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Cited by 14Decision Support System ApplicationsStock Market Forecasting MethodsFuzzy Logic and Control Systems

Unveiling the impact of managerial traits on investor decision prediction: ANFIS approach

Open access
Asemi, Asefeh, Asemi, Adeleh, Ko, Andrea·2023·Soft Computing
DOI 10.1007/s00500-023-08102-2

Published version of a preprint

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Abstract

Investment decisions are influenced by various factors, including personal characteristics and managerial issues. In this research, we aimed to investigate the impact of managerial traits on investment decisions by using adaptive neuro-fuzzy inference system (ANFIS) to develop a personalized investment recommendation system. We collected data from potential investors through a survey, which included questions on investment-types, investment habits, and managerial traits. The survey data were used to create an ANFIS model, which is a hybrid model that combines the strengths of both artificial neural networks and fuzzy logic systems. The ANFIS model was trained using 1542 survey data pairs, and the model's performance was evaluated using a validation set. The results of the ANFIS model showed that the model had a minimal training root mean square error of 0.837341. The ANFIS model was able to effectively capture the relationship between managerial traits and investment decisions and was able to make personalized investment recommendations based on the input data. The results of this research provide valuable insights into the impact of managerial traits on investment decisions and demonstrate the potential of ANFIS in developing personalized investment recommendation systems. In conclusion, this research aimed to investigate the impact of managerial traits on investment decisions using ANFIS. The results of this study demonstrate the potential of ANFIS to personalize investment recommendations based on the input data. This research can be used as a foundation for future research in the field of investment recommendations and can be helpful to investors to take their decision-making.

AI snapshot

Written from the paper’s full text.

TL;DR

The paper develops an ANFIS-based personalized investment recommendation system using survey data from 1,542 potential investors, combining K-means clustering of investment types with managerial-trait inputs. It reports that the ANFIS model captured relationships between managerial traits and investment-type recommendations with a minimal training RMSE of 0.837341 and an average testing error of 0.83732.

Key findings

  • K-means grouped investment types into three clusters in JMP: 592, 406, and 340 respondents; clusters differed by stock-market participation, monitoring of stock performance, and government-bond investment.
  • The ANFIS model used nine managerial-trait inputs—planning, stress, pace, influential, daily schedule, strategy, attachment, satisfaction, and planning time—and one output representing the recommended investment type.
  • The model was trained on 1,542 data pairs with 2,880 fuzzy rules and 3 epochs, achieving a minimal training RMSE of 0.837341 and an average testing error of 0.83732.
  • The paper claims high accuracy and precision in providing customized investment recommendations and visualizes nonlinear input–output relationships via 3D surface graphs, but it does not report accuracy, precision, recall, or F1-score values.

Methods

  • Descriptive study using 1,542 responses to an online questionnaire collected in 2019 in Hungarian as part of the 2018-1.3.1-VKE-2018-00007 project.
  • Data were analyzed in MATLAB and JMP: K-means clustered investment types, clusters were compared and combined using visual comparison and expert knowledge, and ANFIS in MATLAB used managerial traits as inputs and clustered investment type as output.
  • ANFIS training and validation involved splitting data, using 3 epochs, `genfis1`, `anfis`, and `evalfis`; performance was evaluated with RMSE and other metrics.

Limitations

  • Stated: the sample size is not large enough to generalize to the entire population of potential investors; self-reported data may be subject to bias and inaccuracy; the model is based on a specific sample and may not be generalizable; it does not consider other factors such as economic conditions and market trends.
  • Evident: the text inconsistently describes the membership functions—some inputs have varying numbers of MFs, while later it says each input has three membership functions—and once states the trained network has four inputs, making the architecture reporting unclear.
  • Evident: although accuracy, precision, recall, and F1-score are mentioned as evaluation metrics, only RMSE values are reported, and no baseline comparison results are given.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s00500-023-08102-2

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 4 cited sources checked · 0 supported · 0 flagged

  • Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 16

    As the level of satisfaction increases, the recommended investment-type shifts toward more high-risk, high-reward options, such as stocks and mutual funds.

    “As the level of satisfaction increases, the recommended investment-type shifts toward more high-risk, high-reward options, such as stocks and mutual funds, represented by a peak on the graph.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 16

    As the planning time increases, the recommended investment-type shifts toward more long-term options, such as government bonds and real estate.

