Author(s)

Yue Fu ¹

Affiliation(s)

¹ College of Mining, China University of Mining of Technology, Xuzhou, 221116, China

Corresponding Author

Yue Fu

ABSTRACT

Depending on the data of recent years, it is understood that Bitcoin and gold are finan-cial products worth looking forward to receiving. How to make the right choice between Bitcoin, gold, and continue to hold cash at the right time node at the right time and with the right funds to achieve the greatest economic benefits is the most important research object for the establishment of the mathematical model. For Model I, We establish a price prediction model based on GM (1,1). Firstly, we conduct a quasi-exponential test on the prices of gold and bitcoin. After the test, we estab-lish a GM (1,1) grey differential model and combine it with Metabolism GM (1,1) to predict the future trading prices of gold and bitcoin by using MATLAB. Then according to the actual transaction price, the residuals are calculated to quantify the financial risk by Ljung-Box Q test and LM statistical test. Finally, it is found that the prediction results of gold and bitcoin are ' no sequence correlation ' and there are GARCH errors. For Model II, We establish a strategy making model based on genetic algorithm. We first consider using Trend tracking strategy to make decisions. Since the trend tracking strategy is difficult to determine the trend, only using the past results to make decisions, we consider the combination of the previous prediction results, using genetic algorithm to op-timize the trend tracking strategy. Finally, it is concluded that from September 11,2016 to September 10,2021, the value of up to $ 75,000 cans be generated on the basis of $ 1000, and with further iteration no long-er change, the optimal value is achieved. In addition, We provide evidence from Model I and Model II to prove that we have given the highest economic value. For Model I, we add some disturbances to the actual price of gold and bitcoin and then make multiple predictions and error analysis, and the ac-curacy of the final prediction is not greatly affected. For Model II, we add some disturb-ances to the original optimal decision scheme to calculate the investment value, and find that the investment value of the disturbed scheme is lower than that before the disturbance. Eventually, We conducted a sensitivity analysis of Model I and Model II and found that the number of gold transactions decreased significantly as transaction costs increased, but bitcoin transactions were not significantly affected.

KEYWORDS

GM (1,1), Trend tracking strategy, genetic algorithm, sensitivity analysis

CITE THIS PAPER

Yue Fu, GM ( 1,1 ) and Quantitative Trading Decision Model. Financial Engineering and Risk Management (2022) Vol. 5: 66-83. DOI: http://dx.doi.org/10.23977/ferm.2022.050410.

REFERENCES

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[2] Misund, O. A., Vilhjálmsson, H., Jákupsstovu, S. H. Í., Røttingen, I., Belikov, S., Asthorsson, O., & Sveinbjórnsson, S. (1998). Distribution, migration and abundance of Norwegian spring spawning herring in relation to the temperature and zooplankton biomass in the Norwegian Sea as recorded by coordinated surveys in spring and summer 1996. Sarsia, 83(2), 117-127.
[3] Peer, A. C., & Miller, T. J. (2014). Climate change, migration phenology, and fisheries management interact with unanticipated consequences. North American.

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