论文标题
金融市场趋势预测和使用LSTM的绩效分析
Financial Market Trend Forecasting and Performance Analysis Using LSTM
论文作者
论文摘要
金融市场趋势的预测方法正在当今金融市场中成为一个热门话题。目前仍然存在许多挑战，并且已经积极进行了各种研究。特别是，最近对基于神经网络的金融市场趋势预测的研究引起了很多关注。但是，以前的研究并不涉及基于LSTM的金融市场预测方法，该方法在时间序列数据中具有良好的性能。基于神经网络的预测技术和传统预测技术的性能也缺乏比较分析。在本文中，我们提出了使用LSTM的金融市场趋势预测方法，并通过实验通过现有金融市场趋势预测方法分析绩效。该方法通过数据预处理过程准备了输入数据，以反映财务数据分析中使用的所有基本数据，技术数据和定性数据，并通过LSTM进行全面的金融市场分析。在本文中，我们试验并比较了现有金融市场趋势预测模型的性能，并根据金融市场环境进行绩效。此外，我们使用开放源和平台实施了提出的方法，并使用各种财务数据指标预测金融市场趋势。
The financial market trend forecasting method is emerging as a hot topic in financial markets today. Many challenges still currently remain, and various researches related thereto have been actively conducted. Especially, recent research of neural network-based financial market trend prediction has attracted much attention. However, previous researches do not deal with the financial market forecasting method based on LSTM which has good performance in time series data. There is also a lack of comparative analysis in the performance of neural network-based prediction techniques and traditional prediction techniques. In this paper, we propose a financial market trend forecasting method using LSTM and analyze the performance with existing financial market trend forecasting methods through experiments. This method prepares the input data set through the data preprocessing process so as to reflect all the fundamental data, technical data and qualitative data used in the financial data analysis, and makes comprehensive financial market analysis through LSTM. In this paper, we experiment and compare performances of existing financial market trend forecasting models, and performance according to the financial market environment. In addition, we implement the proposed method using open sources and platform and forecast financial market trends using various financial data indicators.
