CoChat
Sign Up
Cited by 6Recommender Systems and TechniquesDecision Support System ApplicationsStock Market Forecasting Methods

A model for investment type recommender system based on the potential investors based on investors and experts feedback using ANFIS and MNN

Open access
Asemi, Asefeh, Asemi, Adeleh, Ko, Andrea·2024·Journal of Big Data
DOI 10.1186/s40537-024-00965-y
Open this paper in CoChat — free accountOpen PDF

Abstract

This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40537-024-00965-y

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ANFIS is suitable for tasks that demand a balance between prediction performance and model explainability.

    “These studies underscore ANFIS's suitability for tasks that demand a balance between prediction performance and model explainability.”

    From GPT-based lifelong learning and ANFIS-driven reply memory ratio prediction for aspect-based sentiment analysis (2025) · cites this paper as [38]

    The ANFIS training and testing section reports reasonably good predictive performance (F1-score 0.6667) and describes 18 fuzzy rules used for investment recommendations. The Discussion likewise says the system performs well in that setting. Neither section establishes that ANFIS balances prediction performance with model explainability or is suitable for tasks requiring both; the paper’s finding is also specific to investment recommendations. No page numbers are available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 21

  1. Hervella ÁS, Rouco J, Novo J, Ortega M. Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis. Expert Syst Appl. 2021;185: 115598. https:// doi. org/ 10. 1016/j. eswa. 2021. 115598. · DOI
  2. Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993;23(3):665-85. https:// doi. org/ 10. 1109/ 21. 256541. · DOI
  3. Asemi A, Ko A. A novel combined business recommender system model using customer investment service feed- back. In: 34th Bled EConference Digital Support from Crisis to Progressive Change: Conference Proceedings, 2021; pp. 223-237. https:// doi. org/ 10. 18690/ 978-961-286-485-9. 17. · DOI
  4. Abraham R, Samad ME, Bakhach AM, El-Chaarani H, Sardouk A, Nemar SE, Jaber D. Forecasting a stock trend using genetic algorithm and random forest. J Risk Financ Manage. 2022;15(5):5. https:// doi. org/ 10. 3390/ jrfm1 50501 88. · DOI
  5. Aggarwal A, Hess O, Lockman JL, Smith L, Stevens M, Bruce J, Caruso T. Anesthesiologists with advanced degrees in education: qualitative study of a changing paradigm. JMIR Med Educ. 2022;8(2):e38050. https:// doi. org/ 10. 2196/ 38050. · DOI
  6. Benkraiem R, Gaaya S, Lakhal F, Lakhal N. Economic policy uncertainty, investor protection, and the value of excess cash: a cross-country comparison. Financ Res Lett. 2023;52: 103572. https:// doi. org/ 10. 1016/j. frl. 2022. 103572. · DOI
  7. Aksar M, Hassan S, Kayani MB, Khan S, Ahmed T. Cash holding and investment efficiency nexus for financially dis- tressed firms: the moderating role of corporate governance. Manage Sci Lett. 2022;12(1):67-74. https:// doi. org/ 10. 5267/j. msl. 2021.7. 001. · DOI
  8. AL-Khafaji AAK, Mustangs RF, Alsaalim FHAJ. The role of creative accounting in increasing the marketing of shares and their profits in the Iraqi stock exchange. Period Eng Nat Sci. 2022;10(2):323-35. https:// doi. org/ 10. 21533/ pen. v10i2. 2886. · DOI
  9. Andajani E. Understanding customer experience management in retailing. Proc Soc Behav Sci. 2015;211:629-33. https:// doi. org/ 10. 1016/j. sbspro. 2015. 11. 082. · DOI
  10. Chen L, Yan D, Wang F. User perception of sentiment-integrated critiquing in recommender systems. Int J Hum Comput Stud. 2019;121:4-20. https:// doi. org/ 10. 1016/j. ijhcs. 2017. 09. 005. · DOI
