رویکردهای نوین در مدل سازی و پیش بینی بازارهای مالی: پیشرفت های اخیر و افق های آینده

رجبی, مهسا; خالوزاده, حمید

دوره 17، شماره 2 - ( مجله کنترل، جلد 17، شماره 2، تابستان 1402 ) جلد 17 شماره 2,1402 صفحات 163-149 | برگشت به فهرست نسخه ها

Mendeley

Zotero

RefWorks

Rajabi M, Khaloozadeh H. New approaches in modeling and forecasting financial markets: recent progress and future horizons. JoC 2023; 17 (2) :149-163
URL: http://joc.kntu.ac.ir/article-1-1001-fa.html

رجبی مهسا، خالوزاده حمید. رویکردهای نوین در مدل سازی و پیش بینی بازارهای مالی: پیشرفت های اخیر و افق های آینده. مجله کنترل. 1402; 17 (2) :149-163

URL: http://joc.kntu.ac.ir/article-1-1001-fa.html

رویکردهای نوین در مدل سازی و پیش بینی بازارهای مالی: پیشرفت های اخیر و افق های آینده

مهسا رجبی¹

، حمید خالوزاده^*

1- دانشکده مهندسی برق، گروه کنترل و سیستم، دانشگاه صنعتی خواجه نصیرالدین طوسی،تهران، ایران

چکیده: (1003 مشاهده)

سری¬های زمانی مالی اساسا پیچیده، دینامیک، نویزی، غیرخطی، غیرپارامتری و آشوبگونه هستند، لذا پیش بینی بازارهای مالی به عنوان یکی از چالش برانگیزترین زمینه ها در حوزه مهندسی و اقتصاد مطرح می باشد. با پیشرفت روزافزون هوش مصنوعی و روی کارآمدن روش های نوین یادگیری عمیق، مسأله پیش بینی بازار سهام با تحولات چشمگیری بخصوص در زمینه مدل های پیاده سازی و نیز حجم عظیمی از انواع داده های ورودی همراه شده است. چهار گام مهم برای ایجاد یک ساختار پیش بینی هوشمند سیستماتیک شامل: ورودی های مدل، انتخاب الگوریتم های پیش بینی و ارائه ساختار کلی مدل پیش بین، بکاربردن توابع خطای متناسب با مسأله جهت آموزش الگوریتم یادگیری و در نهایت ارزیابی صحیح نتایج با توجه به معیارهای مورد نظر می باشد. در این مقاله مرور جامعی بر رویکردهای أخیر مسأله پیش بینی بازار سهام با تمرکز بر چهار عامل فوق ارائه گردیده است. مهم ترین دست آورد های این مقاله عبارتند از: 1- بررسی همه جانبه مسأله شامل: مرور انواع ورودی های مدل، ساختارهای مختلف پیش بینی، آموزش مدل و انواع توابع خطای بکار برده شده، و نیز سنجه های ارزیابی نتایج، بصورت کاملا طبقه بندی شده و ساختاریافته بطوریکه نقشه راه مسأله و چالش های موجود را بسادگی در اختیار علاقه مندان قرار دهد و هر بخش زمینه پژوهشی مهمی را به پژوهشگران ارائه نماید. 2- تحلیل کامل هر بخش، مشخص کردن کاربرد هریک از روش ها و بحث و بررسی مزایا و معایب آن ها براساس آخرین پیشرفت ها و ارائه چشم اندازهایی از مرزهای پژوهشی مسأله 3- مشخص کردن مسیر تحقیقاتی درحال انجام، رویکردهای آینده و مسائل باز جهت کمک به محققان و پژوهشگران علاقه مند به این حوزه.

