A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

[EN] In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating im- portant innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pat- tern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are de- veloped and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.The fourth author of this work was partially supported by MINECO, Project MTM2016-75963-P.Arévalo, R.; García, J.; Guijarro, F.; Peris Manguillot, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications. 81:177-192. https://doi.org/10.1016/j.eswa.2017.03.0281771928

Machine Learning and Natural Language Processing in Stock Prediction

In this thesis, we first study the two ill-posed natural language processing tasks related to stock prediction, i.e. stock movement prediction and financial document-level event extraction. While implementing stock prediction and event extraction, we encountered difficulties that could be resolved by utilizing out-of-distribution detection. Consequently, we presented a new approach for out-of-distribution detection, which is the third focus of this thesis. First, we systematically build a platform to study the NLP-aided stock auto-trading algorithms. Our platform is characterized by three features: (1) We provide financial news for each specific stock. (2) We provide various stock factors for each stock. (3) We evaluate performance from more financial-relevant metrics. Such a design allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a more realistic setting. We also propose a system to automatically learn a good feature representation from various input information. The key to our algorithm is a method called semantic role labelling Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a compact representation of each news paragraph. Based on SRLP, we further incorporate other stock factors to make the stock movement prediction. In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system. Through our experimental study, we show that the proposed method achieves better performance and outperforms all strong baselines’ annualized rate of return as well as the maximum drawdown in back-testing. Second, we propose a generative solution for document-level event extraction that takes into account recent developments in generative event extraction, which have been successful at the sentence level but have not yet been explored for document-level extraction. Our proposed solution includes an encoding scheme to capture entity-to-document level information and a decoding scheme that takes into account all relevant contexts. Extensive experimental results demonstrate that our generative-based solution can perform as well as state-of-theart methods that use specialized structures for document event extraction. This allows our method to serve as an easy-to-use and strong baseline for future research in this area. Finally, we propose a new unsupervised OOD detection model that separates, extracts, and learns the semantic role labelling guided fine-grained local feature representation from different sentence arguments and the full sentence using a margin-based contrastive loss. Then we demonstrate the benefit of applying a self-supervised approach to enhance such global-local feature learning by predicting the SRL extracted role. We conduct our experiments and achieve state-of-the-art performance on out-of-distribution benchmarks.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Hearing Women's Voices in Popular Song: Analyzing Sound and Identity in Country and Soul

In this study I combine music analysis with critical theory to investigate how different conceptions of feminine identity--intersecting with race and class--are materialized through recorded sound. I present interpretive analyses of four popular songs recorded and released between 1967 and 1974: "Baby, I Love You" by Aretha Franklin, "Fist City" by Loretta Lynn, "If I Were Your Woman" by Gladys Knight and the Pips, and "Jolene" by Dolly Parton. My analyses focus on vocal performance, and vocal quality (or timbre) in particular, as I investigate the means by which the sounds of these recordings participate in cultural discourse on gender, sexuality, race, and class. These songs narrate moments in sexual love relationships (the hope of new love or the threat of infidelity), while the performances of each vocalist, the studio musicians, and the work of engineers and producers combine to create representations of black and working-class femininity that express varying degrees of assertiveness and vulnerability in the face of unequal gender power relations. I compare and contextualize these sonic expressions of identity with the personas these vocalists presented in their professional and public lives, illustrating how these recordings participate in the construction of a multi-faceted and always-emergent history of American womanhood. In order to accurately describe the relationship between musical sound and intersectional gender identity, I develop a phenomenological analytic methodology sensitive to how embodied responses (the types of physical engagements invited by sound), associative (or connotative, semiotic) responses, and social and historical context of both the recording and listener all contribute to the process of interpretation. I take my own situated listening experience as the object of study, recognizing how my listening practices and reactions, and overlapping identities--as a white, upper-middle-class woman and music scholar--impact my interpretations of these songs. My focus on the physical engagement inherent in music listening underpins the approach to vocal quality analysis I present at the outset of my study, in which I link descriptive language about voice to the physical components of vocal sound production. In my analyses of lyrics, instrumental quality, dynamics, rhythm, form, pitch, and the sonic "space" afforded by each recording, I continue to attend to the types of embodied and associative responses afforded by each element, demonstrating how an engagement with these sounds informs conceptions of gender identity

Voices and noises: collaborative authorship in Stanley Kubrick’s films

This thesis sets out to challenge the mythology surrounding Kubrick’s filmmaking practice. The still dominant auteur approach in Kubrick studies identifies the director’s filmmaking practice as autonomous, with little creative input from his crew members. Following the recent shift in research that focuses on the collaborative nature of Kubrick’s working practice, I argue for a different perspective on creative practice in film production. The working process in Kubrick’s crews is shown to exhibit strong collaborative features and to encourage individual creative input. This thesis is based on the examination of historical evidence acquired from the Stanley Kubrick Archive in London and an extensive collection of mediated and personally conducted interviews with Kubrick’s collaborators. The historical discourse analysis employed in this thesis is rooted in New Film History methodologies and, with its findings, leads to an alternative perspective on film history. The challenge to the accepted view (or myth) of Kubrick is achieved with the use of discourse sources from production and from the archive, presented in the form of stories from pre-production to the promotion stage of film production. The outcomes of the research reveal other ways in which Kubrick collaborated and these alternative perspectives are then used to build an argument around collaboration in Kubrick’s films. With its focus on challenging Kubrick mythology by revealing the unheard voices in the production process, thereby challenging the common perception of them as ‘noise’, this thesis questions the applicability of authorship theory to the study of filmmaking practice. As such, it represents an important original contribution to the field of Kubrick studies

Strategies to Improve Marine Inspection Performance in the U.S. Coast Guard

U.S. Coast Guard leaders have received feedback concerning gaps in performance management of the Marine Inspection Program (MIP) from maritime industry stakeholders, Department of Homeland Security representatives, and internal agents over the past decade. The purpose of this case study was to explore strategies to improve performance in the U.S. Coast Guard MIP. Data were gathered through a review of documentation pertinent to marine inspection (i.e., policy, requirements, analyses, reports, and job aids) and 13 semistructured interviews with personnel from 3 distinct organizational levels. Study participants represented civilian and active duty personnel from all geographical U.S. Coast Guard districts, as well as tactical, strategic, and policy levels of the MIP. The conceptual framework of the study was Fusch and Gillespie\u27s human competence model. Data analysis was based on coding of words, phrases, and sentences from multiple sources of data to identify recurring themes through methodological triangulation. The thematic analysis of the study data revealed themes that included lack of mission clarity, limited information management resources, differences in skills and knowledge management among inspectors, and unclear requirements for selecting a marine inspector. The study framework provided a basis for additional performance management research in government entities. The recommendations from this study may lead to social change through improved U.S. Coast Guard marine inspection services, which could result in greater safety, reduced pollution, and fewer security risks in the navigable waterways of the United States

Winona Daily News

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