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Forecasting Financial Market Trading Behavior by Physical and Market Profiles

机译:通过实物和市场概况预测金融市场交易行为

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This study applies the back-propagation neural network (BPNN) 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 Taiwan capitalization weighted stock index (TAIEX) futures market logic and knowledge rules. This study compares the experimental group and random trading. The experimental results show the proposed model obtains more profit than random trading. Therefore, integration of market profile and technical analysis can effectively improve forecasting performance and profitability.
机译:本研究应用反向传播神经网络(BPNN)计算随机(KD),移动平均趋同散度(MACD),资金流量指数(MFI),价值区域旋转因子(VARF)和定量市场概况数据以推断台湾市值加权股指(TAIEX)期货市场逻辑和知识规则。本研究比较了实验组和随机交易。实验结果表明,提出的模型比随机交易获得更多的利润。因此,将市场概况和技术分析相结合可以有效地提高预测绩效和盈利能力。

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