# Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

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## Abstract

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## 1. Introduction

- One of the unique contributions of this review article is that it is not just limited to summarizing the research articles. Extra effort is put into implementing the well-known machine learning and deep learning models to understand their nature and performance. Along with our review, a comparative analysis of various algorithms is presented in this article. The machine learning and deep learning ensemble algorithms are tested on TAINIWALCHM and AGROPHOS stock data, which fall under the umbrella of the chemical industry market sector.
- In this review article, detailed future research directions are included. Future research avenues for researchers are identified based on the conducted study stock trend analysis and classification, pattern identification, and candlestick chart pattern analysis using computer vision.

## 2. Comprehensive Summary of Theoretical Basis

#### 2.1. Basic Machine Learning Algorithm

#### 2.1.1. Linear Regression

#### 2.1.2. K-Nearest Neighbor (KNN)

_{r}represents the predicted value and h

_{i}represents the data value.

#### 2.1.3. Support Vector Machine (SVM)

#### 2.1.4. Naïve Bayes Algorithm

#### 2.1.5. Logistic Regression

#### 2.2. Forecasting of Stock Market Using Time Series Forecasting

#### 2.2.1. ARIMA

#### 2.2.2. FB Prophet Model

#### 2.3. Deep Learning Methods

#### 2.3.1. Long Short-Term Memory (LSTM)

#### 2.3.2. Gated Recurrent Neural Network (GRU)

- (1)
- Update gate$$Z\left[t\right]=\sigma ({W}^{\left(z\right)}{x}_{t}+{U}^{\left(z\right)}{h}_{t-1})$$
- (2)
- Reset gate$$r\left[t\right]=\sigma ({W}^{\left(r\right)}{x}_{t}+{U}^{\left(r\right)}{h}_{t-1})$$
_{t}_{−1}is the result of the preceding GRU block, and ${x}_{t}$is the current input.

#### 2.4. Ensemble Learning Methods

#### 2.4.1. Random Forest Algorithm

#### 2.4.2. XG-Boost Regression Algorithm

#### 2.4.3. E-SVR-RF (Ensemble Support Vector Machine–Random Forest)

## 3. General Machine Learning Pipeline

## 4. Significance of Ensemble Modeling

## 5. Implications and Limitations of the Study

## 6. Future Research Directions

#### 6.1. Trend Analysis and Classification

#### 6.2. Pattern Identification Using Computer Vision

#### 6.3. Chart Pattern Analysis Using Computer Vision

## 7. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 7.**TANIWALCHM stock price forecasting: (

**a**) SVR, (

**b**) MLPR, (

**c**) KNN, (

**d**) random forest, (

**e**) XG-Boost, (

**f**) LSTM, (

**g**) Ensemble Random Forest + XG-Boost + LSTM.

**Figure 8.**AGROPHOS stock price forecasting: (

**a**) SVR, (

**b**) MLPR, (

**c**) KNN, (

**d**) random forest, (

**e**) XG-Boost, (

**f**) LSTM, (

**g**) Ensemble Random Forest + XG-Boost + LSTM.

Parameter | Description |
---|---|

Model type | Stacked ensemble model |

Libraries | Keras, TensorFlow, sklearn |

Algorithms | Random Forest + XG-Boost + LSTM |

Training/testing size | 80% for training and 20% for testing |

Loss function | MSE |

Optimizer | Adam |

Maximum epochs | 50 |

Random Forest Configuration | |

Number of estimators | [50, 100, 200] |

Maximum depth | [3, 5, 7] |

Maximum features | [‘sqrt’, ‘log2’] |

Hyperparameter tuning method | Grid search |

XG-Boost Configuration | |

Maximum depth | [3, 4, 5] |

Learning rate | [0.1, 0.01, 0.001] |

Number of estimators | [50, 100, 150, 500, 1000] |

Hyperparameter tuning method | Grid search |

LSTM Configuration | |

LSTM Layers | 2 |

Dropout rate | 0.2 |

Dense layer | 25 units |

Batch size | 32 |

Hyperparameter tuning method | Grid search |

TANIWALCHM | AGROPHOS | |||
---|---|---|---|---|

Algorithm | RMSE | R2 | RMSE | R2 |

SVR | 4.525 | 0.9279 | 1.5074 | 0.9432 |

MLPR | 2.5893 | 0.9611 | 2.4764 | 0.9472 |

KNN | 4.4249 | 0.9311 | 4.7877 | 0.7262 |

LSTM | 5.6241 | 0.8867 | 5.2494 | 0.8809 |

Random forest | 87.8839 | 0.9818 | 98.5633 | 0.9428 |

XG-Boost | 2.0686 | 0.9842 | 1.7618 | 0.9379 |

Random Forest + XG-Boost + LSTM | 2.0247 | 0.9921 | 1.2658 | 0.9897 |

Sr No | Algorithm Name | Gap Analysis | Performance Evaluation |
---|---|---|---|

