# An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks

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## Abstract

**:**

## 1. Introduction

## 2. Backgrounds

#### 2.1. Daily Stock Trading

#### 2.2. Related Studies

## 3. Our Proposed Method

#### 3.1. Problem Formulation

#### 3.2. Overall Framework

#### 3.3. Performace Evaluation

#### 3.3.1. Accuracy

#### 3.3.2. Trading Profit

## 4. Experimental Results

#### 4.1. Datasets

#### 4.2. Performance Analysis

#### 4.3. Usefulness of Trading Volume Information

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Accuracy comparison. (

**a**) Accuracy comparison by Multilayer Perceptron (MLP)-based predictor; (

**b**) Accuracy comparison by Support Vector Machine (SVM)-based predictor.

**Figure 4.**Frequency distributions of the best $\alpha $, $\beta $, and $\gamma $ values found in experiments.

**Figure 5.**Profit comparison. (

**a**) Profit comparison by MLP-based predictor; (

**b**) Profit comparison by SVM-based predictor.

**Figure 7.**An example of detail transaction by our method. “B” and “S” denote the buy and sell actions, respectively.

**Figure 8.**Comparison of the performance between our model and other models without volume input data for the MLPs learning method.

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**MDPI and ACS Style**

Dinh, T.-A.; Kwon, Y.-K. An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks. *Informatics* **2018**, *5*, 36.
https://doi.org/10.3390/informatics5030036

**AMA Style**

Dinh T-A, Kwon Y-K. An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks. *Informatics*. 2018; 5(3):36.
https://doi.org/10.3390/informatics5030036

**Chicago/Turabian Style**

Dinh, Thuy-An, and Yung-Keun Kwon. 2018. "An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks" *Informatics* 5, no. 3: 36.
https://doi.org/10.3390/informatics5030036