  4. Elbannan MA (2015) The capital asset pricing model: an overview of the theory. Int J Econ Finance 7(1):216

    Google Scholar 

  5. Elton EJ, Gruber MJ, Brown SJ, Goetzmann WN (2009) Modern portfolio theory and investment analysis. Wiley, London

    Google Scholar 

  6. Abbey BS, Doukas JA (2012) Is technical analysis profitable forindividual currency traders? J Portfolio Manag 39(1):142

    Google Scholar 

  7. Lau CKM, Demir E, Bilgin MH (2013) Experience-based corporate corruption and stock market volatility: evidence from emerging markets. Emerg Markets Rev 17:1

    Google Scholar 

  8. Durusu-Ciftci D, Ispir MS, Yetkiner H (2017) Financial development and economic growth: some theory and more evidence. J Policy Model 39(2):290

    Google Scholar 

  9. López RA (2005) Trade and growth: reconciling the macroeconomic and microeconomic evidence. J Econ Surv 19(4):623

    Google Scholar 

  10. Bahloul S, Mroua M, Naifar N (2017) The impact of macroeconomic and conventional stock market variables on Islamic index returns under regime switching. Borsa Istanbul Rev 17(1):62

    Google Scholar 

  11. Bettman JL, Sault SJ, Schultz EL (2009) Fundamental and technical analysis: substitutes or complements? Account Finance 49(1):21

    Google Scholar 

  12. Wafi AS, Hassan H, Mabrouk A (2015) Fundamental analysis models in financial markets-review study. Proc Econ Finance 30:939

    Google Scholar 

  13. Thakkar A, Chaudhari K (2020) CREST: cross-reference to exchange-based stock trend prediction using long short-term memory. Proc Comput Sci 167:616

    Google Scholar 

  14. Wafi AS, Hassan H, Mabrouk A (2015) Fundamental analysis vs technical analysis in the Egyptian stock exchange-empirical study. Int J Business Manag Study IJBMS 2(2):212

    Google Scholar 

  15. Gilbert CL (2010) Commodity speculation and commodity investment. Market Rev 1:26

    Google Scholar 

  16. Bogle JC (2012) The clash of the cultures: investment vs. speculation. Wiley, London

    Google Scholar 

  17. Cavalcante RC, Brasileiro RC, Souza VL, Nobrega JP, Oliveira AL (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194

    Google Scholar 

  18. Mullainathan S, Spiess J (2017) Machine learning: an applied econometric approach. J Econ Perspect 31(2):87

    Google Scholar 

  19. Oliveira N, Cortez P, Areal N (2017) The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst Appl 73:125

    Google Scholar 

  20. Xing FZ, Cambria E, Welsch RE (2018) Natural language based financial forecasting: a survey. Artif Intel Rev 50(1):49

    Google Scholar 

  21. Azar AT, Vaidyanathan S (2015) Computational intelligence applications in modeling and control. Springer, Berlin

    Google Scholar 

  22. Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1

    Google Scholar 

  23. Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. In: Nature-inspired computing and optimization (Springer), pp. 475–494

  24. Chaudhari K, Thakkar A (2019) Travelling salesman problem: an empirical comparison between ACO, PSO, ABC, FA and GA. In: Emerging research in computing, information, communication and applications. Springer, pp. 397–405

  25. Slowik A, Kwasnicka H (2017) Nature inspired methods and their industry applications-Swarm intelligence algorithms. IEEE Trans Ind Inf 14(3):1004

    Google Scholar 

  26. Balamurugan R, Natarajan A, Premalatha K (2015) Stellar-mass black hole optimization for biclustering microarray gene expression data. Appl Artif Intel 29(4):353

    Google Scholar 

  27. Ertenlice O, Kalayci CB (2018) A survey of swarm intelligence for portfolio optimization: algorithms and applications. Swarm Evol Comput 39:36

    Google Scholar 

  28. Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387

    Google Scholar 

  29. Wong WK, Manzur M, Chew BK (2003) How rewarding is technical analysis? Evidence from Singapore stock market. Appl Financ Econ 13(7):543