واژه‌های کلیدی: بازارهای مالی، پیش بینی سری زمانی، روش های یادگیری عمیق، توابع خطا، سنجه های ارزیابی

متن کامل [PDF 562 kb] (132 دریافت)

نوع مطالعه: پژوهشي | موضوع مقاله: شماره ویژه (رویکرد های نو در مهندسی کنترل)
دریافت: 1402/5/15 | پذیرش: 1402/6/25 | انتشار الکترونیک پیش از انتشار نهایی: 1402/6/28 | انتشار: 1402/6/30

فهرست منابع

1. [ ] Fama EF. The behavior of stock-market prices. The journal of Business. 1965 Jan 1;38(1):34-105. [DOI:10.1086/294743]

2. [ ] Fama EF. Efficient capital markets: A review of theory and empirical work. The journal of Finance. 1970 May 1;25(2):383-417. [DOI:10.1111/j.1540-6261.1970.tb00518.x]

3. [ ] Samuelson PA. Proof that properly anticipated prices fluctuate randomly. InThe world scientific handbook of futures markets 2016 (pp. 25-38). [DOI:10.1142/9789814566926_0002]

4. [ ] خالوزاده ح, خاکی صدیق ع. ارزیابی روشهای پیش بینی پذیری قیمت سهام و تعیین میزان قابلیت پیش بینی در بازار بورس تهران. پژوهش های مدیریت در ایران. Sep 29;7(3).‎ 2003

5. [ ] Atsalakis GS, Valavanis KP. Surveying stock market forecasting techniques-Part I: Conventional methods. Journal of Computational Optimization in Economics and Finance. 2010;2(1):45-92.

6. [ ] Bhattacharjee I, Bhattacharja P. Stock price prediction: a comparative study between traditional statistical approach and machine learning approach. In2019 4th international conference on electrical information and communication technology (EICT) 2019 Dec 20 (pp. 1-6). IEEE. [DOI:10.1109/EICT48899.2019.9068850]

7. [ ] Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN computer science. 2021 May;2(3):160. [DOI:10.1007/s42979-021-00592-x]

8. [ ] White H. Economic prediction using neural networks: The case of IBM daily stock returns. InICNN 1988 Jul 24 (Vol. 2, pp. 451-458). [DOI:10.1109/ICNN.1988.23959]

9. [ ] Hung CC, Chen YJ, Guo SJ, Hsu FC. Predicting the price movement from candlestick charts: a CNN-based approach. International Journal of Ad Hoc and Ubiquitous Computing. 2020;34(2):111-20. [DOI:10.1504/IJAHUC.2020.107821]

10. [ ] Sari R, Kusnanto K, Aswindo M. Determinants of Stock Investment Decision Making: A Study on Investors in Indonesia. Golden Ratio of Finance Management. 2022 Sep 30;2(2):120-31. [DOI:10.52970/grfm.v2i2.174]

11. [ ] Fjellström C. Long short-term memory neural network for financial time series. In2022 IEEE International Conference on Big Data (Big Data) 2022 Dec 17 (pp. 3496-3504). IEEE. [DOI:10.1109/BigData55660.2022.10020784]

12. [ ] Hoseinzade E, Haratizadeh S, Khoeini A. U-cnnpred: A universal cnn-based predictor for stock markets. arXiv preprint arXiv:1911.12540. 2019 Nov 28.

13. [ ] Vargas MR, Dos Anjos CE, Bichara GL, Evsukoff AG. Deep leaming for stock market prediction using technical indicators and financial news articles. In2018 international joint conference on neural networks (IJCNN) 2018 Jul 8 (pp. 1-8). IEEE. [DOI:10.1109/IJCNN.2018.8489208]

14. [ ] Zhou Z, Gao M, Liu Q, Xiao H. Forecasting stock price movements with multiple data sources: Evidence from stock market in China. Physica A: Statistical Mechanics and its Applications. 2020 Mar 15;542:123389. [DOI:10.1016/j.physa.2019.123389]

15. [ ] Ding G, Qin L. Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics. 2020 Jun;11:1307-17. [DOI:10.1007/s13042-019-01041-1]

16. [ ] Sachdeva A, Jethwani G, Manjunath C, Balamurugan M, Krishna AV. An effective time series analysis for equity market prediction using deep learning model. In2019 International Conference on Data Science and Communication (IconDSC) 2019 Mar 1 (pp. 1-5). IEEE. [DOI:10.1109/IconDSC.2019.8817035]

17. [ ] Hossain MA, Karim R, Thulasiram R, Bruce ND, Wang Y. Hybrid deep learning model for stock price prediction. In2018 ieee symposium series on computational intelligence (ssci) 2018 Nov 18 (pp. 1837-1844). IEEE. [DOI:10.1109/SSCI.2018.8628641]

18. [ ] Kalyoncu S, Jamil A, Karataş E, Rasheed J, Djeddi C. Stock market value prediction using deep learning. Data Science and Applications. 2020 Dec 31;3(2):10-4.

