Optimal Solution for the Gold Bitcoin Portfolio Investment Model

Zihan Yang; Guanhua Zhang; Chuming Liu

doi:doi:10.11648/j.jfa.20231102.12

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Optimal Solution for the Gold Bitcoin Portfolio Investment Model

Zihan Yang, Guanhua Zhang, Chuming Liu

Published in Journal of Finance and Accounting (Volume 11, Issue 2)

Received: 5 April 2023 Accepted: 31 March 2023 Published: 13 April 2023

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Abstract

This topic is a portfolio investment problem with quantitative trading as the background. In order to solve this problem, three types of mathematical models are used in this paper, namely the prediction model, decision model, and risk assessment model. The first is the forecasting model. The paper applies three forecasting models: the grey system Grach (1, 1) forecasting model, the quadratic exponential smoothing forecasting model, and the time series BP-neural network forecasting model. The second is the decision-making model. The decision-making model in the paper is a constrained linear programming model. The objective function is to maximize the total revenue of the day. Finally, there is the risk assessment model. The quantitative investment and multi-factor models are used in the paper to calculate the standard deviation of the rate of return (conventional risk) of gold and Bitcoin respectively in a 30-day cycle, so as to achieve the purpose of quantifying risk, thus reflecting the relationship between gold and Bitcoin. The investment risk index of the two futures products of the currency is provided as a reference for investors. This paper also adjusts the parameters of the prediction model, such as adjusting the value of the number of neurons in the hidden layer of the BP-neural network, to compare the fitting effects corresponding to different parameters, to prove that the prediction model is an optimal solution; Give the decision-making model a certain disturbance, such as changing the definition of the objective function for the total return of the day, to reflect the performance of the forecasting model in dealing with the disturbance of special factors. After that, this paper also conducts a sensitivity analysis of the decision-making model. The specific method is to give a small disturbance to the decision-making model, such as changing its transaction cost, that is, the value of the commission rate a%, recording the final benefit of the decision-making model, and generating a chart to reflect the model. smoothness and sensitivity. Finally, this paper optimizes some models, such as optimizing the BP-neural network model by adaptively adjusting the learning rate and optimizing the linear programming decision model by adding the MACD information factor.

Published in	Journal of Finance and Accounting (Volume 11, Issue 2)
DOI	10.11648/j.jfa.20231102.12
Page(s)	49-60
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Exponential Smoothing, Time Series Model, Gray Model, Constrained Linear Programming Model, ARIMA Model, BP—Neural Network Model, MACD

References

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[10]	Author links open overlay panelJie Cui a b et al. (2012) A novel grey forecasting model and its optimization, Applied Mathematical Modelling. Elsevier. Available at: https://www.sciencedirect.com/science/article/pii/S0307904X12005835 (Accessed: March 20, 2023).
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[15]	Hayes, A. (2023) Autoregressive integrated moving average (ARIMA) prediction model, Investopedia. Investopedia. Available at: https://www.investopedia.com/terms/a/autoregressive-integrated-moving-average-arima.asp (Accessed: March 20, 2023).

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APA Style

Zihan Yang, Guanhua Zhang, Chuming Liu. (2023). Optimal Solution for the Gold Bitcoin Portfolio Investment Model. Journal of Finance and Accounting, 11(2), 49-60. https://doi.org/10.11648/j.jfa.20231102.12

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ACS Style

Zihan Yang; Guanhua Zhang; Chuming Liu. Optimal Solution for the Gold Bitcoin Portfolio Investment Model. J. Finance Account. 2023, 11(2), 49-60. doi: 10.11648/j.jfa.20231102.12

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AMA Style

Zihan Yang, Guanhua Zhang, Chuming Liu. Optimal Solution for the Gold Bitcoin Portfolio Investment Model. J Finance Account. 2023;11(2):49-60. doi: 10.11648/j.jfa.20231102.12

