Search results for: investor sentiment

Authors: Nathan Garcia

Abstract:

The polarizing dialogue of contemporary political America results from core philosophical differences. But these differences are beyond ideological and reach metaphysical distinction. Good intellectual historians have theorized that fundamental concepts such as freedom, God, and nature have been sterilized of their intellectual vigor. They are partially correct. 19th-century pragmatist Charles Sanders Peirce offers a penetrating philosophy which can yield greater insight into the contemporary political divide. Peirce argues that metaphysical and ethical issues are derivative of operational logic. His triadic logical structure and ensuing metaphysical principles constructed therefrom is contemporaneously applicable for three reasons. First, Peirce’s logic aptly scrutinizes the logical processes of liberal and conservative mindsets. Each group arrives at a cosmological root metaphor (abduction), resulting in a contemporary assessment (deduction), ultimately prompting attempts to verify the original abduction (induction). Peirce’s system demonstrates that liberal citizens develop a cosmological root metaphor in the concept of fairness (abduction), resulting in a contemporary assessment of, for example, underrepresented communities being unfairly preyed upon (deduction), thereby inciting anger toward traditional socio-political structures suspected of purposefully destabilizing minority communities (induction). Similarly, conservative citizens develop a cosmological root metaphor in the concept of freedom (abduction), resulting in a contemporary assessment of, for example, liberal citizens advocating an expansion of governmental powers (deduction), thereby inciting anger towards liberal communities suspected of attacking freedoms of ordinary Americans in a bid to empower their interests through the government (induction). The value of this triadic assessment is the categorization of distinct types of inferential logic by their purpose and boundaries. Only deductive claims can be concretely proven, while abductive claims are merely preliminary hypotheses, and inductive claims are accountable to interdisciplinary oversight. Liberals and conservative logical processes preclude constructive dialogue because of (a) an unshared abductive framework, and (b) misunderstanding the rules and responsibilities of their types of claims. Second, Peircean metaphysical principles offer a greater summary of the contemporaneously divisive political climate. His insights can weed through the partisan theorizing to unravel the underlying philosophical problems. Corrosive nominalistic and essentialistic presuppositions weaken the ability to share experiences and communicate effectively, both requisite for any promising constructive dialogue. Peirce’s pragmatist system can expose and evade fallacious thinking in pursuit of a refreshing alternative framework. Finally, Peirce’s metaphysical foundation enables a logically coherent, scientifically informed orthopraxis well-suited for American dialogue. His logical structure necessitates radically different anthropology conducive to shared experiences and dialogue within a dynamic, cultural continuum. Pierce’s fallibilism and sensitivity to religious sentiment successfully navigate between liberal and conservative values. In sum, he provides a normative paradigm for intranational dialogue that privileges individual experience and values morally defensible notions of freedom, God, and nature. Utilizing Peirce’s thought will yield fruitful analysis and offers a promising philosophical alternative for framing and engaging in contemporary American political discourse.

Keywords: Charles s. Peirce, american politics, logic, pragmatism

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Authors: Gertraud Koch, Teresa Stumpf, Alejandra Tijerina García

Abstract:

Discourse analysis research approaches belong to the central research strategies applied throughout the humanities; they focus on the countless forms and ways digital texts and images shape present-day notions of the world. Despite the constantly growing number of relevant digital, multimodal discourse resources, digital humanities (DH) methods are thus far not systematically developed and accessible for discourse analysis approaches. Specifically, the significance of multimodality and meaning plurality modelling are yet to be sufficiently addressed. In order to address this research gap, the D-WISE project aims to develop a prototypical working environment as digital support for the sociology of knowledge approach to discourse analysis and new IT-analysis approaches for the use of context-oriented embedding representations. Playing an essential role throughout our research endeavor is the constant optimization of hermeneutical methodology in the use of (semi)automated processes and their corresponding epistemological reflection. Among the discourse analyses, the sociology of knowledge approach to discourse analysis is characterised by the reconstructive and accompanying research into the formation of knowledge systems in social negotiation processes. The approach analyses how dominant understandings of a phenomenon develop, i.e., the way they are expressed and consolidated by various actors in specific arenas of discourse until a specific understanding of the phenomenon and its socially accepted structure are established. This article presents insights and initial findings from D-WISE, a joint research project running since 2021 between the Institute of Anthropological Studies in Culture and History and the Language Technology Group of the Department of Informatics at the University of Hamburg. As an interdisciplinary team, we develop central innovations with regard to the availability of relevant DH applications by building up a uniform working environment, which supports the procedure of the sociology of knowledge approach to discourse analysis within open corpora and heterogeneous, multimodal data sources for researchers in the humanities. We are hereby expanding the existing range of DH methods by developing contextualized embeddings for improved modelling of the plurality of meaning and the integrated processing of multimodal data. The alignment of this methodological and technical innovation is based on the epistemological working methods according to grounded theory as a hermeneutic methodology. In order to systematically relate, compare, and reflect the approaches of structural-IT and hermeneutic-interpretative analysis, the discourse analysis is carried out both manually and digitally. Using the example of current discourses on digitization in the healthcare sector and the associated issues regarding data protection, we have manually built an initial data corpus of which the relevant actors and discourse positions are analysed in conventional qualitative discourse analysis. At the same time, we are building an extensive digital corpus on the same topic based on the use and further development of entity-centered research tools such as topic crawlers and automated newsreaders. In addition to the text material, this consists of multimodal sources such as images, video sequences, and apps. In a blended reading process, the data material is filtered, annotated, and finally coded with the help of NLP tools such as dependency parsing, named entity recognition, co-reference resolution, entity linking, sentiment analysis, and other project-specific tools that are being adapted and developed. The coding process is carried out (semi-)automated by programs that propose coding paradigms based on the calculated entities and their relationships. Simultaneously, these can be specifically trained by manual coding in a closed reading process and specified according to the content issues. Overall, this approach enables purely qualitative, fully automated, and semi-automated analyses to be compared and reflected upon.

Keywords: entanglement of structural IT and hermeneutic-interpretative analysis, multimodality, plurality of meaning, sociology of knowledge approach to discourse analysis

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Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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