19. [ ] Siami-Namini S, Tavakoli N, Namin AS. A comparative analysis of forecasting financial time series using arima, lstm, and bilstm. arXiv preprint arXiv:1911.09512. 2019 Nov 21.

20. [ ] Agrawal M, Shukla PK, Nair R, Nayyar A, Masud M. Stock Prediction Based on Technical Indicators Using Deep Learning Model. Computers, Materials & Continua. 2022 Jan 1;70(1). [DOI:10.32604/cmc.2022.014637]

21. [ ] Fazeli A, Houghten S. Deep learning for the prediction of stock market trends. In2019 IEEE International Conference on Big Data (Big Data) 2019 Dec 9 (pp. 5513-5521). IEEE. [DOI:10.1109/BigData47090.2019.9005523]

22. [ ] Parray IR, Khurana SS, Kumar M, Altalbe AA. Time series data analysis of stock price movement using machine learning techniques. Soft Computing. 2020 Nov;24:16509-17. [DOI:10.1007/s00500-020-04957-x]

23. [ ] Hoseinzade E, Haratizadeh S. CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications. 2019 Sep 1;129:273-85. [DOI:10.1016/j.eswa.2019.03.029]

24. [ ] Tsai CF, Quan ZY. Stock prediction by searching for similarities in candlestick charts. ACM Transactions on Management Information Systems (TMIS). 2014 Jul 1;5(2):1-21. [DOI:10.1145/2591672]

25. [ ] Guo SJ, Hsu FC, Hung CC. Deep candlestick predictor: a framework toward forecasting the price movement from candlestick charts. In2018 9th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) 2018 Dec 26 (pp. 219-226). IEEE. [DOI:10.1109/PAAP.2018.00044]

26. [ ] Sachdeva A, Jethwani G, Manjunath C, Balamurugan M, Krishna AV. An effective time series analysis for equity market prediction using deep learning model. In2019 International Conference on Data Science and Communication (IconDSC) 2019 Mar 1 (pp. 1-5). IEEE. [DOI:10.1109/IconDSC.2019.8817035]

27. [ ] Lee J, Kim R, Koh Y, Kang J. Global stock market prediction based on stock chart images using deep Q-network. IEEE Access. 2019 Nov 14;7:167260-77. [DOI:10.1109/ACCESS.2019.2953542]

28. [ ] Nofsinger JR. The impact of public information on investors. Journal of Banking & Finance. 2001 Jul 1;25(7):1339-66. [DOI:10.1016/S0378-4266(00)00133-3]

29. [ ] Ding X, Zhang Y, Liu T, Duan J. Using structured events to predict stock price movement: An empirical investigation. InProceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) 2014 Oct (pp. 1415-1425). [DOI:10.3115/v1/D14-1148]

30. [ ] Liu Y, Zeng Q, Ordieres Meré J, Yang H. Anticipating stock market of the renowned companies: A knowledge graph approach. Complexity. 2019 Aug 7;2019. [DOI:10.1155/2019/9202457]

31. [ ] Tang J, Chen X. Stock market prediction based on historic prices and news titles. InProceedings of the 2018 international conference on machine learning technologies 2018 May 19 (pp. 29-34). [DOI:10.1145/3231884.3231887]

32. [ ] Yun H, Sim G, Seok J. Stock prices prediction using the title of newspaper articles with korean natural language processing. In2019 international conference on artificial intelligence in information and communication (ICAIIC) 2019 Feb 11 (pp. 019-021). IEEE. [DOI:10.1109/ICAIIC.2019.8668996]

33. [ ] Ashtiani MN, Raahmei B. News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review. Expert Systems with Applications. 2023 Jan 9:119509. [DOI:10.1016/j.eswa.2023.119509]

