Stock price forecasting over adaptive timescale using supervised learning and receptive fields

Pattern recognition in financial time series is not a trivial task, due to level of noise, volatile context, lack of formal definitions and high number of pattern variants. A current research trend involves machine learning techniques and online computing. However, medium-term trading is still based on human centric heuristics, and the integration with machine learning support remains relatively unexplored. The purpose of this study is to investigate potential and perspectives of a novel architectural topology providing modularity, scalability and personalization capabilities. The proposed architecture is based on the concept of Receptive Fields (RF), i.e., sub-modules focusing on specific patterns, that can be connected to further levels of processing to analyze the price dynamics on different granularities and different abstraction levels. Both Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) have been experimented as a RF. Early experiments have been carried out over the FTSEMIB index

An examination of the head and shoulders technical pattern; A support of the technical analysis’s subjective nature

It is being argued that Head and Shoulders technical price pattern although it occurs seldom it is worth to trust. In this Paper we use the Brownian motion to generate time series of different characteristics (drift, volatility) and we apply on them a matlab script for the identification of the head and shoulders pattern. After ensuring that the aforementioned technical pattern is being identified in random generated time series, we look for the profitability of the pattern through three different trading strategies. In a mechanism, like Brownian motion, of producing time series in a “random” way we should not expect to be able to forecast the further evolution of the price. On the beginning our results show that we come up to a fair game, which means that we cannot profit from the strategies used as a whole. Examining each strategy separately, looking at the way the variables, drift and volatility rate, affect the returns, and after taking into account general rules and principles of TA, the question we bring into discussion is whether investors selectively focus on specific profitable cases of the Head and Shoulders pattern. This results in the misleading conclusion that the pattern is seldom observed but profitable. Is it possible that the pattern is being identified more often having no predictive power at all

A behavioral view of the head-and-shoulders technical analysis pattern

The technical analysis approach to predicting stock returns is based on the identification of recurrent patterns in the way stock prices evolve in time, (a) using optical examination of price history, and (b) a host of technical indicators. Optical examination of price history materialises as the recognition of certain patterns that supposedly have predictive power. “Head-and-Shoulders” (H&S) is a well known technical price pattern, which is said to occur seldom but with high predictive power. In this paper we examine whether or not the H&S pattern can be identified in price series generated stochastically. A rule-based mechanism for the identification of the pattern is applied on half a million price series generated stochastically with the use of the Geometric Brownian Motion and with different combinations of volatility and drift rate. We find that the H&S pattern can be identified almost 11 times out of 100 in randomly generated price series. However, the expected payoff for a short-long strategy including transaction costs is negative (as expected). After examining the characteristics of the pattern (frequency of occurrence, profitability, and so on) we look with a behavioral view the results of our simulation. The question we bring into discussion is whether investors selectively focus on specific profitable cases of the pattern. This results in the misleading conclusion that the pattern is seldom observed but profitable. According to our findings it is possible that the pattern is being identified more often having no predictive power at all. Various cognitive biases affect the way investors make decisions and result in the aforementioned misleading conclusion. This is confirmed by the answers that graduate students from the University of Macedonia gave at a questionnaire which was set based on the results of our simulation

Head and shoulders pattern recognition in stochastic processes

Technical analysis is the process of analyzing a security's historical prices in an effort to determine probable future prices. On the other hand, in the context of Markov Processes (e.g. Wiener, generalized Ito etc) that form the basis of financial markets theory the best estimation for future’s price is current price, and the path that a stock price followed in the past is irrelevant to the future. The second concept is consistent with the weak form of Efficient Market Hypothesis where the first is not. In this paper we examine if technical analysis can predict the unpredictable. Particularly we apply an automatic rule-based mechanism which can identify the Head and Shoulder pattern on a stochastic process, and we examine firstly if the aforementioned pattern occurs, and secondly when the pattern occurs, what is the price’s future behavior

Testing the generalised efficacy of technical analysis with bootstrapped aggregated regression trees

In this paper we examine the predictive power of the combined use of 23 known technical patterns and indicators, on 25 of the world’s most famous market indices, over the last decade. The system implemented for the combination of the above tools is bootstrapped aggregated regression trees, which is an ensemble nonparametric and nonlinear method and allows us to use numerical and categorical input variables simultaneously. Indications of inefficiencies are found, but their magnitude is not sufficient in order to characterise the aforementioned markets as weak form inefficient. In contrast, our overall conclusion suggests that technical analysis might marginally contribute in the interpretation of the manner that returns are evolved

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

In this paper we present a novel approach for identifying the head-and-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 hand-written 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%

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