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@article{10.11648/j.jfa.20231102.12,
  author = {Zihan Yang and Guanhua Zhang and Chuming Liu},
  title = {Optimal Solution for the Gold Bitcoin Portfolio Investment Model},
  journal = {Journal of Finance and Accounting},
  volume = {11},
  number = {2},
  pages = {49-60},
  doi = {10.11648/j.jfa.20231102.12},
  url = {https://doi.org/10.11648/j.jfa.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20231102.12},
  abstract = {This topic is a portfolio investment problem with quantitative trading as the background. In order to solve this problem, three types of mathematical models are used in this paper, namely the prediction model, decision model, and risk assessment model. The first is the forecasting model. The paper applies three forecasting models: the grey system Grach (1, 1) forecasting model, the quadratic exponential smoothing forecasting model, and the time series BP-neural network forecasting model. The second is the decision-making model. The decision-making model in the paper is a constrained linear programming model. The objective function is to maximize the total revenue of the day. Finally, there is the risk assessment model. The quantitative investment and multi-factor models are used in the paper to calculate the standard deviation of the rate of return (conventional risk) of gold and Bitcoin respectively in a 30-day cycle, so as to achieve the purpose of quantifying risk, thus reflecting the relationship between gold and Bitcoin. The investment risk index of the two futures products of the currency is provided as a reference for investors. This paper also adjusts the parameters of the prediction model, such as adjusting the value of the number of neurons in the hidden layer of the BP-neural network, to compare the fitting effects corresponding to different parameters, to prove that the prediction model is an optimal solution; Give the decision-making model a certain disturbance, such as changing the definition of the objective function for the total return of the day, to reflect the performance of the forecasting model in dealing with the disturbance of special factors. After that, this paper also conducts a sensitivity analysis of the decision-making model. The specific method is to give a small disturbance to the decision-making model, such as changing its transaction cost, that is, the value of the commission rate a%, recording the final benefit of the decision-making model, and generating a chart to reflect the model. smoothness and sensitivity. Finally, this paper optimizes some models, such as optimizing the BP-neural network model by adaptively adjusting the learning rate and optimizing the linear programming decision model by adding the MACD information factor.},
 year = {2023}
}

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TY  - JOUR
T1  - Optimal Solution for the Gold Bitcoin Portfolio Investment Model
AU  - Zihan Yang
AU  - Guanhua Zhang
AU  - Chuming Liu
Y1  - 2023/04/13
PY  - 2023
N1  - https://doi.org/10.11648/j.jfa.20231102.12
DO  - 10.11648/j.jfa.20231102.12
T2  - Journal of Finance and Accounting
JF  - Journal of Finance and Accounting
JO  - Journal of Finance and Accounting
SP  - 49
EP  - 60
PB  - Science Publishing Group
SN  - 2330-7323
UR  - https://doi.org/10.11648/j.jfa.20231102.12
AB  - This topic is a portfolio investment problem with quantitative trading as the background. In order to solve this problem, three types of mathematical models are used in this paper, namely the prediction model, decision model, and risk assessment model. The first is the forecasting model. The paper applies three forecasting models: the grey system Grach (1, 1) forecasting model, the quadratic exponential smoothing forecasting model, and the time series BP-neural network forecasting model. The second is the decision-making model. The decision-making model in the paper is a constrained linear programming model. The objective function is to maximize the total revenue of the day. Finally, there is the risk assessment model. The quantitative investment and multi-factor models are used in the paper to calculate the standard deviation of the rate of return (conventional risk) of gold and Bitcoin respectively in a 30-day cycle, so as to achieve the purpose of quantifying risk, thus reflecting the relationship between gold and Bitcoin. The investment risk index of the two futures products of the currency is provided as a reference for investors. This paper also adjusts the parameters of the prediction model, such as adjusting the value of the number of neurons in the hidden layer of the BP-neural network, to compare the fitting effects corresponding to different parameters, to prove that the prediction model is an optimal solution; Give the decision-making model a certain disturbance, such as changing the definition of the objective function for the total return of the day, to reflect the performance of the forecasting model in dealing with the disturbance of special factors. After that, this paper also conducts a sensitivity analysis of the decision-making model. The specific method is to give a small disturbance to the decision-making model, such as changing its transaction cost, that is, the value of the commission rate a%, recording the final benefit of the decision-making model, and generating a chart to reflect the model. smoothness and sensitivity. Finally, this paper optimizes some models, such as optimizing the BP-neural network model by adaptively adjusting the learning rate and optimizing the linear programming decision model by adding the MACD information factor.
VL  - 11
IS  - 2
ER  -

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Author Information

Zihan Yang

Queen Mary University of London Engineering School, Northwestern Polytechnical University, Xi 'an, China
Guanhua Zhang

Queen Mary University of London Engineering School, Northwestern Polytechnical University, Xi 'an, China
Chuming Liu

Queen Mary University of London Engineering School, Northwestern Polytechnical University, Xi 'an, China

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APA Style

Zihan Yang, Guanhua Zhang, Chuming Liu. (2023). Optimal Solution for the Gold Bitcoin Portfolio Investment Model. Journal of Finance and Accounting, 11(2), 49-60. https://doi.org/10.11648/j.jfa.20231102.12

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ACS Style

Zihan Yang; Guanhua Zhang; Chuming Liu. Optimal Solution for the Gold Bitcoin Portfolio Investment Model. J. Finance Account. 2023, 11(2), 49-60. doi: 10.11648/j.jfa.20231102.12

