  The financial time series analysis is important access to touch the complex
laws of financial markets. Among many goals of the financial time series
analysis, one is to construct a model that can extract the information of the
future return out of the known historical stock data, such as stock price,
financial news, and e.t.c. To design such a model, prior knowledge on how the
future return is correlated with the historical stock prices is needed. In this
work, we focus on the issue: in what mode the future return is correlated with
the historical stock prices. We manually design several financial time series
where the future return is correlated with the historical stock prices in
pre-designed modes, namely the curve-shape-feature (CSF) and the
non-curve-shape-feature (NCSF) modes. In the CSF mode, the future return can be
extracted from the curve shapes of the historical stock prices. By applying
various kinds of existing algorithms on those pre-designed time series and real
financial time series, we show that: (1) the major information of the future
return is not contained in the curve-shape features of historical stock prices.
That is, the future return is not mainly correlated with the historical stock
prices in the CSF mode. (2) Various kinds of existing machine learning
algorithms are good at extracting the curveshape features in the historical
stock prices and thus are inappropriate for financial time series analysis
although they are successful in the image recognition and natural language
processing. That is, new models handling the NCSF series are needed in the
financial time series analysis.