    Google Scholar 

  30. Neely CJ, Rapach DE, Tu J, Zhou G (2014) Forecasting the equity risk premium: the role of technical indicators. Manage Sci 60(7):1772

    Google Scholar 

  31. AnyChart (2019) Stock charts technical indicators mathematical description. https://docs.anychart.com/Stock_Charts/Technical_Indicators/Mathematical_Description. Accessed 05 Oct 2019

  32. Sedighizadeh D, Masehian E (2009) Particle swarm optimization methods, taxonomy and applications. Int J Comput Theory Eng 1(5):486

    Google Scholar 

  33. Krollner B, Vanstone BJ, Finnie GR (2010) Financial time series forecasting with machine learning techniques: a survey. In: ESANN

  34. Garcia-Gonzalo E, Fernández-Martínez JL (2012) A brief historical review of particle swarm optimization (PSO). J Bioinf Intel Control 1(1):3

    Google Scholar 

  35. Beiranvand V, Bakar AA, Othman Z (2012) A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. In: 2012 7th international conference on computing and convergence technology (ICCCT) (IEEE), pp 332–337

  36. Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015:1–38

    MathSciNet  MATH  Google Scholar 

  37. Kar AK (2016) Bio inspired computing: a review of algorithms and scope of applications. Expert Syst Appl 59:20

    Google Scholar 

  38. Das SR, Mishra D, Rout M (2017) A survey on impact of bio-inspired computation on stock market prediction. J Eng Sci Technol Rev 10(3):104

    Google Scholar 

  39. Bharne PK, Prabhune SS (2017) Survey on combined swarm intelligence and ANN for optimized daily stock market price. In: 2017 international conference on soft computing and its engineering applications (icSoftComp) (IEEE), pp 1–6

  40. Kalayci CB, Ertenlice O, Akbay MA (2019) A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Syst Appl 4:2019

    Google Scholar 

  41. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science (IEEE), pp 39–43

  42. Van Den Bergh F, et al (2001) An analysis of particle swarm optimizers. Ph.D. thesis, University of Pretoria South Africa

  43. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360) (IEEE), pp 69–73

  44. Dali N, Bouamama S (2015) GPU-PSO: parallel particle swarm optimization approaches on graphical processing unit for constraint reasoning: case of Max-CSPs. Proc Comput Sci 60:1070

    Google Scholar 

  45. AlRashidi MR, El-Hawary ME (2008) A survey of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13(4):913

    Google Scholar 

  46. Kulkarni RV, Venayagamoorthy GK (2010) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybernet Part C (Applications and Reviews) 41(2):262

    Google Scholar 

  47. Rana S, Jasola S, Kumar R (2011) A review on particle swarm optimization algorithms and their applications to data clustering. Artif Intell Rev 35(3):211

    Google Scholar 

  48. Sarkar S, Roy A, Purkayastha BS (2013) Application of particle swarm optimization in data clustering: a survey. Int J Comput Appl 65(25):38

    Google Scholar 

  49. Xie X, Wz Jiang, Nie H, Chi Jh (2016) Empirical study on how to set prices for cruise cabins based on improved quantum particle swarm optimization. Comput Inf Sci 9(2):82

    Google Scholar 

  50. Masdari M, Salehi F, Jalali M, Bidaki M (2017) A survey of PSO-based scheduling algorithms in cloud computing. J Netw Syst Manage 25(1):122

    Google Scholar 

  51. Li W, Huyan J, Xiao L, Tighe S, Pei L (2019) International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization. Expert Syst Appl X 2:100006

    Google Scholar 

  52. Okulewicz M (2017) 2-Dimensional rectangles-in-circles packing and stock cutting with particle swarm optimization. In: 2017 IEEE symposium series on computational intelligence (SSCI) (IEEE), pp 1–5

  53. Abraham A, Rohini V (2019) A particle swarm optimization-backpropagation (PSO-BP) model for the prediction of earthquake in Japan. In: Emerging research in computing, information, communication and applications. Springer, pp 435–441

