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Portfolio Research Based on SVM-GARCH and Dynamic Weighted Multi-Objective Planning Models—An Example of Gold and Bitcoin

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DOI: 10.23977/ferm.2023.060913 | Downloads: 20 | Views: 375

Author(s)

Yue Wu 1

Affiliation(s)

1 Hunan University, Changsha, 410082, China

Corresponding Author

Yue Wu

ABSTRACT

The question of how to benefit from an organic combination of gold and bitcoin has become a prominent topic in the contemporary society. Hence, we've built the time series forecasting models and target planning models of gold and bitcoin, providing the best gold and bitcoin rotation investing strategy based on our methodology. We consider the connection between gold and bitcoin price fluctuations by creating the SVM-GARCH Combination Model, and at the same time, data-based nonlinear feature extraction and heteroscedasticity processing give a more accurate and dependable foundation for investment decision making.In terms of investment planning, We first utilized VaR to clarify our quantitative investment risk indicators, and then built a VaRY Model to organically integrate and balance investment returns and risks. At the same time, we include Risk Adjustment Parameters into the planning model so that, by dynamic weight adjustment, our target planning model can match the wealth utility propensity of investors with diverse risk preferences, therefore improving the model's application and flexibility. Finally, in view of the differences in trading restrictions between Trading Days and Non-trading Days, we formulate different dynamic weights - Multi-objective Programming Models for trading and non trading periods, so that our best investment decision can be more comprehensive and targeted.We present proof for the brilliance of our investment strategy in four dimensions by merging and assessing the forecasting model and the planning model: Accuracy, Rationality, Flexibility, and High Return.

KEYWORDS

Investment Strategy, Risk Appetite, SVM-GARCH, Dynamic Weights, Multi-Objective Programming

CITE THIS PAPER

Yue Wu, Portfolio Research Based on SVM-GARCH and Dynamic Weighted Multi-Objective Planning Models—An Example of Gold and Bitcoin. Financial Engineering and Risk Management (2023) Vol. 6: 93-106. DOI: http://dx.doi.org/10.23977/ferm.2023.060913.

REFERENCES

[1] Hu Qinghua, Zhang Lei, Zhang D, et al. Measuring relevance between discrete and (continuous features based on neighborhood mutual information [J]. Expert Systems with Applications, 2011, 38(9): 10737-10750.
[2] Liu Xiaoxing. Research on Risk Management of Commercial Banks Based on VaR [D]. Southeast University, 2005.
[3] Alexander G J. Economic implication of using a mean-VaR model for portfolio selection: A comparison with mean-variance analysis [J]. Journal of Economic Dynamics and Control, 2002, 26: 1159~1193.
[4] Kim K. Financial time series forecasting using supportvector machines [J]. Neurocomputing, 2003,55(1-2): 307-319
[5] Markowitz H. Portfolio Selection [J]. Journal of Finance, 1952, 7(1):77-91.
[6] Sharpe W. Capital Assets Prices: A Theory of Market Equibrium Under Conditions of Risk [J]. The Journal of Finance, 1964, 19(3): 425-442. 
[7] Ross A. The Arbitrage Theory of Capital Asset Pricing [J]. Journal of Economic Theory, 1976,13(3): 341-360. 
[8] Rubinstein M.Markowitz's "Portfolio  Selection": A fifty year restropective [J]. Journal of Finance, 2002, 57: 1041~1045. 
[9] Sharp W. Capital asset prices: A theory of market equilibrium under conditions of risk [J]. Journal of Finance, 1964, 19: 425~442.

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