Narrative I/O

narrativeio

The 2025 Narrative In Crypto

Příběh z roku 2025 v kryptoměnách

La narrativa del 2025 en las criptomonedas

Le récit de 2025 dans la crypto

הנרטיב של 2025 בקריפטו

Narativ 2025. u kripto

La narrazione del 2025 in criptovaluta

2025年の暗号通貨の物語

2025 წლის ნარატივი კრიპტოში

Lisolo Ya 2025 Na Crypto

ການບັນຍາຍໃນປີ 2025 ໃນ Crypto

ක්‍රිප්ටෝ හි 2025 ආඛ්‍යානය

ትረኻ 2025 ኣብ ክሪፕቶ

The Crypto Lobby Seeks to Influence US Politics: A Peek at Their Smear Campaign and False Narrative

The Bitcoin Hypocrisy Repository - Keeping Track Of Mainstream Lies & Fake Narratives

Authors:
(1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk);
(2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Data Framework Results Conclusions, Acknowledgements, and References 5 Conclusions Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news. The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch. As a final remark, we desire to point to the problem of network alignment, which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions. Acknowledgements Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1). References [1] Nadine Strauß, Rens Vliegenthart, and Piet Verhoeven. Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News. Communication Research, 45(7):1054–1077, 2018. PMID: 30443092. [2] Fabrizio Lillo, Salvatore Miccichè, Michele Tumminello, Jyrki Piilo, and Rosario N. Mantegna. How news affects the trading behaviour of different categories of investors in a financial market. Quantitative Finance, 15(2):213–229, 2015. [3] Eric C. So and Sean Wang. News-driven return reversals: Liquidity provision ahead of earnings announcements. Journal of Financial Economics, 114(1):20–35, 2014. [4] Haizhou Qu and Dimitar Kazakov. Quantifying correlation between financial news and stocks. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–6, 2016. [5] Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi, and Tsutomu Watanabe. High quality topic extraction from business news explains abnormal financial market volatility. CARF F-Series CARF-F-299, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, October 2012. [6] Shaheen Syed and Marco Spruit. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 165–174, 2017. [7] Weisi Chen, Fethi Rabhi, Wenqi Liao, and Islam Al-Qudah. Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: A comparative study. Electronics, 12(12), 2023. [8] Dimo Angelov. Top2vec: Distributed representations of topics, 2020. [9] Maarten Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. [10] Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm – explainable financial time series forecasting, 2023. [11] Udit Gupta. Gpt-investar: Enhancing stock investment strategies through annual report analysis with large language models, 2023. [12] Rick Steinert and Saskia Altmann. Linking microblogging sentiments to stock price movement: An application of gpt-4, 2023. [13] Paolo Pasquariello. Financial market dislocations. The Review of Financial Studies, 27(6):1868–1914, 2014. [14] OpenAI. Gpt-4 technical report, 2023. [15] Partha Pratim Ray. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3:121–154, 2023. [16] C.J. Hutto and Eric Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. 01 2015. [17] Mark E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006. [18] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, Oct 2008. [19] Shihua Zhang, Rui-Sheng Wang, and Xiang-Sun Zhang. Uncovering fuzzy community structure in complex networks. Physical Review E, 76(4):046103, 2007. [20] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space, 2013. [21] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks, 2016. [22] Eghbal Rahimikia, Stefan Zohren, and Ser-Huang Poon. Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, 08 2021. [23] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. [24] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, jun 2002. [25] Yi-Jiao Zhang, Kai-Cheng Yang, and Filippo Radicchi. Systematic comparison of graph embedding methods in practical tasks. Phys. Rev. E, 104:044315, Oct 2021. [26] Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, and Roded Sharan. To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics, 10, 2019. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Authors: Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Abstract and Intro Data Data Framework Framework Results Results Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References 5 Conclusions Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news. The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch. As a final remark, we desire to point to the problem of network alignment , which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions. network alignment Acknowledgements Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1). EPSRC CDT in Mathematics of Random Systems References [1] Nadine Strauß, Rens Vliegenthart, and Piet Verhoeven. Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News. Communication Research, 45(7):1054–1077, 2018. PMID: 30443092. [2] Fabrizio Lillo, Salvatore Miccichè, Michele Tumminello, Jyrki Piilo, and Rosario N. Mantegna. How news affects the trading behaviour of different categories of investors in a financial market. Quantitative Finance, 15(2):213–229, 2015. [3] Eric C. So and Sean Wang. News-driven return reversals: Liquidity provision ahead of earnings announcements. Journal of Financial Economics, 114(1):20–35, 2014. [4] Haizhou Qu and Dimitar Kazakov. Quantifying correlation between financial news and stocks. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–6, 2016. [5] Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi, and Tsutomu Watanabe. High quality topic extraction from business news explains abnormal financial market volatility. CARF F-Series CARF-F-299, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, October 2012. [6] Shaheen Syed and Marco Spruit. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 165–174, 2017. [7] Weisi Chen, Fethi Rabhi, Wenqi Liao, and Islam Al-Qudah. Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: A comparative study. Electronics, 12(12), 2023. [8] Dimo Angelov. Top2vec: Distributed representations of topics, 2020. [9] Maarten Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. [10] Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm – explainable financial time series forecasting, 2023. [11] Udit Gupta. Gpt-investar: Enhancing stock investment strategies through annual report analysis with large language models, 2023. [12] Rick Steinert and Saskia Altmann. Linking microblogging sentiments to stock price movement: An application of gpt-4, 2023. [13] Paolo Pasquariello. Financial market dislocations. The Review of Financial Studies, 27(6):1868–1914, 2014. [14] OpenAI. Gpt-4 technical report, 2023. [15] Partha Pratim Ray. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3:121–154, 2023. [16] C.J. Hutto and Eric Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. 01 2015. [17] Mark E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006. [18] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, Oct 2008. [19] Shihua Zhang, Rui-Sheng Wang, and Xiang-Sun Zhang. Uncovering fuzzy community structure in complex networks. Physical Review E, 76(4):046103, 2007. [20] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space, 2013. [21] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks, 2016. [22] Eghbal Rahimikia, Stefan Zohren, and Ser-Huang Poon. Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, 08 2021. [23] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. [24] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, jun 2002. [25] Yi-Jiao Zhang, Kai-Cheng Yang, and Filippo Radicchi. Systematic comparison of graph embedding methods in practical tasks. Phys. Rev. E, 104:044315, Oct 2021. [26] Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, and Roded Sharan. To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics, 10, 2019. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Conclusions and References

