英文标题：
《Multiple-output support vector regression with a firefly algorithm for
  interval-valued stock price index forecasting》
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作者：
Tao Xiong, Yukun Bao, Zhongyi Hu
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最新提交年份：
2014
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英文摘要：
  Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furthermore, this study proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally traded broad market indices are used to compare the performance of the proposed FA-MSVR method with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of statistical criteria, economic criteria, and computational cost. In terms of statistical criteria, we compare the out-of-sample forecasting using goodness-of-forecast measures and testing approaches. In terms of economic criteria, we assess the relative forecast performance with a simple trading strategy. The results obtained in this study indicate that the proposed FA-MSVR method is a promising alternative for forecasting interval-valued financial time series.
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中文摘要：
股票价格指数的高精度区间预测是在做出投资决策时成功盈利的基础，它提供了一系列价值，而不是一个点估计。在这项研究中，我们研究了使用多输出支持向量回归（MSVR）预测短期和长期区间价值股票价格指数序列的可能性。此外，本研究提出了一种基于萤火虫算法（FA）的方法，建立在已建立的MSVR基础上，用于确定MSVR的参数（简称FA-MSVR）。三个全球交易的大盘指数用于比较拟议的FA-MSVR方法与选定对应方法的性能。根据统计标准、经济标准和计算成本进行定量和综合评估。在统计标准方面，我们使用预测优度度量和测试方法比较了样本外预测。在经济标准方面，我们通过简单的交易策略来评估相对预测绩效。本研究的结果表明，提出的FA-MSVR方法是预测区间值金融时间序列的一种很有前途的替代方法。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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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。
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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
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