Trong bài báo này, chúng tôi đề xuất một phương pháp tiếp cận có hệ thống và tự động nhận dạng mẫu kỹ thuật bằng cách sử dụng hồi quy hạt nhân nonparametric, và chúng tôi áp dụng phương pháp này để một số lượng lớn các chứng khoán Mỹ 1962-1996 để đánh giá hiệu quả của phân tích kỹ thuật. Bằng cách so sánh phân phối của vô điều kiện thực nghiệm chứng khoán hàng ngày | Foundations of Technical Analysis 1715 Another promising direction for future research is to consider alternatives to kernel regression. Although kernel regression is useful for its simplicity and intuitive appeal kernel estimators suffer from a number of well-known deficiencies for instance boundary bias lack of local variability in the degree of smoothing and so on. A popular alternative that overcomes these particular deficiencies is local polynomial regression in which local averaging of polynomials is performed to obtain an estimator of m x .6 Such alternatives may yield important improvements in the pattern-recognition algorithm described in Section II. II. Automating Technical Analysis Armed with a mathematical representation m of Pt with which geometric properties can be characterized in an objective manner we can now construct an algorithm for automating the detection of technical patterns. Specifically our algorithm contains three steps 1. Def ine each technical pattern in terms of its geometric properties for example local extrema maxima and minima . 2. Construct a kernel estimator m of a given time series of prices so that its extrema can be determined numerically. 3. Analyze m for occurrences of each technical pattern. The last two steps are rather straightforward applications of kernel regression. The f irst step is likely to be the most controversial because it is here that the skills and judgment of a professional technical analyst come into play. Although we will argue in Section that most technical indicators can be characterized by specific sequences of local extrema technical analysts may argue that these are poor approximations to the kinds of patterns that trained human analysts can identify. While pattern-recognition techniques have been successful in automating a number of tasks previously considered to be uniquely human endeavors fingerprint identification handwriting analysis face recognition and so on nevertheless it is possible that