    “Similarly, as the planning time increases, the recommended investment-type shifts toward more long-term options, such as government bonds and real estate, represented by a peak on the graph.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § 6 Experimental results · p. 7

    K-means clustering grouped the data into three distinct clusters based on the types of investments used by potential investors.

    “The results of the experiment showed that the clustering process was successful in grouping the data into three distinct clusters based on the types of investments used by potential investors.”
    • Also stated in: § 6.1 Managerial traits ANFIS output · p. 7 · plain
      “The results in JMP and MATLAB show that the data are grouped into three clusters, each representing a distinct group of potential investors with specific investment habits and behaviors (Fig. 2).”
    Quote in the paperSubstantial claim
  • Findingown data
    § Abstract · p. 1

    The ANFIS model had a minimal training root mean square error of 0.837341.

    “The results of the ANFIS model showed that the model had a minimal training root mean square error of 0.837341.”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 15

    Certain combinations of satisfaction and influence levels result in the same recommended investment-type.

    “The graph also shows that certain combinations of satisfaction and influence levels result in the same recommended investment-type.”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 30
  • May combine two claims. 20

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § 6.3 Proposing the managerial traits ANFIS · p. 11

    The graph demonstrates the effectiveness of the ANFIS model in providing customized investment recommendations based on potential investors' managerial traits, risk tolerance, and investment goals.

    “The graph demonstrates the effectiveness of the ANFIS model in providing customized investment recommendations based on the potential investors' managerial traits, risk tolerance, and investment goals.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Managerial traits of potential investors can affect investment-types.

  • May combine two claims.

    Findingreasoning
    § 7 Discussion · p. 19

    The model was able to accurately predict the investment-type recommendations based on the inputs provided by the potential investors, and the ANFIS was able to generate accurate recommendations for investment-types based on the input data.

    “The model was able to accurately predict the investmenttype recommendations based on the inputs provided by the potential investors.”
    • Also stated in: § 7 Discussion · p. 19 · plain
      “the ANFIS was able to generate accurate recommendations for investment-types based on the input data.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given
    § 7 Discussion · p. 19

    The clustering results were consistent across both MATLAB and JMP software.

    “The clustering results were consistent across both MATLAB and JMP software”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 7 Discussion · p. 19

    The first cluster represents individuals who are not currently invested in the stock market and have not been monitoring stock performance.

    “the first cluster represents individuals who are not currently invested in the stock market and have not been monitoring stock performance”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 7 Discussion · p. 19

    The second cluster represents individuals who are currently invested in the stock market, regularly monitor stock performance, and have investments in government bonds.

    “the second cluster represents individuals who are currently invested in the stock market, regularly monitor stock performance, and have investments in government bonds”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 15

    The relationship between satisfaction and influence on investment-type is nonlinear and monolithic.

    “The surface of the graph is nonlinear and monolithic, indicating that the relationship between satisfaction and influence on investment-type is not a simple linear relationship because it considers several factors that influence the investment decision.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given
    § 6.3 Proposing the managerial traits ANFIS · p. 11

    The training process employed a hybrid method with a total of 3 epochs, resulting in an error rate of approximately 0.84 for each epoch.

    “The training process employed a hybrid method with a total of 3 epochs, resulting in an error rate of approximately 0.84 for each epoch.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Findingnone given
    § 3 Literature review · p. 4

    In the proposed system, ANFIS and K-means are used together for the investment recommendations approach, while existing systems use ANFIS alone or with other clustering techniques.

    “In the proposed system, ANFIS and K-means are used together for the investment recommendations approach, while existing systems use ANFIS alone or with other clustering techniques.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 13

    The system's output is represented in 3D surface graphs that depict the nonlinear relationship between different input pairs and the resulting investment-type recommendations.

    “The system's output is represented in a series of three-dimensional surface graphs, as seen in Fig. 10a-h, which depict the nonlinear relationship between different input pairs and the resulting investment-type recommendations.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Managerial traits of potential investors can affect investment-types.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 16

    The surface of the graph in Figure 10g is nonlinear.

    “Figure 10g for the relationship between satisfaction, attachment, and investment-type displays a nonlinear surface that illustrates how changes in these two inputs affect the recommended investment-type.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 7 Discussion · p. 19

    The proposed system/model can effectively cluster different types of investments used by potential investors and provide investment-type recommendations based on the clusters.

