# A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review

^{1}

^{2}

^{3}

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## Abstract

**:**

## 1. Introduction

## 2. Results

#### 2.1. Planning

_{1}are described above; answers to QR

_{2}–QR

_{3}are presented in Section 2.5; and the solution to QR

_{4}is presented in the Section 4.

#### 2.2. Searching the Literature

#### 2.3. Study Selection

_{2}criteria; thus, 13 articles were deleted from the SD sources, and 361 articles were deleted from the PQ sources, leaving 364 from the three databases. Next, two articles were deleted because of duplication, leaving 362 articles. Deletion was also performed if the title and abstract were deemed not relevant to the topic. At this stage, 264 articles had been deleted, leaving 98 articles. Further selection was conducted by reading the contents of the articles. By following the QA presented in Table 2, 85 articles were removed because they did not meet QA

_{1}, QA

_{2}, or QA

_{3}. Table 5 presents the studies that were selected on the basis of using the QA.

#### 2.4. Bibliometric Analysis

#### 2.5. General Characteristic of the Literature

#### 2.5.1. Publications and Citations by Year

#### 2.5.2. Citations

#### 2.5.3. Journals

#### 2.5.4. Keywords

#### 2.5.5. Stock Markets Covered

#### 2.5.6. Methodology

## 3. Materials and Methods

#### 3.1. Materials

#### 3.2. Methods

- Interpret all available research to provide specific answers to the research questions developed at the planning stage.
- Perform a bibliometric analysis by using the VOSviewer application. The bibliometric analysis is carried out on the selected studies to determine the relationships between words contained in the article; next, the results were processed to identify shifts in topics in the article (Sukono et al. 2022).
- Analyze the general characteristics of the literature and examine the mathematical model to predict investment risk in the stock market in reference to the methods and models used in the development of the conceptual model.
- Determine gaps in the literature from models and methods to predict investment risks in the stock market by using EVT. The goal is to identify gaps to fill, which will assist in developing future models.
- Report the review, propose a conceptual model, and provide directions for future studies.

## 4. Discussion

#### 4.1. Literature Analysis

- All the above studies used one input variable in the model, namely daily returns.
- All the studies in the literature used the POT method, based on GPD.
- Predicting VaR using only the EVT approach identified the limitations of this model in predicting dynamic VaR.
- The above research illustrates that the EVT approach is better if it uses a hybrid method and works well in univariate cases or when using one input variable.
- The EVT method shows difficulties in multivariate cases.

#### 4.2. Gaps in the Existing Literature

#### 4.3. Conceptual Model

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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QR | Questions |
---|---|

QR_{1} | What is the purpose of this research? |

QR_{2} | How did the VaR–CVaR model function as an EVT method for predicting investment risk in the stock market during the COVID-19 pandemic? |

QR_{3} | What are the input variables commonly used? |

QR_{4} | What is the investment-risk-prediction model that is dynamic and sensitive to extreme fluctuations? |

Criteria | IC | EC |
---|---|---|

IC_{1} | The study of the analysis, prediction, forecasting, and estimation of investment risk in the stock market with the VaR–CVaR hybrid method with the EVT approach. | Studies that are not related to the analysis, prediction, forecasting, and estimation of investment risk in the stock market. |

IC_{2} | Research articles from peer-reviewed international journals. | None of the research articles. |

IC_{3} | Articles published in the period 2019 to 2022. | Articles published outside the period 2019 to 2022. |

IC_{4} | Using English. | Using a language other than English. |

QA | Information |
---|---|

QA_{1} | Is the article analysis, forecasting, estimation, or prediction of investment risk in the stock market? |

QA_{2} | Does the article use the hybrid VaR—CVaR method with EVT, block maxima, peaks over threshold, GEV distribution, and GPD? |

QA_{3} | Is the primary source of the stock market data in the form of stocks? |

K | Query | Results | |||
---|---|---|---|---|---|

S | SD | PQ | Total | ||

K_{1} | (“forecasting” OR “prediction” OR “predicting”) AND (“var” OR “cvar” OR “risk”) | 200 | 383,468 | 1,122,461 | 1,506,129 |