  54. Briza AC, Naval PC Jr (2011) Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Appl Soft Comput 11(1):1191

    Google Scholar 

  55. Botosan CA, Plumlee MA (2005) Assessing alternative proxies for the expected risk premium. Account Rev 80(1):21

    Google Scholar 

  56. Dye CY, Ouyang LY (2011) A particle swarm optimization for solving joint pricing and lot-sizing problem with fluctuating demand and trade credit financing. Comput Ind Eng 60(1):127

    Google Scholar 

  57. Dye CY (2012) A finite horizon deteriorating inventory model with two-phase pricing and time-varying demand and cost under trade credit financing using particle swarm optimization. Swarm Evol Comput 5:37

    Google Scholar 

  58. Hung JC (2011) Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization. Inf Sci 181(20):4673

    Google Scholar 

  59. Liyan G, Zhanfu Z (2012) CARRX model based on LSSVR optimized by adaptive PSO. In: 2012 third international conference on digital manufacturing & automation (IEEE), pp 268–271

  60. Kuo RJ, Chao CM, Chiu Y (2011) Application of particle swarm optimization to association rule mining. Appl Soft Comput 11(1):326

    Google Scholar 

  61. Qin X, Peng Q (2012) Stock turning point recognition using multiple model algorithm with multiple types of features. In: Proceedings of the 10th world congress on intelligent control and automation (IEEE), pp 4020–4025

  62. Butler M, Kazakov D (2012) Testing implications of the adaptive market hypothesis via computational intelligence. In: 2012 IEEE conference on computational intelligence for financial engineering & economics (CIFEr) (IEEE), pp 1–8

  63. Pinto T, Morais H, Sousa TM, Sousa T, Vale Z, Praca I, Faia R, Pires EJS (2015) Adaptive portfolio optimization for multiple electricity markets participation. IEEE Trans Neural Netw Learn Syst 27(8):1720

    MathSciNet  Google Scholar 

  64. Faia R, Pinto T, Vale Z, Corchado JM (2018) Optimization of multiple electricity markets participation using evolutionary PSO. In: 2018 IEEE/PES transmission and distribution conference and exposition (T&D) (IEEE), pp 1–9

  65. Faia R, Pinto T, Vale Z, Corchado JM (2017) Hybrid particle swarm optimization of electricity market participation portfolio. In: 2017 IEEE symposium series on computational intelligence (SSCI) (IEEE), pp 1–8

  66. Faia R, Pinto T, Vale Z, Corchado JM (2018) Multi-objective portfolio optimization of electricity markets participation. In: 2018 power systems computation conference (PSCC) (IEEE), pp 1–6

  67. Faia R, Pinto T, Vale Z, Corchado JM (2019) Hybrid approach based on particle swarm optimization for electricity markets participation. Energy Inf 2(1):1

    Google Scholar 

  68. Tekın P, Erol R (2017) A new hybrid model for dynamic pricing strategies of perishable products. In: 2017 seventh international conference on innovative computing technology (INTECH) (IEEE), pp 85–89

  69. Xiong T (2018) Forecasting soybean futures price using dynamic model averaging and particle swarm optimization. In: Proceedings of the genetic and evolutionary computation conference companion (ACM), pp 75–76

  70. Zhang JL, Zhang YJ, Zhang L (2015) A novel hybrid method for crude oil price forecasting. Energy Econ 49:649

    Google Scholar 

  71. Hu Y, Sun X, Nie X, Li Y, Liu L (2019) An enhanced LSTM for trend following of time series. IEEE Access 7:34020

    Google Scholar 

  72. Paravisini D, Rappoport V, Ravina E (2016) Risk aversion and wealth: Evidence from person-to-person lending portfolios. Manage Sci 63(2):279

    Google Scholar 

  73. Agarwal V, Mullally KA, Tang Y, Yang B (2015) Risk aversion and wealth: evidence from person-to-person lending portfolios. J Finance 70(6):2733

    Google Scholar 

  74. Guiso L, Sapienza P, Zingales L (2018) Time varying risk aversion. J Financ Econ 128(3):403

    Google Scholar 

  75. Afik Z, Arad O, Galil K (2016) Using Merton model for default prediction: an empirical assessment of selected alternatives. J Empir Finance 35:43