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Results

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Framework

Authors:
(1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk);
(2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Data Framework Results Conclusions, Acknowledgements, and References 2 Data 2.1 Corpus of news We download a tractable corpus of news from Factiva[1] data provider by looking for news written in English, which also belong to the “Economics” section of the Wall Street Journal (WSJ). In this way, we aim at having a set of news that carries a low-noise and focused view on the evolution of news themes that can be of help for financial markets understanding. We consider approximately four years of daily news, i.e. from January 2020 to October 2023, and aggregate them at weekly level. After pre-processing them to a standard format, we achieve a overall dataset of 197 weeks with a total of 21, 590 news, with 110 ± 21 data points per week (i.e. average number of articles and its standard deviation). Importantly, we considered the week ending on 14th March 2021 as an outlier and dropped it, since we could only download three associated articles from Factiva. 2.2 Market dislocations Our aim is to identify and quantify the evolution of narratives within news, but with the further end goal to unravel consequent relationships to the evolution of financial markets. In particular, we are interested in financial market dislocations, which are often recognised as moments when “financial markets, operating under stressful conditions, experience large, widespread asset mispricings” [13]. However, we decide to adopt more a data-driven definition of market dislocations, and consider them as dates when combined shocks to equity, FX, bond, and macro factor risk premium indices occur, i.e. shocks to all the major asset classes. We begin by downloading the following four indices from Bloomberg L.P. at a weekly frequency, with the proposed descriptions taken from its interface: VIX Index - “The VIX Index is a financial benchmark designed to be an up-to-the-minute market estimate of the expected volatility of the S&P 500 Index, and is calculated by using the midpoint of real-time S&P 500 Index option bid/ask quotes.”


JPMVXYEM Index (VIX FX) - “J.P. Morgan Emerging Market Currency Implied Volatility Index.”


MRI CITI Index - “The Citi Macro Risk Index measures risk aversion based on prices of assets that are typically sensitive to risk. A reading above (below) 0.5 means that risk aversion is above (below) average.”