    “The system was able to cluster the types of investments used by potential investors and provide investment-type recommendations based on the clusters.”
    • Also stated in: § 7 Discussion · p. 19 · hedged
      “The results of the current research indicate that the proposed ANFIS model for the investment recommendation system can effectively cluster different types of investments used by potential investors.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § 7 Discussion · p. 19

    The ANFIS model for the investment recommendation system was able to determine the relationship between the managerial traits of potential investors and the recommended investment-types, and showed promising results in determining this relation.

    “The ANFIS model for the investment recommendation system was able to determine the relationship between the managerial traits of potential investors and the recommended investment-types.”
    • Also stated in: § 7 Discussion · p. 19 · plain
      “The proposed ManagerialTraitsANFIS system also showed promising results in determining the relation between managerial traits and investment-type.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given
    § 6.1 Managerial traits ANFIS output · p. 8

    Cluster 1 represents individuals who are not currently invested in the stock market and have not been monitoring stock performance.

    “The first cluster represents individuals who are not currently invested in the stock market and have not been monitoring stock performance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given
    § 7 Discussion · p. 19

    The model's structure was able to effectively process the data through the fuzzification, implication rules, normalization, defuzzification, and integration layers.

    “The model's structure was able to effectively process the data through the fuzzification, implication rules, normalization, defuzzification, and integration layers.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 16

    The surface of the graph in Figure 10e is nonlinear and monolithic.

    “The surface of the graph is nonlinear and monolithic, indicating that the relationship between the variables is not a simple linear relationship.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 6.1 Managerial traits ANFIS output · p. 8

    Cluster 2 represents individuals who are currently invested in the stock market, regularly monitor stock performance, and have investments in government bonds.

    “The second cluster represents individuals who are currently invested in the stock market, regularly monitor stock performance, and have investments in government bonds.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Findingnone given
    § 3 Literature review · p. 4

    The proposed system uses MATLAB and JMP for data import and validation, while existing systems use only MATLAB or JMP.

    “Additionally, the proposed system uses MATLAB and JMP for data import and validation, while existing systems use only MATLAB or JMP.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given
    § 6.3 Proposing the managerial traits ANFIS · p. 11

    The system generated a total of 2880 rules.

    “The system generated a total of 2880 rules, providing a comprehensive analysis of the potential investors' managerial traits.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 6.1 Managerial traits ANFIS output · p. 7

    The first cluster contains 592 items, the second cluster contains 406 items, and the third cluster contains 340 items.

    “The results in JMP showed that the first cluster contains 592 items, the second cluster contains 406 items, and the third cluster contains 340 items.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data
    § Neuro-FuzzyDesigner:ManagerialTraitsANFIS · p. 15

    The surface of the graph in Figure 10b is nonlinear and monolithic.

    “The graph is a nonlinear and monolithic surface that shows the recommended investment-type for a given level of satisfaction and stress.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given
    § 7 Discussion · p. 19

    The system's 3D graphs effectively visualized the nonlinear and monolithic relationship between inputs and output.

    “The system's 3D graphs effectively visualized the nonlinear and monolithic relationship between inputs and output”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given
    § 8 Conclusion · p. 20

    The proposed model addresses the issue of incomplete data by using techniques to handle missing data and impute missing values.

    “The proposed model also addresses the issue of incomplete data by using techniques to handle missing data and impute missing values.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Findingnone given
    § 5 Proposed model · p. 6

    The data are grouped into three clusters based on similarity.

    “The data are grouped into three clusters based on similarity”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given
    § 7 Discussion · p. 19

    The use of both clustering and ANFIS in the proposed model allows for a more detailed analysis of the data and a more accurate determination of investment recommendations.

    “The use of both clustering and ANFIS in the proposed model allows for a more detailed analysis of the data and a more accurate determination of investment recommendations.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationnone given
    § 7 Discussion · p. 19

    The current study expands on previous research by including a wider range of inputs, such as managerial traits, in the ANFIS system.

    “The current study also expands on previous research by including a wider range of inputs, such as managerial traits, in the ANFIS system.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § 4 Methods · p. 4

    Multiple methods were used to evaluate and compare the results of clustering in MATLAB and JMP to ensure the validity of the findings, including visual comparison, combining the results, and expert knowledge.