    Google Scholar 

  76. Rua A, Nunes LC (2009) International comovement of stock market returns: a wavelet analysis. J Empir Finance 16(4):632

    Google Scholar 

  77. Li Z, Liu Y, Tan S, Liu B, Li J (2011) A novel time-scale feature based hybrid portfolio selection model for index fund. In: 2011 fourth international conference on business intelligence and financial engineering (IEEE), pp 63–67

  78. Mishra SK, Panda G, Majhi B (2016) Prediction based mean-variance model for constrained portfolio assets selection using multiobjective evolutionary algorithms. Swarm Evol Comput 28:117

    Google Scholar 

  79. Golmakani HR, Fazel M (2011) Constrained portfolio selection using particle swarm optimization. Expert Syst Appl 38(7):8327

    Google Scholar 

  80. Garcia R, Ghysels E, Renault E (2010) The econometrics of option pricing. In: Handbook of financial econometrics: tools and techniques (Elsevier), pp 479–552

  81. Sharma B, Thulasiram RK, Thulasiraman P (2012) Portfolio management using particle swarm optimization on GPU. In: 2012 IEEE 10th international symposium on parallel and distributed processing with applications (IEEE), pp 103–110

  82. Liang JJ, Qu BY (2013) Large-scale portfolio optimization using multiobjective dynamic mutli-swarm particle swarm optimizer. In: 2013 IEEE symposium on swarm intelligence (SIS) (IEEE), pp 1–6

  83. Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005. (IEEE), pp 124–129

  84. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281

    Google Scholar 

  85. Wang Jb, Chen WN, Zhang J, Lin Y (2015) A dimension-decreasing particle swarm optimization method for portfolio optimization. In: Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation (ACM), pp 1515–1516

  86. Tsujimoto T, Shindo T, Kimura T, Jin’no K (2012) A relationship between network topology and search performance of PSO. In: 2012 IEEE congress on evolutionary computation (IEEE), pp 1–6

  87. Ni Q, Deng J (2013) A new logistic dynamic particle swarm optimization algorithm based on random topology. Sci World J 2013:409167

    Google Scholar 

  88. Yin X, Ni Q, Zhai Y (2015) A novel particle swarm optimization for portfolio optimization based on random population topology strategies. In: International conference in swarm intelligence (Springer), pp 164–175

  89. Ni Q, Yin X, Tian K, Zhai Y (2017) Particle swarm optimization with dynamic random population topology strategies for a generalized portfolio selection problem. Nat Comput 16(1):31

    MathSciNet  MATH  Google Scholar 

  90. Reid SG, Malan KM (2015) Constraint handling methods for portfolio optimization using particle swarm optimization. In: 2015 IEEE symposium series on computational intelligence (IEEE), pp 1766–1773

  91. Adebiyi A, Ayo C (2015) Improved constrained portfolio selection model using particle swarm optimization. Indian J Sci Technol 8(31):1

    Google Scholar 

  92. Kamili H, Riffi ME (2016) A comparative study on portfolio optimization problem. In: 2016 International conference on engineering & MIS (ICEMIS) (IEEE), pp 1–8

  93. Fieldsend JE, Matatko J, Peng M (2004) Cardinality constrained portfolio optimisation. In: International conference on intelligent data engineering and automated learning (Springer), pp 788–793

  94. Chen C, Ys Zhou (2018) Robust multiobjective portfolio with higher moments. Expert Syst Appl 100:165

    Google Scholar 

  95. Wang B, Li Y, Wang S, Watada J (2018) A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio. IEEE Trans Fuzzy Syst 26(6):3673

    Google Scholar 

  96. Rajabi M, Khaloozadeh H (2018) Investigation and comparison of the performance of multi-objective evolutionary algorithms based on decomposition and dominance in portfolio optimization. In: Iranian conference on electrical engineering (ICEE) (IEEE), pp 923–929

  97. Maopeng R, Qing W, Chaoyang D (2014) A dynamic search space particle swarm optimization algorithm based on population entropy. In: The 26th Chinese control and decision conference (2014 CCDC) (IEEE), pp 4292–4296