  11. Chen X, Ye S, Huang C. Cluster-based mutual fund classification and price prediction using machine learning for robo-advisors. Comput Intell Neurosci. 2021;2021: e4984265. https:// doi. org/ 10. 1155/ 2021/ 49842 65. · DOI
  12. Chatterjee I, Gwan J, Kim YJ, Lee MS, Cho M. An NLP and LSTM based stock prediction and recommender system for KOSDAQ and KOSPI. In: Singh M, Kang DK, Lee JH, Tiwary US, Singh D, Chung WY, editors. Intelligent human computer interaction, Pt I, vol. 12615. Cham: Springer International Publishing; 2021. p. 403-13. https:// doi. org/ 10. 1007/ 978-3-030-68449-5_ 40. · DOI
  13. Asemi A, Salim SSB, Shahamiri SR, Asemi A, Houshangi N. Adaptive neuro-fuzzy inference system for evaluating dysarthric automatic speech recognition (ASR) systems. Soft Comput. 2019;23:3529-44. https:// doi. org/ 10. 1007/ s00500-018-3013-4. · DOI
  14. Birim ŞÖ, Sönmez FE, Liman YS. Estimating return rate of blockchain financial product by ANFIS-PSO method. In: Lecture notes in networks and systems, 504 LNNS, pp. 802-809. Scopus. 2022. https:// doi. org/ 10. 1007/ 978-3-031- 09173-5_ 92. · DOI
  15. D'lima N, Khan S. FOREX rate prediction using ANN and ANFIS Conference. https:// www. seman ticsc holar. org/ paper/ FOREX-rate-predi ction-using-ANN-and-ANFIS-D% 27lima-Khan/ 6817d 1cc9f 7ac35 cf284 04f0e 17e35 8b54f a16d1. 2016.
  16. Ezhilarasi TP, Sashi Rekha K. Secure recommendation application for environment crop using big data analytics with fuzzy framework. J Green Eng. 2020;10(4):1799-815.
  17. Asemi A, Asemi A, Ko A. Investment recommender system model based on the potential investors' key decision factors. Big Data. 2023. https:// doi. org/ 10. 1089/ big. 2022. 0302. · DOI
  18. Asemi A, Asemi A, Ko A. A systematic review and propose an ANFIS-based investment type recommender system using investors' demographic. In: A Hybrid Conference 8th International Congress on Information and Communica- tion Technology ICICT 2023, London, UK, 20-23. https:// www. resea rchga te. net/ publi cation/ 36901 9468_ Syste matic_ Review_ and_ Propo se_ an_ ANFIS-Based_ Inves tment_ Type_ Recom mender_ System_ using_ Inves tors'_ Demog raphic. 2023b.
  19. Huang Y, Capretz LF, Ho D. Neural network models for stock selection based on fundamental analysis. IEEE Can Conf Electr Comput Eng (CCECE). 2019;2019:1-4. https:// doi. org/ 10. 1109/ CCECE. 2019. 88615 50. · DOI
  20. Kovács T, Ko A, Asemi A. Exploration of the investment patterns of potential retail banking customers using two- stage cluster analysis. J Big Data, 2021; 8(1). https:// doi. org/ 10. 1186/ s40537-021-00529-4. · DOI
  21. Wang Y, Zhang M. Simulation analysis of regional real estate investment risk based on system dynamics. E3S Web Conf. 2021;251:01070. https:// doi. org/ 10. 1051/ e3sco nf/ 20212 51010 70. · DOI

Cited by

Sorted by citations

Authors
Asemi, A., Asemi, A., & Ko, A.
Affiliations
Corvinus University of Budapest (HU)
Corresponding author
  • Asemi, Asefeh
  • Asemi, Adeleh
ORCID
Venue
Journal of Big Data
Publisher
Springer Science and Business Media LLC
Published
2024-09-12
Volume
11
Issue
1
ISSN
2196-1115 (ISSN-L)
DOI
10.1186/s40537-024-00965-y
OpenAlex
W4402495009
Cited by
6
References
2
Citations by year
2026: 1 · 2025: 5
FWCI
2.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal Of Big Data (publishedVersion, cc-by-nc-nd)
  • Corvinus Research Archive (Corvinus University of Budapest) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Keywords
adaptive neuro-fuzzy inference system, multimodal neural networks, expert feedback, investor preferences, investment objectives, fuzzy rules

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review