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首页> 外文会议>2014 International Conference on Advances in Engineering and Technology Research >Multiple Kernel Learning for stock price direction prediction
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Multiple Kernel Learning for stock price direction prediction

机译：多重核学习用于股票价格方向预测

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摘要

Unstable and assumptive aspects of the securities makes it hard to predict the next day stock prices. There is no absolute indicator for financial forecasting but there are many technical indicators like simple moving average, exponential moving average, stochastic fast and slow, on balance volume for better accomplishment. It is important to have a significant and well-constructed set of features to elaborate stock trends. In this paper, we have proposed a Multiple Kernel Learning Model which predicts the daily trend of stock prices such as up or down, it comprises of 2-tier framework. In first tier, we extracted some technical indicators based on five raw elements- opening price, daily high price, daily low price, closing price and trading volume. In second tier, we built different base kernels on the extracted feature set and then combined these base kernels through Multiple Kernel learning, we have trained the model through walk forward method and predicted the movement of daily stock trend such as up or down, and then evaluated its performance. Experiment results shows that our proposed solution performs well consistently than baseline methods (Support Vector Machine) in terms of prediction accuracy for two commodities in stock market.
机译：证券的不稳定和假设性使得很难预测第二天的股价。没有用于财务预测的绝对指标，但是有许多技术指标，例如简单的移动平均线，指数移动平均线，随机快速和慢速，平衡量以达到更好的业绩。重要的是要有一组重要且结构良好的功能来阐述库存趋势。在本文中，我们提出了一个多核学习模型，该模型可预测股票价格的每日趋势（例如涨跌），它由2层框架组成。在第一层中，我们基于五个原始要素（开盘价，每日高价，每日低价，收盘价和交易量）提取了一些技术指标。在第二层中，我们基于提取的特征集构建了不同的基础内核，然后通过多核学习将这些基础内核进行组合，我们通过前向方法对模型进行了训练，并预测了每日库存趋势（例如向上或向下）的运动，然后评估其性能。实验结果表明，就股票市场上两种商品的预测准确性而言，我们提出的解决方案与基线方法（支持向量机）的性能一致。

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