  98. Feng C, Dong Y, Jiang Y, Ran M (2018) Dynamic search space particle swarm optimization approach for portfolio optimization. In: Proceedings of the 2018 international conference on control and computer vision (ACM), pp 127–131

  99. Liu C, Yin Y (2018) Particle swarm optimised analysis of investment decision. Cogn Syst Res 52:685

    Google Scholar 

  100. Kaucic M (2019) Equity portfolio management with cardinality constraints and risk parity control using multi-objective particle swarm optimization. Comput Oper Res 109:300

    MathSciNet  MATH  Google Scholar 

  101. Mishra SK, Panda G, Majhi R (2014) A comparative performance assessment of a set of multiobjective algorithms for constrained portfolio assets selection. Swarm Evol Comput 16:38

    Google Scholar 

  102. Boudt K, Wan C (2019) The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization. Optim Lett 2019:1–12

    MATH  Google Scholar 

  103. Kuo R, Hong C (2013) Integration of genetic algorithm and particle swarm optimization for investment portfolio optimization. Appl Math Inf Sci 7(6):2397

    Google Scholar 

  104. Cortés DG, Jofré AJC, San Martín L (2018) Artificial intelligence based method for portfolio selection. In: 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI) (IEEE), pp 1–4

  105. Najafi AA, Mushakhian S (2015) Multi-stage stochastic mean-semivariance-CVaR portfolio optimization under transaction costs. Appl Math Comput 256:445

    MathSciNet  MATH  Google Scholar 

  106. Tsai JT, Ho WH, Liu TK, Chou JH (2007) Improved immune algorithm for global numerical optimization and job-shop scheduling problems. Appl Math Comput 194(2):406

    MathSciNet  MATH  Google Scholar 

  107. Shalan SAB, Ykhlef M (2015) Solving multi-objective portfolio optimization problem for Saudi Arabia stock market using hybrid clonal selection and particle swarm optimization. Arab J Sci Eng 40(8):2407

    MathSciNet  MATH  Google Scholar 

  108. Pai GV, Michel T (2017) Metaheuristic optimization of constrained large portfolios using hybrid particle swarm optimization. IJAMC 8(1):1

    Google Scholar 

  109. Chen T, Zhu Y, Teng J (2018) Beetle swarm optimisation for solving investment portfolio problems. J Eng 2018(16):1600

    Google Scholar 

  110. Chen C, Chen By (2018) Complex portfolio selection using improving particle swarm optimization approach. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on smart city; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS) (IEEE), pp 828–835

  111. Zaheer KB, Aziz MIBA, Kashif AN, Raza SMM (2018) Two stage portfolio selection and optimization model with the hybrid particle swarm optimization. Matematika 34(1):125

    MathSciNet  Google Scholar 

  112. Sethia AM (2018) Application of swarm intelligence to portfolio optimisation. In: 2018 international conference on computing, power and communication technologies (GUCON) (IEEE), pp 1029–1033

  113. Almahdi S, Yang SY (2019) A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning. Expert Syst Appl 130:145

    Google Scholar 

  114. Burney SA, Jilani T, Tariq H, Asim Z, Amjad U, Mohammad SS (2019) A portfolio optimization algorithm using fuzzy granularity based clustering, BRAIN. Broad Res Artif Intell Neurosci 10(2):159

    Google Scholar 

  115. Soni S (2011) Applications of ANNs in stock market prediction: a survey. Int J Comput Sci Eng Technol 2(3):71

    Google Scholar 

  116. Sharma A, Bhuriya D, Singh U (2017) Survey of stock market prediction using machine learning approach. In: 2017 International conference of electronics, communication and aerospace technology (ICECA), vol. 2 (IEEE). pp 506–509

  117. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Syst Appl 83:187

    Google Scholar 

  118. Xie Gq (2011) The optimization of share price prediction model based on support vector machine. In: 2011 International conference on control, automation and systems engineering (CASE) (IEEE), pp 1–4

