论文标题
EB-DYNARE：布朗运动的实时调节器，其中基于新型基于事件的监督学习算法来预测股票趋势的示例
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm
论文作者
论文摘要
股价随着时间的推移而受到宏观经济因素的影响。我们跳出了关于市场噪音不可预测性的常规假设的盒子，我们通过马尔可夫决策过程对股票价格的变化进行了建模，马尔可夫决策过程是一个离散的随机控制过程，在部分随机的情况下有助于决策。然后，我们进行了“感兴趣的区域”（ROI）汇总库存时间序列图，以预测现有价格的未来价格。然后，基于一对竞争性的监督学习算法，使用生成对抗网络（GAN），以实时再生未来的股票价格预测。此外，这项研究中使用的监督学习算法是本研究的原始方法，并且将具有更广泛的用途。通过这些算法的合奏，我们能够确定每个特定的宏观经济因素在多大程度上影响布朗/随机市场运动的变化。此外，我们的模型将对其他布朗运动的预测产生更大的影响。
Stock prices are influenced over time by underlying macroeconomic factors. Jumping out of the box of conventional assumptions about the unpredictability of the market noise, we modeled the changes of stock prices over time through the Markov Decision Process, a discrete stochastic control process that aids decision making in a situation that is partly random. We then did a "Region of Interest" (RoI) Pooling of the stock time-series graphs in order to predict future prices with existing ones. Generative Adversarial Network (GAN) is then used based on a competing pair of supervised learning algorithms, to regenerate future stock price projections on a real-time basis. The supervised learning algorithm used in this research, moreover, is original to this study and will have wider uses. With the ensemble of these algorithms, we are able to identify, to what extent, each specific macroeconomic factor influences the change of the Brownian/random market movement. In addition, our model will have a wider influence on the predictions of other Brownian movements.
