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The financial time series analysis is important access to touch the complexlaws of financial markets. Among many goals of the financial time seriesanalysis, one is to construct a model that can extract the information of thefuture 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 thefuture return is correlated with the historical stock prices is needed. In thiswork, we focus on the issue: in what mode the future return is correlated withthe historical stock prices. We manually design several financial time serieswhere the future return is correlated with the historical stock prices inpre-designed modes, namely the curve-shape-feature (CSF) and thenon-curve-shape-feature (NCSF) modes. In the CSF mode, the future return can beextracted from the curve shapes of the historical stock prices. By applyingvarious kinds of existing algorithms on those pre-designed time series and realfinancial time series, we show that: (1) the major information of the futurereturn is not contained in the curve-shape features of historical stock prices.That is, the future return is not mainly correlated with the historical stockprices in the CSF mode. (2) Various kinds of existing machine learningalgorithms are good at extracting the curveshape features in the historicalstock prices and thus are inappropriate for financial time series analysisalthough they are successful in the image recognition and natural languageprocessing. That is, new models handling the NCSF series are needed in thefinancial time series analysis.

Why Existing Machine Learning Methods Fails At Extracting the  Information of Future Returns Out of Historical Sctock Prices : the  Curve-Shape-Feature and Non-Curve-Shape-Feature Modes