  119. Xiao Y, Xiao M, Zhao F (2012) Improving financial returns using neural networks and adaptive particle swarm optimization. In: 2012 Fifth international conference on business intelligence and financial engineering (IEEE), pp 15–19

  120. Xiao Y, Xiao J, Lu F, Wang S (2014) Ensemble ANNs-PSO-GA approach for day-ahead stock e-exchange prices forecasting. Int J Comput Intell Syst 7(2):272

    Google Scholar 

  121. Sun Y, Gao Y (2015) An improved hybrid algorithm based on PSO and BP for stock price forecasting. Open Cybern Syst J 9(1):2565

    Google Scholar 

  122. Yu J, Kim S (2015) Automatic structure identification of TSK fuzzy model for stock index forecasting. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE) (IEEE), pp 1–8

  123. Sands TM, Tayal D, Morris ME, Monteiro ST (2015) Robust stock value prediction using support vector machines with particle swarm optimization. In: 2015 IEEE congress on evolutionary computation (CEC) (IEEE), pp 3327–3331

  124. Yin S, Wu F, Luo H, Gao H (2015) Support vector regression based approach for key index forecasting with applications. In: 2015 IEEE 13th international conference on industrial informatics (INDIN) (IEEE), pp 591–596

  125. Siddique M, Mohanty S, Panda D (2018) A hybrid forecasting model for prediction of stock value of tata steel using support vector regression and particle swarm optimization. Int J Pure Appl Math 119(14):1719

    Google Scholar 

  126. Guo Y, Han S, Shen C, Li Y, Yin X, Bai Y (2018) An adaptive SVR for high-frequency stock price forecasting. IEEE Access 6:11397

    Google Scholar 

  127. Yeh WC (2013) New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series. IEEE Trans Neural Netw Learn Syst 24(4):661

    Google Scholar 

  128. Yeh WC, Huang CL (2015) Simplified swarm optimization to solve the K-harmonic means problem for mining data. In: Proceedings of the 18th Asia pacific symposium on intelligent and evolutionary systems, Vol 2 (Springer), pp 429–439

  129. Pan J, Tang YY, Wang Y, Zheng X, Luo H, Yuan H, Wang PSP (2016) A hybrid swarm optimization for neural network training with application in stock price forecasting. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC) (IEEE), pp 004450–004453

  130. Jamous RA, Seidy EE, Bayoum BI (2016) A Novel efficient forecasting of stock market using particle swarm optimization with center of mass based technique. Int J Adv Comput Sci Appl 7(4):342

    Google Scholar 

  131. Lahmiri S (2016) Intraday stock price forecasting based on variational mode decomposition. J Comput Sci 12:23

    Google Scholar 

  132. Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531

    MathSciNet  MATH  Google Scholar 

  133. Mahanta R, Pandey TN, Jagadev AK, Dehuri S (2016) Optimized radial basis functional neural network for stock index prediction. In: 2016 International conference on electrical, electronics, and optimization techniques (ICEEOT) (IEEE), pp 1252–1257

  134. Swami P, Vyas R (2016) Prediction of stock rates using PSO hybridized BPNN model. Int J 6(2):117

    MathSciNet  Google Scholar 

  135. Ghasemiyeh R, Moghdani R, Sana SS (2017) A hybrid artificial neural network with metaheuristic algorithms for predicting stock price. Cybern Syst 48(4):365

    Google Scholar 

  136. Siddique M, DebdulalPanda SD, Mohapatra SK (2017) A hybrid forecasting model for stock value prediction using soft computing. Int J Pure Appl Math 117(19):357

    Google Scholar 

  137. Wang KC, Yang CI, Chang KF (2017) Stock prices forecasting based on wavelet neural networks with PSO. In: MATEC web of conferences, vol. 119 (EDP Sciences), p 01029

  138. Yan D, Zhou Q, Wang J, Zhang N (2017) Bayesian regularisation neural network based on artificial intelligence optimisation. Int J Prod Res 55(8):2266