    “Multiple methods were used to evaluate and compare the results of clustering in MATLAB and JMP to ensure the validity of the findings. The methods included visual comparison, combining the results, and expert knowledge.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given
    § 7 Discussion · p. 19

    The model relies on the self-reported data provided by the potential investors, which may not always be accurate or complete.

    “the model relies on the self-reported data provided by the potential investors, which may not always be accurate or complete.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given
    § 8 Conclusion · p. 20

    One of the main innovations of this research is the use of ANFIS in combination with clustering to determine investment-type recommendations for potential investors, and this is a novel approach in the field of investment recommendation systems.

    “One of the main innovations of this research is the use of ANFIS in combination with clustering to determine investment-type recommendations for potential investors.”
    • Also stated in: § 8 Conclusion · p. 20 · plain
      “This is a novel approach in the field of investment recommendation systems.”
    • Also stated in: § 8 Conclusion · p. 20 · plain
      “In summary, this research presents a novel approach for determining investment-type recommendations for potential investors using ANFIS and clustering.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § 6.1 Managerial traits ANFIS output · p. 8

    Multiple methods (visual comparison, combining the results, and expert knowledge) were used to evaluate and compare the results of clustering in both MATLAB and JMP to ensure the validity of the findings.

    “To ensure the validity of the findings, multiple methods are used to evaluate and compare the results of clustering in both MATLAB and JMP. These include visual comparison, combining the results, and expert knowledge.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § Abstract · p. 1

    The ANFIS model's performance was evaluated using a validation set.

    “the model's performance was evaluated using a validation set”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning
    § 5 Proposed model · p. 6

    The ANFIS model provides customized investment recommendations based on potential investors' input preferences and individual needs.

    “Overall, The ANFIS model provides customized investment recommendations based on the needs of potential investors by using their input preferences to generate personalized output recommendations.”
    • Also stated in: § 5 Proposed model · p. 6 · plain
      “In this way, the ANFIS model can provide customized investment recommendations to potential investors based on their individual needs and preferences.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation
    § 1 Introduction · p. 2

    The information and characteristics of people who participate in the market systematically affect their investment decisions as well as the market.

    “there is a basic assumption that the information and characteristics of people who participate in the market systematically affect their investment decisions as well as the market.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Baker and Nofsinger 2010 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § 4 Methods · p. 5

    The study is based on self-reported data, which may be subject to bias and inaccurate.

    “Additionally, the study is based on self-reported data, which may be subject to bias and inaccurate.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Premisenone given
    § 3 Literature review · p. 2

    Incorporating investor preferences and fuzzy information in investment decision-making using portfolio models and various fuzzy sets and optimization methods can lead to more robust and efficient investment decisions.

    “Overall, research suggests that incorporating investor preferences and fuzzy information in investment decision-making using portfolio models and various fuzzy sets and optimization methods can lead to more robust and efficient investment decisions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given
    § 8 Conclusion · p. 20

    The proposed model does not consider other factors that may affect investment decisions such as economic conditions and market trends.

    “the proposed model does not consider other factors that may affect investment decisions such as economic conditions and market trends.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data
    § 7 Discussion · p. 19

    The proposed model is based on a specific data set and may not be universally applicable to all potential investors.

    “the proposed model is based on a specific data set and may not be universally applicable to all potential investors.”
    • Also stated in: § 8 Conclusion · p. 20 · hedged
      “The proposed model is based on a specific sample of potential investors and may not be generalizable to other populations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given
    § 7 Discussion · p. 19

    The use of both MATLAB and JMP in the clustering process adds to the robustness and validity of the results.

    “Additionally, the use of both MATLAB and JMP in the clustering process adds to the robustness and validity of the results.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § 7 Discussion · p. 19

    The current study adds to the literature by providing a more comprehensive and holistic approach to an investment recommendation compared to previous research.

    “In comparison to previous research in the field, the current study adds to the literature by providing a more comprehensive and holistic approach to an investment recommendation.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given
    § 3 Literature review · p. 2

    All the studies mentioned above have been conducted on a limited set of data and the generalization of the findings to other contexts, scenarios or other domains may be limited.