34. [ ] TajMazinani M, Hassani H, Raei R. A comprehensive review of stock price prediction using text mining. Advances in Decision Sciences. 2022;26(2):116-52. [DOI:10.47654/v26y2022i2p116-152]

35. [ ] Lee CS, Cheang PY, Moslehpour M. Predictive analytics in business analytics: decision tree. Advances in Decision Sciences. 2022 Mar 1;26(1):1-29. [DOI:10.47654/v26y2022i1p1-29]

36. [ ] Zhang X, Shi J, Wang D, Fang B. Exploiting investors social network for stock prediction in China's market. Journal of computational science. 2018 Sep 1;28:294-303. [DOI:10.1016/j.jocs.2017.10.013]

37. [ ] Swathi T, Kasiviswanath N, Rao AA. An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Applied Intelligence. 2022 Sep;52(12):13675-88. [DOI:10.1007/s10489-022-03175-2]

38. [ ] Qiu Y, Song Z, Chen Z. Short-term stock trends prediction based on sentiment analysis and machine learning. Soft Computing. 2022 Mar;26(5):2209-24. [DOI:10.1007/s00500-021-06602-7]

39. [ ] Carosia AE, Coelho GP, Silva AE. Analyzing the Brazilian financial market through Portuguese sentiment analysis in social media. Applied Artificial Intelligence. 2020 Jan 2;34(1):1-9. [DOI:10.1080/08839514.2019.1673037]

40. [ ] Ghadimpour M, Ebrahimi SB. Forecasting Financial Time Series Using Deep Learning Networks: Evidence from Long-Short Term Memory and Gated Recurrent Unit. Iranian Journal of Finance. 2022 Oct 1;6(4):81-94. [DOI:10.30699/ijf.2022.313164.1286]

41. [ ] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in neural information processing systems. 2017;30.

42. [ ] Ding X, Zhang Y, Liu T, Duan J. Using structured events to predict stock price movement: An empirical investigation. InProceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) 2014 Oct (pp. 1415-1425). [DOI:10.3115/v1/D14-1148]

43. [ ] Ding X, Zhang Y, Liu T, Duan J. Deep learning for event-driven stock prediction. InTwenty-fourth international joint conference on artificial intelligence 2015 Jun 25.

44. [ ] Oncharoen P, Vateekul P. Deep learning using risk-reward function for stock market prediction. InProceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence 2018 Dec 8 (pp. 556-561). [DOI:10.1145/3297156.3297173]

45. [ ] Tang J, Chen X. Stock market prediction based on historic prices and news titles. InProceedings of the 2018 international conference on machine learning technologies 2018 May 19 (pp. 29-34). [DOI:10.1145/3231884.3231887]

46. [ ] Lin WC, Tsai CF, Chen H. Factors affecting text mining based stock prediction: Text feature representations, machine learning models, and news platforms. Applied Soft Computing. 2022 Nov 1;130:109673. [DOI:10.1016/j.asoc.2022.109673]

47. [ ] Wu S, Liu Y, Zou Z, Weng TH. S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis. Connection Science. 2022 Dec 31;34(1):44-62. [DOI:10.1080/09540091.2021.1940101]

48. [ ] Jin Z, Yang Y, Liu Y. Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications. 2020 Jul;32:9713-29. [DOI:10.1007/s00521-019-04504-2]

49. [ ] Zhai Y, Hsu A, Halgamuge SK. Combining news and technical indicators in daily stock price trends prediction. InAdvances in Neural Networks-ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part III 4 2007 (pp. 1087-1096). Springer Berlin Heidelberg. [DOI:10.1007/978-3-540-72395-0_132]

50. [ ] Vargas MR, De Lima BS, Evsukoff AG. Deep learning for stock market prediction from financial news articles. In2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA) 2017 Jun 26 (pp. 60-65). IEEE. [DOI:10.1109/CIVEMSA.2017.7995302]

51. [ ] Ruhal R, Prashar EV. A Comparative Study Of Statistical Methods And Machine Learning Approaches For Stock Price Prediction. Journal of Emerging Technologies and Innovative Research (JETIR). 2023.