    Google Scholar 

  139. Zhang Z, Shen Y, Zhang G, Song Y, Zhu Y (2017) Short-term prediction for opening price of stock market based on self-adapting variant PSO-Elman neural network. In: 2017 8th IEEE international conference on software engineering and service science (ICSESS) (IEEE), pp 225–228

  140. Ostadi B, Motamedi Sedeh O, Husseinzadeh Kashan A, Amin-Naseri MR (2018) An intelligent model to predict the day-ahead deregulated market clearing price: a hybrid NN, PSO and GA approach, Scientia Iranica

  141. Lu T, Li Z (2017) Forecasting CSI 300 index using a hybrid functional link artificial neural network and particle swarm optimization with improved wavelet mutation. In: 2017 International conference on computer network, electronic and automation (ICCNEA) (IEEE), pp 241–246

  142. Kaplan SN, Moskowitz TJ, Sensoy BA (2013) The effects of stock lending on security prices: an experiment. J Finance 68(5):1891

    Google Scholar 

  143. Papacostantis E, Engelbrecht AP (2011) Coevolutionary particle swarm optimization for evolving trend reversal indicators. In: 2011 IEEE symposium on computational intelligence for financial engineering and economics (CIFEr) (IEEE), pp 1–8

  144. Beber A, Pagano M (2013) Short-selling bans around the world: evidence from the 2007–09 crisis. J Finance 68(1):343

    Google Scholar 

  145. Chiang WC, Enke D, Wu T, Wang R (2016) An adaptive stock index trading decision support system. Expert Syst Appl 59:195

    Google Scholar 

  146. Chiong R, Fan Z, Hu Z, Adam MT, Lutz B, Neumann D (2018) A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In: Proceedings of the genetic and evolutionary computation conference companion (ACM), pp 278–279

  147. Albert AA, de Mingo López LF, Blas NG (2019) Multilinear weighted regression (MWE) with neural networks for trend prediction. Appl Soft Comput 58:105555

    Google Scholar 

  148. Zhen LZ, Choo YH, Muda AK, Abraham A (2013) Forecasting FTSE bursa malaysia klci trend with hybrid particle swarm optimization and support vector machine technique. In: 2013 World congress on nature and biologically inspired computing (IEEE), pp 169–174

  149. Li J, Liu G, Yeung HWF, Yin J, Chung YY, Chen X (2017) A novel stacked denoising autoencoder with swarm intelligence optimization for stock index prediction. In: 2017 International joint conference on neural networks (IJCNN) (IEEE), pp 1956–1961

  150. Chen H, Chen S, Chen Z, Li F (2017) Empirical investigation of an equity pairs trading strategy. Manage Sci 65(1):370

    Google Scholar 

  151. Hutchinson MC, O’Brien J (2018) Testing futures trading strategy assumptions

  152. Aitken M, Cumming D, Zhan F (2015) Exchange trading rules, surveillance and suspected insider trading. J Corpor Finance 34:311

    Google Scholar 

  153. Wang F, Philip L, Cheung DW (2012) Complex stock trading strategy based on particle swarm optimization. In: 2012 IEEE conference on computational intelligence for financial engineering & economics (CIFEr) (IEEE), pp 1–6

  154. Wang F, Philip L, Cheung DW (2014) Combining technical trading rules using particle swarm optimization. Expert Syst Appl 41(6):3016

    Google Scholar 

  155. Wang F, Philip L, Cheung DW (2014) Combining technical trading rules using parallel particle swarm optimization based on Hadoop. In: 2014 International joint conference on neural networks (IJCNN) (IEEE), pp 3987–3994

  156. Liu X, An H, Wang L, Jia X (2017) An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms. Appl Energy 185:1778

    Google Scholar 

  157. Wang L, An H, Xia X, Liu X, Sun X, Huang X (2014) Generating moving average trading rules on the oil futures market with genetic algorithms. Math Probl Eng 2014:58

    Google Scholar 

  158. Jingshu L, Shuguang Z (2011) Empirical research on hedging strategy of Chinese index future market. In: MSIE 2011 (IEEE), pp 731–734

