Prodromos E. Tsinaslanidis

A novel, rule-based technical pattern identification mechanism: Identifying and evaluating saucers and resistant levels in the US stock market

This paper has two main purposes. The first one is the development of a rigorous rule-based mechanism for identifying the rounding bottoms (also known as saucers) pattern and resistant levels. The design of this model is based solely on principles of technical analysis, and thus making it a proper system for evaluating the efficacy of the aforementioned technical trading patterns. The second aim of this paper is measuring the predictive power of buy-signals generated by these technical patterns. Empirical results obtained from seven US tech stocks indicate that simple resistant levels outperform saucers patterns. Furthermore, positive statistical significant excess returns are being generated only in first sub-periods of examination. These returns decline or even vanish as the experiment proceeds to recent years. Our findings are aligned with the results reported by various former studies. The proposed identification mechanism can be used as a component of an expert system to assist academic community in evaluating trading strategies where technical patterns are embedded.

A prediction scheme using perceptually important points and dynamic time warping

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price.

Identification of the head-and-shoulders technical analysis pattern with neural networks

In this paper we present a novel approach for identifying the headand-shoulders technical analysis pattern based on neural networks. For training the network we use actual patterns that were identified in stochastically simulated price series by means of a rule-based algorithm. Then the patterns are being converted to binary images, in a manner similar to the one used in handwritten character and digit recognition. Our approach is tested on new simulated price series using a rolling window of variable size. The results are very promising with an overall correct classification rate of 97.1%.

Identifying and evaluating horizontal support and resistance levels: an empirical study on US stock markets

We propose a novel rule-based mechanism that identifies Horizontal Support And Resistance (HSAR) levels. The novelty of this system resides in the manner it encloses principles, found in well known technical manuals, used for the identification via visual assessment. The drawing of these levels derives from historical locals, rather than denoting support (resistance) levels from the lowest (highest) price levels of precedent constant time intervals. We further proceed in evaluating whether these levels are efficient trend-reversal predictors, and if they can generate systematic abnormal returns. The dataset used includes adjusted for dividends and splits, daily closing prices of stocks listed on National Association of Securities Dealers Automated Quotation (NASDAQ) and New York Stock Exchange (NYSE) for the last 2 decades. Our results are aligned with the efficient market hypothesis. More concretely, support levels outperform resistance ones in predicting trend interruptions but they fail to generate excess returns when they are compared with simple buy-and-hold strategies.

Subsequence dynamic time warping for charting: Bullish and bearish class predictions for NYSE stocks

Advanced pattern recognition algorithms have been historically designed in order to mitigate the problem of subjectivity that characterises technical analysis (also known as ‘charting’). However, although such methods allow to approach technical analysis scientifically, they mainly focus on automating the identification of specific technical patterns. In this paper, we approach the assessment of charting from a more generic point of view, by proposing an algorithmic approach using mainly the dynamic time warping (DTW) algorithm and two of its modifications; subsequence DTW and derivative DTW. Our method captures common characteristics of the entire family of technical patterns and is free of technical descriptions and/or guidelines for the identification of specific technical patterns. The algorithm assigns bullish and bearish classes to a set of query patterns by looking the price behaviour that follows the realisation of similar, in terms of price and volume, historical subsequences to these queries. A large number of stocks listed on NYSE from 2006 to 2015 is considered to statistically evaluate the ability of the algorithm to predict classes and resulting maximum potential profits within a test period that spans from 2010 to 2015. We find statistically significant bearish class predictions that generate on average significant maximum potential profits. However, bullish performance measures are not significant.

A Statistical Assessment

This chapter examines the performance of TA, by implementing all technical patterns and indicators presented previous chapters, on 18 financial market indices around the world, for the requested period of 1998–2014. Parameters used for defining these trading rules are excerpted from the literature, and are the most commonly used. The methodology applied includes ordinary statistical tests and a bootstrap assessment.

Technical Analysis for Algorithmic Pattern Recognition

The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an economic test of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes.

Assessing the Predictive Performance of Technical Analysis

This chapter presents some of the celebrated means by which the predictive performance of a technical trading system or a particular technical tool can be assessed. Although not all of these procedures are used in the subsequent chapters, we believe that they are important basic tools for anyone who wishes to assess the performance of such trading systems.

Dynamic Time Warping for Pattern Recognition

This chapter presents a Dynamic Time Warping (DTW) algorithmic process to identify similar patterns on a price series. This methodology initially became popular in applications of voice recognition, and it is not considered to be included within the context of TA. In this chapter our analysis on technical pattern recognition processes is extended, by presenting an alternative methodology, which combines two main modifications of the DTW algorithmic process; the Derivative DTW and the subsequence DTW. We believe that this methodology captures the same conventions of technical patterns; that history is repeated, forming patterns which may vary in length. With this chapter we intend to inspire the reader to look for alternative quantitative techniques for recognizing similar patterns on financial price series, beyond those presented within the context of technical analysis.