52. [ ] Shahrokhi SR, Khaloozadeh H, Momeni H. Analyzing the Advantage of Combination of Density Forecasts in Tehran Stock Exchange. International Journal of Industrial Electronics Control and Optimization. 2021 Jan 1;4(1):23-31.

53. [ ] Atsalakis GS, Valavanis KP. Surveying stock market forecasting techniques-Part I: Conventional methods. Journal of Computational Optimization in Economics and Finance. 2010;2(1):45-92.

54. [ ] Siami-Namini S, Tavakoli N, Namin AS. A comparison of ARIMA and LSTM in forecasting time series. In2018 17th IEEE international conference on machine learning and applications (ICMLA) 2018 Dec 17 (pp. 1394-1401). IEEE. [DOI:10.1109/ICMLA.2018.00227]

55. [ ] Araújo RD, Nedjah N, Oliveira AL, Meira SR. A deep increasing-decreasing-linear neural network for financial time series prediction. Neurocomputing. 2019 Jun 28;347:59-81. [DOI:10.1016/j.neucom.2019.03.017]

56. [ ] Mondal P, Shit L, Goswami S. Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications. 2014 Apr 1;4(2):13. [DOI:10.5121/ijcsea.2014.4202]

57. [ ] Adebiyi AA, Adewumi AO, Ayo CK. Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics. 2014 Mar 5;2014. [DOI:10.1155/2014/614342]

58. [ ] McDonald S, Coleman S, McGinnity TM, Li Y, Belatreche A. A comparison of forecasting approaches for capital markets. In2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) 2014 Mar 27 (pp. 32-39). IEEE. [DOI:10.1109/CIFEr.2014.6924051]

59. [ ] SINAYI, HA, S. MORTAZAVI, and Y. TAYMOORIASL. "THE PREDICTION TEHRAN EXCHANGE PRICE INDEX (TEPLX) USING ARTIFICIAL NEURAL NETWORKS (ANN)." (2005): 59-83.

60. [ ] Siami-Namini S, Namin AS. Forecasting economics and financial time series: ARIMA vs. LSTM. arXiv preprint arXiv:1803.06386. 2018 Mar 16.

61. [ ] Kumbure MM, Lohrmann C, Luukka P, Porras J. Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications. 2022 Jul 1;197:116659. [DOI:10.1016/j.eswa.2022.116659]

62. [ ] Rajihy Y, Nermend K, Alsakaa A. Back-propagation artificial neural networks in stock market forecasting. an application to the Warsaw stock exchange WIG20. Aestimatio: The IEB International Journal of Finance. 2017(15):88-99.

63. [ ] De Oliveira FA, Nobre CN, Zárate LE. Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index-Case study of PETR4, Petrobras, Brazil. Expert systems with applications. 2013 Dec 15;40(18):7596-606. [DOI:10.1016/j.eswa.2013.06.071]

64. [ ] Niaki ST, Hoseinzade S. Forecasting S&P 500 index using artificial neural networks and design of experiments. Journal of Industrial Engineering International. 2013 Dec;9:1-9. [DOI:10.1186/2251-712X-9-1]

65. [ ] Madge S, Bhatt S. Predicting stock price direction using support vector machines. Independent work report spring. 2015;45.

66. [ ] Kurani A, Doshi P, Vakharia A, Shah M. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science. 2023 Feb;10(1):183-208. [DOI:10.1007/s40745-021-00344-x]

67. [ ] Zhang N, Lin A, Shang P. Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A: Statistical Mechanics and Its Applications. 2017 Jul 1;477:161-73. [DOI:10.1016/j.physa.2017.02.072]

68. [ ] Yunneng Q. A new stock price prediction model based on improved KNN. In2020 7th International Conference on Information Science and Control Engineering (ICISCE) 2020 Dec 18 (pp. 77-80). IEEE. [DOI:10.1109/ICISCE50968.2020.00026]

69. [ ] Khaidem L, Saha S, Dey SR. Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003. 2016 Apr 29.

