Patterns In Financial Time Series Research Articles

Estimating the Likelihood of Financial Behaviours Using Nearest Neighbors

As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts’ efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.

Feature extraction for chart pattern classification in financial time series

Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related features are widely used for chart pattern matching in financial time series, the intrinsic properties of these features and their relationships to the patterns are rarely investigated in research community. This paper aims to formally identify shape-related features used in chart patterns and investigates their impact on chart pattern classifications in financial time series. In this paper, we describe a comprehensive analysis of 14 shape-related features which can be used to classify 41 known chart patterns in technical analysis domain. In order to evaluate their effectiveness, shape-related features are then translated into rules for chart pattern classification. We perform extensive experiments on real datasets containing historical price data of 24 stocks/indices to analyze the effectiveness of the rules. Experimental results reveal that the features put forward in this paper can be effectively used for recognizing chart patterns in financial time series. Our analysis also reveals that high-level features can be hierarchically composed from low-level features. Hierarchical composition allows construction of complex chart patterns from features identified in this paper. We hope that the features identified in this paper can be used as a reference model for the future research in chart pattern analysis.

Thresholded ConvNet ensembles: neural networks for technical forecasting

Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of ‘domain knowledge’. We investigate the predictive value of patterns in financial time series, applying machine learning and signal processing techniques to 22 years of US equity data. By reframing technical analysis as a poorly specified, arbitrarily preset feature-extractive layer in a deep neural network, we show that better convolutional filters can be learned directly from the data, and provide visual representations of the features being identified. We find that an ensemble of shallow, thresholded convolutional neural networks optimised over different resolutions achieves state-of-the-art performance on this domain, outperforming technical methods while retaining some of their interpretability.

Design of an Artificial Neural Network battery for an optimal recognition of patterns in financial time series

The increasingly massive use of advanced Machine Learning methodologies in the financial field sector has led credit institutions to quickly move to new FinTech technologies. This paper deals with how a battery of Artificial Neural Networks (ANN), dedicated to the automatic recognition of financial patterns of potential interest to traders, can be designed and validated. The battery of neural networks that have been designed is composed of a shallow ANN, a deep ANN with ReLu, a deep ANN with Dropout and a convolutional network (ConvNet). Depending on the type of classification problem, the ANN battery dynamically recognizes the best classifier and makes use of it for pattern recognition. The first part of the paper describes how these technologies work, the second one performs a validation of the code and the third one suggests a technical analysis application on financial time series.

A Language for Financial Chart Patterns

In stock markets around the world, financial analysts continuously monitor and screen chart patterns (technical patterns) to predict future price trends. Although a plethora of methods have been proposed for classification of these patterns, there is no uniform standard in defining their shapes. To facilitate the classification and discovery of chart patterns in financial time series, we propose a novel domain-specific language called “Financial Chart Pattern Language” (FCPL). The proposed language is formally described in Extended Backus–Naur Form (EBNF). FCPL allows incremental composition of complex shapes from simple basic units called primitive shapes. Hence, patterns defined in FCPL can be reused for composing new chart patterns. FCPL separates the specification of a chart pattern from the mechanism of its implementation. Due to its simplicity, FCPL can be used by stock market experts and end users to describe the patterns without programming expertise. To highlight its capabilities, several representative financial chart patterns are defined in FCPL for illustration. In the experiments, we classify several representative chart patterns from the datasets of HANG SENG INDEX (HSI), NYSE AMEX COMPOSITE INDEX (NYSE), and Dow Jones Industrial Average (DJI).

Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation

Financial time series represent the stock prices over time and exhibit behavior similar to a data stream. Many works report on the use of data mining techniques to predict the future direction of stock prices and to discover patterns in the time series data to provide decision support for trading operations. Traditional optimization methods do not take into account the possibility that the function to be optimized, namely, the final financial balance for operations considering some stock, may have multiple peaks, i.e., be represented by multimodal functions. However, multimodality is a known feature of real-world financial time series optimization problems. To deal with this issue, this article proposes the PAA-MS-IDPSO-V approach (Piecewise Aggregate Approximation - Multi-Swarm of Improved Self-adaptive Particle Swarm Optimization with Validation). The proposed method aims to find patterns in financial time series to support investment decisions. The approach uses multi-swarms to obtain a better particle initialization for the final optimization phase since it aims to tackle multimodal problems. Furthermore, it uses a validation set with early stopping to avoid overfitting. The patterns discovered by the method are used together with investment rules to support decisions and thus help investors to maximize the profit in their operations in the stock market. The experiments reported in this paper compare the results obtained by the proposed model with the Buy-and-Hold, PAA-IDPSO approaches and another approach found in the literature. We report on experiments conducted with S&P100 index stocks and using the Friedman Non-Parametric Test with the Nemenyi post-hoc Test both with 95% confidence level. The results show that the proposed model outperformed the competing methods and was able to considerably reduce the variance for all stocks.

Adaptive neuro fuzzy inference system for chart pattern matching in financial time series

In technical analysis, the appearance of chart patterns in financial time series is considered as one of the crucial signals in predicting future price trend. In recent years, various classification methods have been proposed by researchers to locate and identify potential chart patterns from input time series. This paper presents a novel application of adaptive neuro fuzzy inference system (ANFIS) for chart pattern matching in financial time series. The construction of ANFIS for chart patterns is described in the paper. In addition, we propose a method to determine the thresholds to implement chart patterns matching with the trained ANFIS model. The effectiveness and efficiency of the ANFIS model are compared with six pattern matching approaches on both synthetic datasets and real datasets. Experimental results reveal that the ANFIS model is effective in classifying different chart patterns when compared with other pattern matching approaches.

Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm

The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes a local search method aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.

Financial time series pattern matching with extended UCR Suite and Support Vector Machine

Chart patterns are frequently used by financial analysts for predicting price trends in stock markets. Identifying chart patterns from historical price data can be regarded as a subsequence pattern-matching problem in financial time series data mining. A two-phase method is commonly used for subsequence pattern-matching, which includes segmentation of the time series and similarity calculation between subsequences and the template patterns. In this paper, we propose a novel approach for locating chart patterns in financial time series. In this approach, we extend the subsequence search algorithm UCR Suite with a Support Vector Machine (SVM) to train a classifier for chart pattern-matching. The experimental results show that our approach has achieved significant improvement over other methods in terms of speed and accuracy.

Characteristics of the transmission of autoregressive sub-patterns in financial time series.

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

Permutation approach, high frequency trading and variety of micro patterns in financial time series

Permutation approach is suggested as a method to investigate financial time series in micro scales. The method is used to see how high frequency trading in recent years has affected the micro patterns which may be seen in financial time series. Tick to tick exchange rates are considered as examples. It is seen that variety of patterns evolve through time; and that the scale over which the target markets have no dominant patterns, have decreased steadily over time with the emergence of higher frequency trading.

OBST-based segmentation approach to financial time series

Financial time series data are large in size and dynamic and non-linear in nature. Segmentation is often performed as a pre-processing step for locating technical patterns in financial time series. In this paper, we propose a segmentation method based on Turning Points (TPs). The proposed method selects TPs from the financial time series in question based on their degree of importance. A TP's degree of importance is calculated on the basis of its contribution to the preservation of the trends and shape of the time series. Algorithms are also devised to store the selected TPs in an Optimal Binary Search Tree (OBST) and to reconstruct the reduced sample time series. Comparison with existing approaches show that the time series reconstructed by the proposed method is able to maintain the shape of the original time series very well and preserve more trends. Our approach also ensures that the average retrieval cost is kept at a minimum.

Asymmetric dependence patterns in financial time series

This article proposes a new copula-based approach to test for asymmetries in the dependence structure of financial time series. Simply splitting observations into subsamples and comparing conditional correlations lead to spurious results due to the well-known conditioning bias. Our suggested framework is able to circumvent these problems. Applying our test to market data, we statistically confirm the widespread notion of significant asymmetric dependence structures between daily changes of the VIX, VXN, VDAXnew, and VSTOXX volatility indices and their corresponding equity index returns. A maximum likelihood method is used to perform a likelihood ratio test between the ordinary t-copula and its asymmetric extension. To the best of our knowledge, our study is the first empirical implementation of the skewed t-copula to generate meta-skewed Student's t-distributions. Its asymmetry leads to significant improvements in the description of the dependence structure between equity returns and implied volatility changes.

Layer histogram patterns in financial time series

This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behavior of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states.

Asymmetric Dependence Patterns in Financial Time Series

This paper proposes a new copula-based approach to test for asymmetries in the dependence structure of financial time series. Simply splitting observations into subsamples and comparing conditional correlations leads to spurious results due to the well-known conditioning bias. Our suggested framework is able to circumvent these problems. Applying our test to market data, we statistically confirm the widespread notion of significant asymmetric dependence structures between daily changes of the VIX, VXN, VDAXnew, and VSTOXX volatility indices and their corresponding equity index returns. A maximum likelihood method is used to perform a likelihood ratio test between the ordinary t-copula and its asymmetric extension. To the best of our knowledge, our study is the first empirical implementation of the skewed t-copula to generate meta skewed student t-distributions. Its asymmetry leads to significant improvements in the description of the dependence structure between equity returns and implied volatility changes.

Pattern-Based Expectations: International Experimental Evidence and Applications in Financial Economics

We study how subjects extrapolate simple patterns in financial time series in order to develop a descriptive model of actual agent behavior. The laboratory experiment for this analysis was conducted in Germany and Japan. Statistical analyses indicate considerable similarity in expectations formation across cultures and document that agents' expectations are at variance with the notion of standard trend extrapolation. The paper then proposes a method for computing expectations for any economic time series based on the experimental data. Such pattern-based expectations are shown to explain stock prices and the dynamics of the forward discount on the foreign exchange market.

Forbidden patterns in financial time series

The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, such as Lyapunov exponent or Kolmogorov entropy, thus allowing to separate deterministic (usually chaotic) from random series; however, it requires fewer values of the series to be calculated, and it is suitable for using with small datasets. In this paper, the appearance of forbidden patterns is studied in different economical indicators such as stock indices (Dow Jones Industrial Average and Nasdaq Composite), NYSE stocks (IBM and Boeing), and others (ten year Bond interest rate), to find evidence of deterministic behavior in their evolutions. Moreover, the rate of appearance of the forbidden patterns is calculated, and some considerations about the underlying dynamics are suggested.

BL-GARCH models and asymmetries in volatility

In this paper the class of Bilinear GARCH (BL-GARCH) models is proposed. BL-GARCH models allow to capture asymmetries in the conditional variance of financial and economic time series by means of interactions between past shocks and volatilities. The availability of likelihood based inference is an attractive feature of BL-GARCH models. Under the assumption of conditional normality, the log-likelihood function can be maximized by means of an EM type algorithm. The main reason for using the EM algorithm is that it allows to obtain parameter estimates which naturally guarantee the positive definiteness of the conditional variance with no need for additional parameter constraints. We also derive a robust LM test statistic which can be used for model identification. Finally, the effectiveness of BL-GARCH models in capturing asymmetric volatility patterns in financial time series is assessed by means of an application to a time series of daily returns on the NASDAQ Composite stock market index.