英文标题：
《Mid-price Prediction Based on Machine Learning Methods with Technical
  and Quantitative Indicators》
---
作者：
Adamantios Ntakaris, Juho Kanniainen, Moncef Gabbouj, Alexandros
  Iosifidis
---
最新提交年份：
2019
---
英文摘要：
  Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a new quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few advanced hand-crafted features.
---
中文摘要：
股票价格预测是一项具有挑战性的任务，但机器学习方法最近已成功用于此目的。在本文中，我们从技术和定量分析中提取了270多个手工特征（因素），并在短期中期价格变动预测中检验了它们的有效性。我们重点讨论了一种使用熵、最小均方和线性判别分析的包装特征选择方法。我们还为在线学习构建了一个新的基于自适应logistic回归的定量特征，该特征在大多数提出的特征选择方法中始终处于第一位。本研究使用纳斯达克北欧证券交易所的高频限价指令簿数据来检验特征的最佳组合。我们的结果表明，排序方法和分类器的使用方式可以使您只需结合极少数高级手工功能即可达到最佳性能。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
PDF下载：
-->