Detecting intentional herding: what lies beneath intraday data in the spanish stock market

This paper examines the intentional herd behaviour of market participants using a new bootstrap-based approach that compares the scaled cross-sectional deviation of returns in the intraday market with the cross-sectional deviation of returns of an “artificially created” market free of intentional herding effects. The analysis is carried out for both the overall market and a sample of the most representative. The results show that the Spanish market exhibits a significant intraday herding effect that is not detected using other traditional measures when familiar stocks are analysed. Furthermore, it is suggested that herding is likely to be better revealed using intraday data.Behaviour, finance, time series

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

GetHFData : A R package for downloading and aggregating high frequency trading data from Bovespa

This paper introduces GetHFData, a R package for downloading, importing and aggregating high frequency trading data from the Brazilian nancial market. Based on a set of user choices, the package GetHFData will download the required les directly from Bovespa's site and aggregate the nancial data. The main objective of the publication of this software is to facilitate the computational e ort related to research based on this large nancial dataset and also to increase the reproducibility of studies by setting a replicable standard for data acquisition and processing. In this paper we present the available functions of the software, a brief description of the Brazilian market and several reproducible examples of usage

Securities clearance and settlement systems - a guide to best practices

As an essential part of a nation's financial sector infrastructure, securities clearance, and settlement systems must be closely integrated with national payment systems, so that safety, soundness, certainty, and efficiency can be achieved at a cost acceptable to all participants. Central banks have paid considerable attention to payment systems, but securities clearance, and settlement systems have only recently been subjected to rigorous assessment. The Western Hemisphere Payments and Securities Clearance and Settlement Initiative (WHI), led by the World Bank, and in cooperation with the Centro de Estudios Monetarios Latinoamericanos (CEMLA), gave the authors a unique opportunity to observe how various countries in Latin America, and the Caribbean undertake securities clearance, and settlement. To do so, the authors developed a practical, and implementable assessment methodology, covering key issues that affect the quality of such systems. In this paper they discuss the objectives, scope, and content of a typical securities system, identify the elements that influence the system's quality, and show how their assessment methodology works. They focus on the development of core principles, and minimum standards for integrated systems of payments, and securities clearance and settlement. Their paper fills a gap by providing an evaluation tool for assessors of such systems, especially those who must assess evolving systems in developing, and transition economies. Essentially, an assessment involves a structured analysis to answer four related questions: 1) What are the objective, and scope of a securities clearance and settlement system? 2) Who are the participants, what roles do they play, and what expectations do they have? 3) What procedures are required to satisfy the participants'needs? 4) What inherent risks are involved, and how can they be mitigated at an acceptable cost?Environmental Economics&Policies,Payment Systems&Infrastructure,Financial Intermediation,International Terrorism&Counterterrorism,Securities Markets Policy&Regulation,Financial Intermediation,Environmental Economics&Policies,Settlement of Investment Disputes,Payment Systems&Infrastructure,Insurance&Risk Mitigation

Stock price change prediction using news text mining

Along with the advent of the Internet as a new way of propagating news in a digital format, came the need to understand and transform this data into information. This work presents a computational framework that aims to predict the changes of stock prices along the day, given the occurrence of news articles related to the companies listed in the Down Jones Index. For this task, an automated process that gathers, cleans, labels, classifies, and simulates investments was developed. This process integrates the existing data mining and text algorithms, with the proposal of new techniques of alignment between news articles and stock prices, pre-processing, and classifier ensemble. The result of experiments in terms of classification measures and the Cumulative Return obtained through investment simulation outperformed the other results found after an extensive review in the related literature. This work also argues that the classification measure of Accuracy and incorrect use of cross validation technique have too few to contribute in terms of investment recommendation for financial market. Altogether, the developed methodology and results contribute with the state of art in this emerging research field, demonstrating that the correct use of text mining techniques is an applicable alternative to predict stock price movements in the financial market.Com o advento da Internet como um meio de propagação de notícias em formato digital, veio a necessidade de entender e transformar esses dados em informação. Este trabalho tem como objetivo apresentar um processo computacional para predição de preços de ações ao longo do dia, dada a ocorrência de notícias relacionadas às companhias listadas no índice Down Jones. Para esta tarefa, um processo automatizado que coleta, limpa, rotula, classifica e simula investimentos foi desenvolvido. Este processo integra algoritmos de mineração de dados e textos já existentes, com novas técnicas de alinhamento entre notícias e preços de ações, pré-processamento, e assembleia de classificadores. Os resultados dos experimentos em termos de medidas de classificação e o retorno acumulado obtido através de simulação de investimentos foram maiores do que outros resultados encontrados após uma extensa revisão da literatura. Este trabalho também discute que a acurácia como medida de classificação, e a incorreta utilização da técnica de validação cruzada, têm muito pouco a contribuir em termos de recomendação de investimentos no mercado financeiro. Ao todo, a metodologia desenvolvida e resultados contribuem com o estado da arte nesta área de pesquisa emergente, demonstrando que o uso correto de técnicas de mineração de dados e texto é uma alternativa aplicável para a predição de movimentos no mercado financeiro

What makes trading strategies based on chart pattern recognition profitable?

[EN] Automating chart pattern recognition is a relevant issue addressed by researchers and practitioners when designing a system that considers technical analysis for trading purposes. This article proposes the design of a trading system that takes into account any generic pattern that has been proven to be profitable in the past, without restricting the search to the specific technical patterns reported in the literature, hence the term generic pattern recognition. A fast version of dynamic time warping, the University College Riverside subsequence search suite (called the UCR suite), is employed for the pattern recognition task in an effort to produce trading signals in realistic timescales. This article evaluates the significance of the relation between the system's profitability and (a) the pattern length, (b) the take-profit and stop-loss levels and (c) the performance consensus of past patterns. The trading system is assessed under the mean¿variance perspective by using 560 NYSE stocks. The results obtained by the different parameter configurations are reported, controlling for both data-snooping and transaction costs. On average, the proposed system dominates the market index in the mean¿variance sense. Although transaction costs reduce the profitability of the proposed trading system, 92.5% of the experiments are profitable if the analysis is reduced to the parameter values aligned with the technical analysisTsinaslanidis, P.; Guijarro, F. (2021). What makes trading strategies based on chart pattern recognition profitable?. Expert Systems. 38(5):1-17. https://doi.org/10.1111/exsy.12596S11738

Liquidity crises on different time scales

We present an empirical analysis of the microstructure of financial markets and, in particular, of the static and dynamic properties of liquidity. We find that on relatively large time scales (15 min) large price fluctuations are connected to the failure of the subtle mechanism of compensation between the flows of market and limit orders: in other words, the missed revelation of the latent order book breaks the dynamical equilibrium between the flows, triggering the large price jumps. On smaller time scales (30 s), instead, the static depletion of the limit order book is an indicator of an intrinsic fragility of the system, which is related to a strongly nonlinear enhancement of the response. In order to quantify this phenomenon we introduce a measure of the liquidity imbalance present in the book and we show that it is correlated to both the sign and the magnitude of the next price movement. These findings provide a quantitative definition of the effective liquidity, which proves to be strongly dependent on the considered time scales