K_{2} | K_{1} AND (“stock market”) | 200 | 11,514 | 76,943 | 88,657 |

K_{3} | K_{2} AND (“extreme value theory” OR “EVT”) | 8 | 152 | 578 | 738 |

Number | Sources | Authors | QA_{1} | QA_{2} | QA_{3} |
---|---|---|---|---|---|

1 | (Ji et al. 2019) | Jingru Ji; Donghua Wang; Dinghai Xu. | √ | √ | √ |

2 | (Karmakar and Paul 2019) | Madhusudan Karmakar; Samit Paul. | √ | √ | √ |

3 | (Tabasi et al. 2019) | Hamėd Tabasi; Jolanta Tamosaitiene; Vahidreza Yousėfi; Foroogh Ghasemi. | √ | √ | √ |

4 | (Banerjee and Paul 2020) | Aditya Banerjee; Samit Paul. | √ | √ | √ |

5 | (Bień-Barkowska 2020) | Katarzyna Bień-Barkowska. | √ | √ | √ |

6 | (Chen and Yu 2020) | Yan Chen; Wenqiang Yu. | √ | √ | √ |

7 | (Ji et al. 2020) | Jingru Ji; Dinghai Xu; Donghua Wang; Chi Xu. | √ | √ | √ |

8 | (Miloš 2020) | Miloš Božović. | √ | √ | √ |

9 | (Sobreira and Louro 2020) | Nuno Sobreira; Rui Louro. | √ | √ | √ |

10 | (Chaiboonsri and Wannapan 2021) | Chukiat Chaiboonsri; SatawatWannapan. | √ | √ | √ |

11 | (Ghourabi et al. 2021) | Mohamed El Ghourabi; Asma Nani; Imed Gammoudi. | √ | √ | √ |

12 | (Song et al. 2021) | Shijia Song; Fei Tian; Handong Li. | √ | √ | √ |

13 | (Chebbi and Hedhli 2022) | Ali Chebbi; Amel Hedhli. | √ | √ | √ |

Rank | Sources | Journal | Citations |
---|---|---|---|

1 | (Karmakar and Paul 2019) | International Journal of Forecasting. | 32 |

2 | (Tabasi et al. 2019) | Administrative Sciences. | 11 |

3 | (Sobreira and Louro 2020) | Finance Research Letters. | 8 |

4 | (Ji et al. 2020) | Journal of Empirical Finance. | 7 |

5 | (Bień-Barkowska 2020) | Entropy. | 7 |

6 | (Song et al. 2021) | Journal of Asian Economics. | 5 |

7 | (Chebbi and Hedhli 2022) | The Quarterly Review of Economics and Finance. | 4 |

8 | (Chen and Yu 2020) | Physica A: Statistical Mechanics and its Applications. | 4 |

9 | (Chaiboonsri and Wannapan 2021) | Economies. | 2 |

10 | (Banerjee and Paul 2020) | Global Business Review. | 2 |

11 | (Ji et al. 2019) | Economic Modeling. | 2 |

12 | (Miloš 2020) | Entropy. | 1 |

13 | (Ghourabi et al. 2021) | International Journal of Finance and Economics. | 1 |

Number | Journal | ISSN | Country | Publisher | H | Quartiles | SJR | Articles | Citations |
---|---|---|---|---|---|---|---|---|---|

Index | 2021 | ||||||||

1 | International Journal of Forecasting | 1692070 | Netherlands | Elsevier | 100 | Q1 | 1.99 | 1 | 32 |

2 | Administrative Sciences | 20763387 | Switzerland | MDPI AG | 23 | Q2 | 0.48 | 1 | 11 |

3 | Finance Research Letters | 15446123 | Netherlands | Elsevier BV | 62 | Q1 | 2.01 | 1 | 8 |

4 | Entropy | 10994300 | Switzerland | MDPI | 81 | Q2 | 0.55 | 2 | 8 |

5 | Journal of Empirical Finance | 9275398 | Netherlands | Elsevier | 80 | Q1 | 1.20 | 1 | 7 |

