# The Way to Invest: Trading Strategies Based on ARIMA and Investor Personality

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## Abstract

**:**

## 1. Introduction

- Adding investor personality theory to the portfolio model expands the rationality of the investment.
- Expanded the application of Sharpe ratio in the investment field by applying the generalized Sharpe ratio to portfolio models.
- A novel investment strategy portfolio model based on investor personality is proposed.
- Expanded the theory of investment personality theory applied to the stock investment market.

- Assuming no unpredictable fluctuations in the stock market during the forecast period due to large political factors.
- Conservative and aggressive investors follow the principle of limited rationality.
- Conservative investors are more sensitive to changes in tax rates.
- Tax changes will not significantly affect the investment enthusiasm of aggressive investors.

**(1) forecasting problem and (2) optimal planning problem**. We select the best performing model as the forecasting model by comparing different time-series forecasting models (e.g., ARIMA, SESM) and then introduce the Sharpe ratio, efficient frontier theory for risk quantification, and portfolio optimization. We assume a start-up capital of $1000 from 2016 and evaluate the amount of investment return after five years under the optimal portfolio. In Section 3, we upgrade and optimize our model by using intelligent algorithm and particle swarm algorithm, and the model has better robustness. Moreover, we analyze whether the current model has a high stability by adding a perturbation term. In Section 4, we mainly analyze the impact of different investor personalities on investment trading strategies, set different investor personalities, including conservative, intermediate, and aggressive, and study the impact of investor personalities on trading cost, expected investment return, and the number of trades.

## 2. Dynamic Trading Strategy Based on ARIMA

#### 2.1. Predictive Modeling

#### 2.1.1. Settlement Price Forecast Analysis

#### 2.1.2. ARIMA (Autoregressive Integrated Moving Average Model)

#### 2.1.3. SESM (Second Exponential Smoothing Method)

#### 2.1.4. Comparison of the Accuracy of Prediction Models

#### 2.2. Quantitative Trading Strategies Based on Dynamic Programming

#### 2.2.1. Dynamic Planning Problem Analysis

Algorithm 1 Simulation of asset investment |

Input: original assets C, G, B, risk factor risk, data predicted from the original data, days to be invested, working days flag, growth rate of assets R. Output: the final distribution of the total value of assets V. 1: for t=1 to # of day do 2: if then 3: allocate (data, asset table, risk) 4: Calculate the optimal solution for asset allocation under risk for the three assets according to Date using the portfolio toolbox function to divide the funds Q 5: G, B change amount for commission payment 6: Evaluate the total value of C, G, B according to R 7: else 8: Calculate the optimal solution for only two assets under risk according to Date, as above 9: end if 10: end for return V |

#### 2.2.2. Mean-Variance Mode

#### 2.2.3. Combination of Effective Frontier and Sharpe Ratio

- The point on the efficient frontier curve that maximizes the expected return for a given expected risk
- The point on the efficient frontier curve that minimizes risk given the expected return

#### 2.3. Analysis of the Advantages and Disadvantages of Prediction Models

## 3. Improvement of the Optimization of the Model

#### 3.1. Planning Strategy Model Improvement

#### 3.1.1. Optimization of Sharpe Ratio

#### 3.1.2. Particle Swarm Optimization Algorithm

#### 3.1.3. Model Stability Testing

## 4. Stability Analysis of the Model

## 5. Model Evaluation

#### 5.1. Strength

**Comprehensive consideration:**

**Making the best use of information**:

**Excellent robustness of the model:**

**Low time complexity:**

**Improvements in data processing:**

#### 5.2. Weakness

**Insufficient data:**

**Subjective assumptions about personality:**

## 6. Conclusions

## 7. Future Research Direction

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 12.**Combining cash with risky assets and adding cash as a risk-free asset to the adjusted risk frontier curve.

Aggressive | Intermediate | Conservative | |
---|---|---|---|

Return | 15,581.85 | 9869.34 | 3676.75 |

MAPE | RMSE | |
---|---|---|

ARIMA | 0.026754 | 651,580.6 |

SEME | 0.034827 | 742,891.6 |

Aggressive | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|

ratio_gold | 1% | 1.50% | 0.50% | 1% | 1% | 0.50% | 2% |

ratio_bitcoin | 2% | 2% | 2% | 1.50% | 2.50% | 1% | 4% |

return | 15,581 | 15,181 | 15,992 | 19,051 | 12,740 | 23,901 | 6599 |

g_times | 1189 | 1176 | 1297 | 1218 | 1214 | 1362 | 1183 |

b_times | 1372 | 1302 | 1452 | 1414 | 1289 | 1501 | 1275 |

Intermediate | |||||||

return | 8869 | 8636 | 9108 | 10,427 | 7542 | 12,589 | 4395 |

g_times | 378 | 314 | 498 | 354 | 318 | 458 | 245 |

b_times | 923 | 892 | 956 | 1136 | 893 | 1013 | 769 |

Conservative | |||||||

return | 3676 | 3632 | 3721 | 3939 | 3431 | 4272 | 2722 |

g_times | 112 | 59 | 248 | 126 | 102 | 272 | 53 |

b_times | 654 | 631 | 642 | 712 | 521 | 852 | 383 |

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**MDPI and ACS Style**

Tang, X.; Xu, S.; Ye, H.
The Way to Invest: Trading Strategies Based on ARIMA and Investor Personality. *Symmetry* **2022**, *14*, 2292.
https://doi.org/10.3390/sym14112292

**AMA Style**

Tang X, Xu S, Ye H.
The Way to Invest: Trading Strategies Based on ARIMA and Investor Personality. *Symmetry*. 2022; 14(11):2292.
https://doi.org/10.3390/sym14112292

**Chicago/Turabian Style**

Tang, Xiaoyu, Sijia Xu, and Hui Ye.
2022. "The Way to Invest: Trading Strategies Based on ARIMA and Investor Personality" *Symmetry* 14, no. 11: 2292.
https://doi.org/10.3390/sym14112292