1 | Linear regression | Linear regression assumes a linear relationship between dependent and independent variables and is not suitable for most real-time applications, and is used to perform observations on readily accessible sample data (Gururaj et al. 2019). | This is a regression type of algorithm that shows graphs in a linear way, where in RMSE is 3.22, MAE is 2.53, MSE is 10.37, and R-squared is 0.73 (Gururaj et al. 2019). |

2 | Support vector machine | SVM’s excellent memory efficiency and effectiveness make it an ideal estimating technique in high-dimensional space. The shortcoming of SVM is that it might experience overfitting, but it performs exceptionally well on tiny datasets (Pathak and Pathak 2020; Grigoryan 2017). | This is a classification type of algorithm, and when used for stock prediction, the results are as follows: accuracy is 68.2, recall is 65.2, precision is 64.2, and F1-Score is 64.9% (0.65). When used as a support vector regression (SVR) algorithm, the evaluation parameters are as follows: SMAPE = 5.59, R-squared = 1.69, and RMSE = 43.36. |

3 | K-nearest neighbor | KNN is an algorithm that skips the learning step, so it may not generalize effectively. With a huge dataset, it takes longer since it must sort all the distances from the unknown item (Tanuwijaya and Hansun 2019; Pathak and Pathak 2020). | This is a classification type of ML algorithm in which the results of stock prediction are as follows: accuracy is 65.2, recall is 63.6, precision is 64.8, and F1 Score is 64.1% (0.64). For KNN regressor, the evaluation parameters are as follows: SMAPE = 14.32, R-squared = −2.42, and RMSE = 56.44 (Venkat 2022). |

4 | Gaussian naïve Bayes | Models with an integrated GNB algorithm will yield feature extraction and feature scaling outcomes that are superior to those already achieved using models that incorporate either the GNB algorithm or feature scaling (Setiani et al. 2020; Ampomah et al. 2021). | Gaussian naïve Bayes used by authors in their research. Kendall’s Test of Concordance is used in this feature, which is scaled and extracted. The results are as follows: accuracy is 84, F1 Score is 62.44% (0.62), specificity is 0.70, and AUC values is 0.90 (Bansal et al. 2022). |

5 | Logistic regression | Both binary and multiclass classification use this algorithm. The findings obtained via logistic regression are the most accurate; however, identifying the best-fitting feature is necessary (Pathak and Pathak 2020; Ali et al. 2018). | In this algorithm, with the help of research papers, various financial factors are considered through which factors are grouped for prediction. The results of this algorithm are as follows: accuracy is 78.6, recall is 76.6, precision is 77.8, and F1 Score is 77.1% (0.77) (Pathak and Pathak 2020; Ali et al. 2018). |

6 | ARIMA | ARIMA can be considered because it is a unique model with significant coefficients and passes all the diagnostic tests (Mashadihasanli 2022). | ARIMA is a time series forecasting technique for predicting market or stock prices. It is a combination or integration of autoregressive moving averages; the results are as follows: RMSE, 88.05; MAE, 65.88; and MAPE, 5.73, and if performed with sentiment analysis, the RMSE score is 6.41 (Kedar 2021). |

7 | FB Prophet | Prophet can use regression models to determine seasonality on a daily, monthly, and annual basis, as well as effects related to holidays (Suresh et al. 2022; Kaninde et al. 2022). | This algorithm was created by Facebook for time series forecasting. One of its advantages is that it does not consider holidays or null values. The result of its RMSE is achieved 93 (Suresh et al. 2022; Kaninde et al. 2022). |