6 | Journal of Asian Economics | 10490078 | Netherlands | Elsevier | 51 | Q2 | 0.65 | 1 | 5 |

7 | Physica A: Statistical Mechanics and its Applications | 3784371 | Netherlands | Elsevier | 170 | Q1 | 0.89 | 1 | 4 |

8 | Quarterly Review of Economics and Finance | 10629769 | Netherlands | Elsevier | 55 | Q2 | 0.69 | 1 | 4 |

9 | Economies | 22277099 | Switzerland | MDPI | 19 | Q2 | 0.44 | 1 | 2 |

10 | Global Business Review | 9721509 | India | Sage Publications India Pvt. Ltd. | 30 | Q2 | 0.45 | 1 | 2 |

11 | Economic Modeling | 2649993 | Netherlands | Elsevier | 87 | Q2 | 1.07 | 1 | 2 |

12 | International Journal of Finance and Economics | 10769307 | UK | John Wiley and Sons Ltd. | 41 | Q2 | 0.42 | 1 | 1 |

Sources | Best Model | Model Used |
---|---|---|

(Ji et al. 2019) | The agent-based (AB) model. | The AB model. |

(Karmakar and Paul 2019) | CGARCH–EVT-Copula. | CGARCH, CGARCH–EVT-Clayton, CGARCH-HS, GARCH–EVT, CGARCH–t-EVT, CGARCH–Gumbel-EVT, CGARCH–EVT-Copula, and CGARCH- BB1-EVT. |

(Tabasi et al. 2019) | POT-GARCH with a Student’s t distribution (T-SD) for residual values (RV). | GARCH with a T-SD for RV, GARCH model with a normal distribution for RV, and POT-GARCH with a normal distribution for RV. |

(Banerjee and Paul 2020) | MCS–GARCH–EVT. | EGARCH-EVT, CGARCH–EVT, MCS-GARCH, MCS-GARCH–EVT, GARCH, EGARCH, CGARCH, and GARCH–EVT. |

(Bień-Barkowska 2020) | SEP-POT. | SEP-POT, EGARCH skewed–t, and SEI-POT. |

(Chen and Yu 2020) | APARCH-GPD. | APARCH-t, APARCH-GPD, and EWMA. |

(Ji et al. 2020) | The self-exciting point process (SEEP) with the truncated GPD. | The SEEP with the truncated GPD. |

(Miloš 2020) | mv GARCH–GP. | mv GARCH–GP, mv GJR–GP. |

(Sobreira and Louro 2020) | GARCH–EVT. | IGARCH, GARCH, GJR–GARCH, EGARCH, GARCH–EVT, EGARCH-EVT, IGARCH-EVT, and GJR–GARCH-EVT. |

(Chaiboonsri and Wannapan 2021) | Quantum mechanics (QM). | QM. |

(Ghourabi et al. 2021) | VaR based GAS-EVT. | VaR based GAS-EVT, and Dekker’s-VaR. |

(Song et al. 2021) | GP–DCS-VaR. | GP–DCS-VaR, RGARCH–SSTD-RV, RGARCH–GED-RV, RGARCH–NIG-RV, RGARCH–SSTD-RRV, RGARCH–GED-RRV, and RGARCH–NIG-RRV. |

(Chebbi and Hedhli 2022) | GARCH–EVT–vine copula. | GARCH–EVT–vine copula, EWMA, HS, and GARCH. |

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## Share and Cite

**MDPI and ACS Style**

Melina; Sukono; Napitupulu, H.; Mohamed, N.
A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review. *Risks* **2023**, *11*, 60.
https://doi.org/10.3390/risks11030060

**AMA Style**

Melina, Sukono, Napitupulu H, Mohamed N.
A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review. *Risks*. 2023; 11(3):60.
https://doi.org/10.3390/risks11030060

**Chicago/Turabian Style**

Melina, Sukono, Herlina Napitupulu, and Norizan Mohamed.
2023. "A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review" *Risks* 11, no. 3: 60.
https://doi.org/10.3390/risks11030060