Statistics and Its Interface

Volume 11 (2018)

Number 3

Discovering stock chart patterns by statistical estimation and inference

Pages: 441 – 453



Hoang Tran (Department of Statistics, Florida State University, Tallahassee, Fl., U.S.A.)

Yiyuan She (Department of Statistics, Florida State University, Tallahassee, Fl., U.S.A.)


Statistical modeling of stock price data is challenging due to heteroskedasticity, heavy-tails and outliers. These issues can be particularly relevant to the technical analysis practitioner who extracts trading signals from geometric patterns in prices. In this work, we propose a new method called Non-Parametric Outlier Identification and Smoothing (NOIS), which robustly smooths stock prices, automatically detects outliers and constructs pointwise confidence bands around the resulting curves. In real-world examples of high-frequency data, NOIS successfully detects erroneous prices as outliers and uncovers borderline cases for further study. NOIS can also highlight notable features and reveal new insights in inter-day chart patterns.


outlier detection, confidence bands, technical analysis, high-frequency data

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This research was supported in part by NSF grants DMS-1352259 and CCF-1617801.

Received 20 April 2017