8 | GRU | GRU is a neural network approach that is an improvement upon RNN but has fewer parameters than LSTM, so it trains faster. Also, the chances of overfitting are lower compared to RNN, and it can handle long-term dependency (Shahi et al. 2020). | GRU is a deep learning algorithm that has fewer parameters and handles short-term data properly. The evaluation parameters of GRU are, without sentiment analysis, MAE = 42.8, RMSE = 47.31, and R-squared = 0.879, and with sentiment analysis based on news evaluation parameters, MAE = 24.472, RMSE = 29.153, and R-squared = 0.967 (Shahi et al. 2020). |

9 | LSTM | In this algorithm, weights are adjusted for each long short-term memory data point via stochastic gradient descent. LSTM can handle more very long-term dependency compared to any other neural network algorithm (Shahi et al. 2020; Pramod and Pm 2021; Mukherjee et al. 2021). | LSTM is a more developed type of RNN and is a deep learning technique. This is one of the most used ML algorithms for stock forecasting, and when used along with sentiment analysis, it shows better results than without sentiment analysis. The result without sentiment analysis are MAE = 48.47, RMSE = 55.993, and R-squared = 0.867, and with sentiment analysis based on news evaluation parameters, are MAE = 17.689, RMSE = 23.070, and R-squared = 0.867 (Shahi et al. 2020). |

10 | Random forest | The effectiveness of random forest on large datasets is one of its advantages. It can be applied to classification and regression issues. The model becomes more random as a result, improving it. This model’s use of a huge number of trees slows it down, which is a drawback (Pathak and Pathak 2020; Polamuri et al. 2019). | This is an ensemble type of algorithm that is used for stock forecasting. The results for random forest are as follows: accuracy is 80.7, recall is 78.3, precision is 75.2, and F1 Score is 76.7% (0.77) (Pathak and Pathak 2020; Polamuri et al. 2019). |

11 | XG-Boost | XG-Boost is an ensembled learning technique that uses decision trees but in a sequential manner and uses gradient boosting in an iterative manner to obtain better results. XG-Boost is sensitive to hyperparameters and will not work as well on large datasets as random forest (Zhu and He 2022). | XG-Boost has the following evaluation parameter: MSE = 360.0 (Zhu and He 2022). |

12 | E-SVR-RF | Ensemble support vector regression and random forest shows improved accuracy as it leverages the advantages of both algorithms, and its robustness is increased, but due to the increased complexity of both algorithms, overfitting is an issue (Xu et al. 2020). | The E-SVR-RF ensembled algorithm shows the following evaluation parameters: MAPE = 1.335, MAE = 0.1537, RMSE = 0.0188, and MAE = 0.0485 (Xu et al. 2020). |

13 | XG-Boost + LSTM | Ensembling XG-Boost and LSTM can leverage the advantages of both algorithms. XG-Boost can handle linear and non-linear relationships and LSTM can handle long-term dependence. Due to this algorithm’s complexity, hyperparameter tuning can be an issue (Vuong et al. 2022). | The ensemble algorithm of XG-Boost + LSTM shows the following evaluation parameter: MSE = 3.465 (Vuong et al. 2022). |

14 | Blending ensemble (LSTM + GRU) | Blending ensemble (LSTM+GRU) is a combination of two to of the most-used improvements to RNN and solves the vanishing gradient problem. Both of them can handle long-term dependence well, and combining them would improve forecasting; also, overfitting can be reduced. It may require high computational power and time to train both LSTM and GRU (Li and Pan 2021). | The blending ensemble algorithm, which consists of a modified version of RNN, i.e., LSTM and GRU, has the following evaluation parameters: MSE = 186.32, MPA = 99.65, precision = 60%, Recall = 75%, F1-Score = 66.67% (Li and Pan 2021). |

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## Share and Cite

**MDPI and ACS Style**

Sonkavde, G.; Dharrao, D.S.; Bongale, A.M.; Deokate, S.T.; Doreswamy, D.; Bhat, S.K.
Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. *Int. J. Financial Stud.* **2023**, *11*, 94.
https://doi.org/10.3390/ijfs11030094

**AMA Style**

Sonkavde G, Dharrao DS, Bongale AM, Deokate ST, Doreswamy D, Bhat SK.
Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. *International Journal of Financial Studies*. 2023; 11(3):94.
https://doi.org/10.3390/ijfs11030094

**Chicago/Turabian Style**

Sonkavde, Gaurang, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat.
2023. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications" *International Journal of Financial Studies* 11, no. 3: 94.
https://doi.org/10.3390/ijfs11030094