Research the Financial Networking Market Trading Behavior and Physics

Due to deep find-out the financial market trading behavior and physics. This study applies the back-propagation neural network (BPNN) and grey system methods to compute stochastic (KD), moving average convergence-divergence (MACD), money flow index (MFI), value area rotation factor (VARF) and quantitative market profile data to extrapolate the market logic, and knowledge rules that influence the Taiwan capitalization weighted stock index (TAIEX) futures networking market structure via an integral physical quantities assessment. The experimental results show that using the proposed model to measure the momentum of value area over the past few days improves the prediction model accuracy and profit. This implies that the momentum trend over the past few days is important for predicting the price direction in one day. This study compares the experimental group with random trading and finds the proposed model obtains more profit than random trading. Therefore, financial networking market trading behavior patterns are not random.