    “It is worth noting that all the studies mentioned above have been conducted on a limited set of data and the generalization of the findings to other contexts, scenarios or other domains may be limited.”
    Quote in the paperNot a substantial claim
39 of 39

  • The paper relies on this without support.

    ANFIS is a hybrid model that combines the strengths of both artificial neural networks (ANNs) and fuzzy logic systems and is effective in various applications, including financial modeling.

    “ANFIS is a hybrid model that combines the strengths of both artificial neural networks (ANNs) and fuzzy logic systems and is effective in various applications, including financial modeling.”

  • The paper relies on this without support.

    Personal characteristics such as stress levels, the pace of work, and satisfaction with work can affect an individual's risk tolerance and investment horizon.

    “Personal characteristics such as stress levels, the pace of work, and satisfaction with work can affect an individual's risk tolerance and investment horizon.”

  • The paper relies on this without support.

    Managerial issues such as the ability to plan, strategy formation, and the level of attachment to the work can also influence investment decisions.

    “Similarly, managerial issues such as the ability to plan, strategy formation, and the level of attachment to the work can also influence investment decisions.”

  • The paper relies on this without support.

    Managerial traits of potential investors can affect investment-types.

    “The inputs represent different managerial traits that can affect investment-types, such as planning, stress, pace, influential, daily schedule, strategy, attachment, satisfaction, and planning time.”

  • The paper relies on this without support.

    The nine questions asked in the questionnaire assess respondents' traits related to planning and management.

    “Nine questions were asked to assess respondents' traits related to planning and management.”
5 of 5

Self-check

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

Each contradiction between two passages listed here was confirmed by an independent check.

  • Satisfaction MF2 has opposite meanings

    Minor

    The prose assigns MF2 to work that does not satisfy the respondent mentally, whereas Table 5 labels that same MF2 as finding the work satisfying.

  • Trained ANFIS is described with four inputs instead of nine

    Minor

    Figure 7's trained network is said to have four inputs, while the proposed ManagerialTraitsANFIS system is repeatedly specified as having nine.

  • Cluster labels are described as both ANFIS inputs and outputs

    Minor

    The methods specify managerial traits as ANFIS inputs and clustered investment types as its output. The experimental results instead say the model was trained with clustered data as inputs, reversing the stated model design.

  • Vredenburgh citation year differs from the reference

    Minor

    The literature review cites Vredenburgh et al. as a 2016 study, while its reference-list entry gives 2000.

  • Membership-function count for each input conflicts with Input 1

    Minor

    The model description says every input has three membership functions, but the Input 1 description assigns it five.

  • Figure 10c misidentifies input 1

    Minor

    Figure 10c calls input 1 satisfaction, but the input definitions identify input 1 as planning.

6 of 6

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Ezhilarasi TP, Sashi Rekha K (2020) Secure recommendation application for environment crop using big data analytics with fuzzy framework. J Green Eng 10(4):1799–1815
  2. Widjanarti A, Okfantia AD, Jabbar MA (2021) Predicting foreign investors' behavior and flows projection in Indonesia government bonds market using machine learning IFC-Bank of Italy Workshop on “Machine learning in central banking”, 19–22 October 2021, Rome. https://www.bis.org/ifc/publ/ifcb57_20.pdf

Cited by

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Related

Authors
Asemi, A., Asemi, A., & Ko, A.
Affiliations
Corvinus University of Budapest (HU)
ORCID
Venue
Soft Computing
Publisher
Springer Science and Business Media LLC
Published
2023-04-18
ISSN
  • 1432-7643 (ISSN-L)
  • 1433-7479
DOI
10.1007/s00500-023-08102-2
OpenAlex
W4366346896
Semantic Scholar
730d3b12fb72b068166fdf1d611b4467078d245a
Cited by
14
References
27
Citations by year
2026: 1 · 2025: 6 · 2024: 4 · 2023: 3
FWCI
4.02
Type
journal article
Language
en
Open access
Licence
Locations
  • Soft Computing (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • Corvinus Research Archive (Corvinus University of Budapest) (submittedVersion, cc-by)
Funders
Corvinus University of Budapest
Topics
  • 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
  • Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
Keywords
managerial characteristics, investment decision-making, adaptive neuro-fuzzy inference system, investor decision-making, investment habits, investment types

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