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AMA Style

Zihan Yang, Guanhua Zhang, Chuming Liu. Optimal Solution for the Gold Bitcoin Portfolio Investment Model. J Finance Account. 2023;11(2):49-60. doi: 10.11648/j.jfa.20231102.12

Copy | Download

@article{10.11648/j.jfa.20231102.12,
  author = {Zihan Yang and Guanhua Zhang and Chuming Liu},
  title = {Optimal Solution for the Gold Bitcoin Portfolio Investment Model},
  journal = {Journal of Finance and Accounting},
  volume = {11},
  number = {2},
  pages = {49-60},
  doi = {10.11648/j.jfa.20231102.12},
  url = {https://doi.org/10.11648/j.jfa.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20231102.12},
  abstract = {This topic is a portfolio investment problem with quantitative trading as the background. In order to solve this problem, three types of mathematical models are used in this paper, namely the prediction model, decision model, and risk assessment model. The first is the forecasting model. The paper applies three forecasting models: the grey system Grach (1, 1) forecasting model, the quadratic exponential smoothing forecasting model, and the time series BP-neural network forecasting model. The second is the decision-making model. The decision-making model in the paper is a constrained linear programming model. The objective function is to maximize the total revenue of the day. Finally, there is the risk assessment model. The quantitative investment and multi-factor models are used in the paper to calculate the standard deviation of the rate of return (conventional risk) of gold and Bitcoin respectively in a 30-day cycle, so as to achieve the purpose of quantifying risk, thus reflecting the relationship between gold and Bitcoin. The investment risk index of the two futures products of the currency is provided as a reference for investors. This paper also adjusts the parameters of the prediction model, such as adjusting the value of the number of neurons in the hidden layer of the BP-neural network, to compare the fitting effects corresponding to different parameters, to prove that the prediction model is an optimal solution; Give the decision-making model a certain disturbance, such as changing the definition of the objective function for the total return of the day, to reflect the performance of the forecasting model in dealing with the disturbance of special factors. After that, this paper also conducts a sensitivity analysis of the decision-making model. The specific method is to give a small disturbance to the decision-making model, such as changing its transaction cost, that is, the value of the commission rate a%, recording the final benefit of the decision-making model, and generating a chart to reflect the model. smoothness and sensitivity. Finally, this paper optimizes some models, such as optimizing the BP-neural network model by adaptively adjusting the learning rate and optimizing the linear programming decision model by adding the MACD information factor.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Optimal Solution for the Gold Bitcoin Portfolio Investment Model
AU  - Zihan Yang
AU  - Guanhua Zhang
AU  - Chuming Liu
Y1  - 2023/04/13
PY  - 2023
N1  - https://doi.org/10.11648/j.jfa.20231102.12
DO  - 10.11648/j.jfa.20231102.12
T2  - Journal of Finance and Accounting
JF  - Journal of Finance and Accounting
JO  - Journal of Finance and Accounting
SP  - 49
EP  - 60
PB  - Science Publishing Group
SN  - 2330-7323
UR  - https://doi.org/10.11648/j.jfa.20231102.12
AB  - This topic is a portfolio investment problem with quantitative trading as the background. In order to solve this problem, three types of mathematical models are used in this paper, namely the prediction model, decision model, and risk assessment model. The first is the forecasting model. The paper applies three forecasting models: the grey system Grach (1, 1) forecasting model, the quadratic exponential smoothing forecasting model, and the time series BP-neural network forecasting model. The second is the decision-making model. The decision-making model in the paper is a constrained linear programming model. The objective function is to maximize the total revenue of the day. Finally, there is the risk assessment model. The quantitative investment and multi-factor models are used in the paper to calculate the standard deviation of the rate of return (conventional risk) of gold and Bitcoin respectively in a 30-day cycle, so as to achieve the purpose of quantifying risk, thus reflecting the relationship between gold and Bitcoin. The investment risk index of the two futures products of the currency is provided as a reference for investors. This paper also adjusts the parameters of the prediction model, such as adjusting the value of the number of neurons in the hidden layer of the BP-neural network, to compare the fitting effects corresponding to different parameters, to prove that the prediction model is an optimal solution; Give the decision-making model a certain disturbance, such as changing the definition of the objective function for the total return of the day, to reflect the performance of the forecasting model in dealing with the disturbance of special factors. After that, this paper also conducts a sensitivity analysis of the decision-making model. The specific method is to give a small disturbance to the decision-making model, such as changing its transaction cost, that is, the value of the commission rate a%, recording the final benefit of the decision-making model, and generating a chart to reflect the model. smoothness and sensitivity. Finally, this paper optimizes some models, such as optimizing the BP-neural network model by adaptively adjusting the learning rate and optimizing the linear programming decision model by adding the MACD information factor.
VL  - 11
IS  - 2
ER  -

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