Short Term Forecast Of Gold Futures Price And Evaluation Of Trading Effect Based On Multi-dimensional Information

Gold is one of the most important commodities in the financial market.It is a special precious metal that combines both currency and financial attributes,while possessing the characteristics of high liquidity,high security,and low yield.The change in gold futures prices is influenced by various factors,such as supply and demand,global economic conditions,national policies,exchange rates,stock market indices,and crude oil prices.The fluctuation of gold futures prices is quite complex,and the interference of unexpected events makes gold futures price prediction increasingly challenging.Traditional prediction methods are difficult to meet the requirements for short-term prediction of gold futures prices in the current environment.Therefore,this thesis analyzes the gold futures prices and their influencing factors from the perspectives of feature selection and feature fusion,aiming to explore new methods to improve the accuracy of gold futures price prediction.The main work is as follows:Regarding feature selection,the elastic net is first used to select and reduce the dimensionality of the Chinese gold futures and their influencing factors.The phase space reconstruction method and time-lagged correlation analysis method are used to determine the lag period of the gold futures themselves and the lag period of the compressed variables,respectively.In terms of prediction models,based on the BP,ELM,and LSSVR models,the particle swarm optimization algorithm(PSO)and gray wolf optimization algorithm(GWO)are used to optimize the basic model parameters and conduct sliding window prediction.The prediction results show that the GWO-ELM model has a faster optimization speed and robustness compared to other models.To improve the directional accuracy of gold futures price prediction,the already selected GWO-ELM model is further optimized by setting an adaptive function that combines directional and horizontal accuracy to train the neural network model.The results show that the prediction model with the modified fitness function has better directional accuracy in both one-step and multi-step prediction than the original GWO-ELM model.Finally,different trading strategies are designed to backtest the experimental results of one-step and multi-step prediction,and relevant indicators such as strategy yield are used to further verify the good prediction performance of the modified fitness function GWO-ELM model.Regarding feature fusion,not only the impact of gold futures prices and market factors is considered,but also the investor attention measurement index,namely the Baidu search index dataset,is included in the influencing factors.The Pearson correlation coefficient,MIV,and KPCA methods are used to screen and fuse the mixed data,and the information contribution rate is compared and analyzed to determine the optimal threshold for MIV and KPCA.The GWO-ELM model with strong robustness is selected for one-step prediction,and the prediction effects of feature extraction methods are compared for the mixed dataset.It is found that feature extraction methods are more suitable for predicting gold futures prices in multi-dimensional information.