70. [ ] Yin L, Li B, Li P, Zhang R. Research on stock trend prediction method based on optimized random forest. CAAI Transactions on Intelligence Technology. 2023 Mar;8(1):274-84. [DOI:10.1049/cit2.12067]

71. [ ] Vaiz JS, Ramaswami M. A study on technical indicators in stock price movement prediction using decision tree algorithms. American Journal of Engineering Research (AJER). 2016;5(12):207-12.

72. [ ] Alimohamadi AM, Abbasimehr MH, Javaheri A. Prediction of stock return using financial ratios: A decision tree approach. Financial Management Strategy. 2015 Dec 22;3(4):125-46.

73. [ ] Jiang W. Applications of deep learning in stock /market prediction: recent progress. Expert Systems with Applications. 2021 Dec 1;184:115537. [DOI:10.1016/j.eswa.2021.115537]

74. [ ] LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44. [DOI:10.1038/nature14539]

75. [ ] Chen JH, Hao YH, Wang H, Wang T, Zheng DW. Futures price prediction modeling and decision-making based on DBN deep learning. Intelligent Data Analysis. 2019 Jan 1;23(S1):53-65. [DOI:10.3233/IDA-192742]

76. [ ] Thakkar A, Chaudhari K. A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert Systems with Applications. 2021 Sep 1;177:114800. [DOI:10.1016/j.eswa.2021.114800]

77. [ ] Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation. 1997 Nov 15;9(8):1735-80. [DOI:10.1162/neco.1997.9.8.1735]

78. [ ] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks. 1994 Mar;5(2):157-66. [DOI:10.1109/72.279181]

79. [ ] Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. 2014 Jun 3. [DOI:10.3115/v1/D14-1179]

80. [ ] Baek Y, Kim HY. ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications. 2018 Dec 15;113:457-80. [DOI:10.1016/j.eswa.2018.07.019]

81. [ ] Sanboon T, Keatruangkamala K, Jaiyen S. A deep learning model for predicting buy and sell recommendations in stock exchange of thailand using long short-term memory. In2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) 2019 Feb 23 (pp. 757-760). IEEE. [DOI:10.1109/CCOMS.2019.8821776]

82. [ ] Nikou M, Mansourfar G, Bagherzadeh J. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management. 2019 Oct;26(4):164-74. [DOI:10.1002/isaf.1459]

83. [ ] Zhang J, Rong W, Liang Q, Sun H, Xiong Z. Data augmentation based stock trend prediction using self-organising map. InNeural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24 2017 (pp. 903-912). Springer International Publishing. [DOI:10.1007/978-3-319-70096-0_92]

84. [ ] Liu J, Lu Z, Du W. Combining enterprise knowledge graph and news sentiment analysis for stock price prediction.

85. [ ] Taye MM. Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions. Computation. 2023 Mar 6;11(3):52. [DOI:10.3390/computation11030052]

86. [ ] Brim A, Flann NS. Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images. Plos one. 2022 Feb 18;17(2):e0263181. [DOI:10.1371/journal.pone.0263181]

87. [ ] Andriyanto A, Wibowo A, Abidin NZ. Sectoral stock prediction using convolutional neural networks with candlestick patterns as input images. International Journal. 2020 Jun;8(6). [DOI:10.30534/ijeter/2020/07862020]

88. [ ] Liao S, Wang J, Yu R, Sato K, Cheng Z. CNN for situations understanding based on sentiment analysis of twitter data. Procedia computer science. 2017 Jan 1;111:376-81. [DOI:10.1016/j.procs.2017.06.037]

89. [ ] Sun L, Xu W, Liu J. Two-channel attention mechanism fusion model of stock price prediction based on cnn-lstm. Transactions on Asian and Low-Resource Language Information Processing. 2021 Jul 22;20(5):1-2. [DOI:10.1145/3453693]

90. [ ] Chen W, Yeo CK, Lau CT, Lee BS. Leveraging social media news to predict stock index movement using RNN-boost. Data & Knowledge Engineering. 2018 Nov 1;118:14-24. [DOI:10.1016/j.datak.2018.08.003]

91. [ ] Zhu Y. Stock price prediction using the RNN model. InJournal of Physics: Conference Series 2020 Oct 1 (Vol. 1650, No. 3, p. 032103). IOP Publishing. [DOI:10.1088/1742-6596/1650/3/032103]

92. [ ] Ren Y, Mao J, Liu Y, Li Y. A novel dbn model for time series forecasting. IAENG International Journal of Computer Science. 2017 Mar 1;44(1):79-86.

