金融时间序列指标判别框架：以特质波动率为例
A Discrimination framework for Financial Time Series Indices based on Inflection Points Set: A Idiosyncratic Volatility Case

基于拐点集合判别的TBUD方法主要思路是分析拐点集合间的关系，并在高维空间进行划分，从而搭建判别模型，并将分析框架应用在特质波动率等若干指标上，利用实证数据得到结论。应用TBUD判别框架可以发现，特质波动率等指标无法对拐点集合进行清晰划分，因而并不具有预测能力。
This paper presents a new method--TBUD to partition the inflection points into collections for time series of the stock price. Analyzing the relation among the collections, this paper builds a discrimination framework which is applied to Idiosyncratic Volatility, as a case. The result suggests that Idiosyncratic Volatility can not be divided the inflection points set and is therefore unable to make an accurate prediction on the future trends of the stock price.