  159. Berk JB, Van Binsbergen JH (2015) Measuring skill in the mutual fund industry. J Financ Econ 118(1):1

    Google Scholar 

  160. Barber BM, Huang X, Odean T (2016) Which factors matter to investors? Evidence from mutual fund flows. Rev Financi Stud 29(10):2600

    Google Scholar 

  161. Hsu LY, Horng SJ, He M, Fan P, Kao TW, Khan MK, Run RS, Lai JL, Chen RJ (2011) Mutual funds trading strategy based on particle swarm optimization. Expert Syst Appl 38(6):7582

    Google Scholar 

  162. Souza VL, Brasileiro RC, Oliveira AL (2015) A PAA-PSO technique for investment strategies in the financial market. In: IJCNN, pp 1–8

  163. Ucar I, Ozbayoglu AM, Ucar M (2015) Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms. In: 2015 IEEE congress on evolutionary computation (CEC) (IEEE), pp 2526–2531

  164. Worasucheep C, Nuannimnoi S, Khamvichit R, Attagonwantana P (2017) An automatic stock trading system using particle swarm optimization. In: 2017 14th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON)

  165. Brasileiro RC, Souza VL, Oliveira AL (2017) Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation. Decis Support Syst 104:79

    Google Scholar 

  166. Chen SM, Manalu GMT, Pan JS, Liu HC (2013) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans Cybern 43(3):1102

    Google Scholar 

  167. Chen SM, Jian WS (2017) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Inf Sci 391:65

    Google Scholar 

  168. Bagheri A, Peyhani HM, Akbari M (2014) Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Syst Appl 41(14):6235

    Google Scholar 

  169. Hajizadeh E, Mahootchi M, Esfahanipour A, Kh MM (2015) A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility. Neural Comput Appl 2015:1–9

    Google Scholar 

  170. Pradeepkumar D, Ravi V (2017) Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Appl Soft Comput 58:35

    Google Scholar 

  171. M’ng JCP, Mehralizadeh M, (2016) Forecasting East Asian indices futures via a novel hybrid of wavelet-PCA denoising and artificial neural network models. PloS one 11(6):e0156338

    Google Scholar 

  172. El Hami N, Bouchekourte M (2016) Optimising liquidity with modified particle swarm optimization application: case of casablanca stock exchange. In: 2016 4th IEEE international colloquium on information science and technology (CiSt) (IEEE), pp 725–729

  173. Huang Q, Yang J, Feng X, Liew AWC, Li X (2019) Automated trading point forecasting based on bicluster mining and fuzzy inference. IEEE Trans Fuzzy Syst 6:755

    Google Scholar 

  174. Durán-Rosal AM, Gutiérrez PA, Carmona-Poyato Á, Hervás-Martínez C (2019) A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation. Neurocomputing

  175. Barak S (2017) Dependency evaluation of financial market returns for classifying and grouping stocks. In: 2017 3rd Iranian conference on intelligent systems and signal processing (ICSPIS) (IEEE), pp 193–198

  176. Luss R, d’Aspremont A (2015) Predicting abnormal returns from news using text classification. Quant Finance 15(6):999

    MathSciNet  MATH  Google Scholar 

  177. Hajek P, Prochazka O (2017) Learning interval-valued fuzzy cognitive maps with PSO algorithm for abnormal stock return prediction. In: International conference on theory and practice of natural computing (Springer), pp 113–125

  178. Gandoman SH, Kiamehr N, Hemetfar M et al (2017) Forecasting initial public offering pricing using particle swarm optimization (PSO) algorithm and support vector machine (SVM) In Iran. Business Econ Res 7(1):336

    Google Scholar 

  179. Iansiti M, Lakhani KR (2017) The truth about blockchain. Harvard Business Rev 95(1):118

    Google Scholar 

  180. Indera N, Yassin I, Zabidi A, Rizman Z (2017) Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators. J Fund Appl Sci 9(3S):791

    Google Scholar 

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  1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382 481, India

    Ankit Thakkar & Kinjal Chaudhari

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Thakkar, A., Chaudhari, K. A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization. Arch Computat Methods Eng 28, 2133–2164 (2021). https://doi.org/10.1007/s11831-020-09448-8

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