93. [ ] Liang Q, Rong W, Zhang J, Liu J, Xiong Z. Restricted Boltzmann machine based stock market trend prediction. In2017 International Joint Conference on Neural Networks (IJCNN) 2017 May 14 (pp. 1380-1387). IEEE. [DOI:10.1109/IJCNN.2017.7966014]

94. [ ] Ramadhan A, Palupi I, Wahyudi BA. Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market. Journal of Computer System and Informatics (JoSYC). 2022 Sep 3;3(4):339-47. [DOI:10.47065/josyc.v3i4.2133]

95. [ ] Lu W, Li J, Li Y, Sun A, Wang J. A CNN-LSTM-based model to forecast stock prices. Complexity. 2020 Nov 23;2020:1-0. [DOI:10.1155/2020/6622927]

96. [ ] Hossain MA, Karim R, Thulasiram R, Bruce ND, Wang Y. Hybrid deep learning model for stock price prediction. In2018 ieee symposium series on computational intelligence (ssci) 2018 Nov 18 (pp. 1837-1844). IEEE. [DOI:10.1109/SSCI.2018.8628641]

97. [ ] Ho TT, Huang Y. Stock price movement prediction using sentiment analysis and CandleStick chart representation. Sensors. 2021 Nov 29;21(23):7957. [DOI:10.3390/s21237957]

98. [ ] Kim T, Kim HY. Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PloS one. 2019 Feb 15;14(2):e0212320. [DOI:10.1371/journal.pone.0212320]

99. [ ] Elliott G, Timmermann A. Economic forecasting. Journal of Economic Literature. 2008 Mar 1;46(1):3-56. [DOI:10.1257/jel.46.1.3]

100. [ ] Zhang, D., Jiang, Q., & Li, X. (2004). Application of neural networks in financial data mining. In International Conference on Computational Intelligence (pp. 392-395).

101. [ ] Hayward, S. (2004, December). Evolutionary artificial neural network optimisation in financial engineering. In Fourth International Conference on Hybrid Intelligent Systems (HIS'04) (pp. 210-215). IEEE.

102. [ ] Elliott G, Timmermann A, Komunjer I. Estimation and testing of forecast rationality under flexible loss. The Review of Economic Studies. 2005 Oct 1;72(4):1107-25. [DOI:10.1111/0034-6527.00363]

103. [ ] Oncharoen P, Vateekul P. Deep learning for stock market prediction using event embedding and technical indicators. In2018 5th international conference on advanced informatics: concept theory and applications (ICAICTA) 2018 Aug 14 (pp. 19-24). IEEE. [DOI:10.1109/ICAICTA.2018.8541310]

104. [ ] Song Y, Lee JW, Lee J. A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction. Applied Intelligence. 2019 Mar 15;49:897-911. [DOI:10.1007/s10489-018-1308-x]

105. [ ] Matsubara T, Akita R, Uehara K. Stock price prediction by deep neural generative model of news articles. IEICE TRANSACTIONS on Information and Systems. 2018 Apr 1;101(4):901-8.no. 4, pp. 901_908, 2018. [DOI:10.1587/transinf.2016IIP0016]

106. [ ] Rajabi M, Khaloozadeh H. Long-term prediction in Tehran stock market using a new architecture of Deep neural networks. AUT Journal of Modeling and Simulation. 2020 Dec 1;52(2):179-88.

107. [ ] Eini EJ, Khaloozadeh H. Tail variance for generalized skew-elliptical distributions. Communications in Statistics-Theory and Methods. 2022 Jan 17;51(2):519-36. [DOI:10.1080/03610926.2020.1751853]

ارسال پیام به نویسنده مسئول

بازنشر اطلاعات
	این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این وب سایت متعلق به مجله کنترل می باشد.

طراحی و برنامه نویسی : یکتاوب افزار شرق

Designed & Developed by : Yektaweb

پایگاه های مرتبط

کلمات کلیدی