MOVE Index - “The MOVE Index measures U.S. bond market volatility by tracking a basket of Over-theCounter options on U.S. interest rate swaps. The Index tracks implied normal yield volatility of a yield curve weighted basket of at-the-money one month options on the 2-year, 5-year, 10-year, and 30-year constant maturity interest rate swaps.” Then, we compute the related z-scores for a rolling window ∆T of three months, i.e. 13 weeks if we assume one year to be made of 52 weeks. The z-score is defined as where in our case i is the current value of the index, µ is its mean over the previous time range ∆T, and σ is the related standard deviation. Intuitively, the z-score shows how many standard deviations above the mean the current outcome is. Figure 1 proposes the dates for which all our four indices have positive z-score. According to the strength of these z-scores, this can imply that broad dislocations across asset classes were witnessed. This paper is available on arxiv under CC0 1.0 DEED license. [1] https://www.dowjones.com/professional/factiva/ Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Authors: Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Abstract and Intro Data Data Framework Framework Results Results Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References 2 Data 2.1 Corpus of news We download a tractable corpus of news from Factiva[1] data provider by looking for news written in English, which also belong to the “Economics” section of the Wall Street Journal (WSJ). In this way, we aim at having a set of news that carries a low-noise and focused view on the evolution of news themes that can be of help for financial markets understanding. We consider approximately four years of daily news, i.e. from January 2020 to October 2023, and aggregate them at weekly level. After pre-processing them to a standard format, we achieve a overall dataset of 197 weeks with a total of 21, 590 news, with 110 ± 21 data points per week (i.e. average number of articles and its standard deviation). Importantly, we considered the week ending on 14th March 2021 as an outlier and dropped it, since we could only download three associated articles from Factiva. 2.2 Market dislocations Our aim is to identify and quantify the evolution of narratives within news, but with the further end goal to unravel consequent relationships to the evolution of financial markets. In particular, we are interested in financial market dislocations, which are often recognised as moments when “financial markets, operating under stressful conditions, experience large, widespread asset mispricings” [13]. However, we decide to adopt more a data-driven definition of market dislocations, and consider them as dates when combined shocks to equity, FX, bond, and macro factor risk premium indices occur, i.e. shocks to all the major asset classes. We begin by downloading the following four indices from Bloomberg L.P. at a weekly frequency, with the proposed descriptions taken from its interface: VIX Index - “The VIX Index is a financial benchmark designed to be an up-to-the-minute market estimate of the expected volatility of the S&P 500 Index, and is calculated by using the midpoint of real-time S&P 500 Index option bid/ask quotes.” JPMVXYEM Index (VIX FX) - “J.P. Morgan Emerging Market Currency Implied Volatility Index.” MRI CITI Index - “The Citi Macro Risk Index measures risk aversion based on prices of assets that are typically sensitive to risk. A reading above (below) 0.5 means that risk aversion is above (below) average.” MOVE Index - “The MOVE Index measures U.S. bond market volatility by tracking a basket of Over-theCounter options on U.S. interest rate swaps. The Index tracks implied normal yield volatility of a yield curve weighted basket of at-the-money one month options on the 2-year, 5-year, 10-year, and 30-year constant maturity interest rate swaps.” VIX Index - “The VIX Index is a financial benchmark designed to be an up-to-the-minute market estimate of the expected volatility of the S&P 500 Index, and is calculated by using the midpoint of real-time S&P 500 Index option bid/ask quotes.” VIX Index - “The VIX Index is a financial benchmark designed to be an up-to-the-minute market estimate of the expected volatility of the S&P 500 Index, and is calculated by using the midpoint of real-time S&P 500 Index option bid/ask quotes.” JPMVXYEM Index (VIX FX) - “J.P. Morgan Emerging Market Currency Implied Volatility Index.” JPMVXYEM Index (VIX FX) - “J.P. Morgan Emerging Market Currency Implied Volatility Index.” MRI CITI Index - “The Citi Macro Risk Index measures risk aversion based on prices of assets that are typically sensitive to risk. A reading above (below) 0.5 means that risk aversion is above (below) average.” MRI CITI Index - “The Citi Macro Risk Index measures risk aversion based on prices of assets that are typically sensitive to risk. A reading above (below) 0.5 means that risk aversion is above (below) average.” MOVE Index - “The MOVE Index measures U.S. bond market volatility by tracking a basket of Over-theCounter options on U.S. interest rate swaps. The Index tracks implied normal yield volatility of a yield curve weighted basket of at-the-money one month options on the 2-year, 5-year, 10-year, and 30-year constant maturity interest rate swaps.” MOVE Index - “The MOVE Index measures U.S. bond market volatility by tracking a basket of Over-theCounter options on U.S. interest rate swaps. The Index tracks implied normal yield volatility of a yield curve weighted basket of at-the-money one month options on the 2-year, 5-year, 10-year, and 30-year constant maturity interest rate swaps.” Then, we compute the related z-scores for a rolling window ∆T of three months, i.e. 13 weeks if we assume one year to be made of 52 weeks. The z-score is defined as where in our case i is the current value of the index, µ is its mean over the previous time range ∆T, and σ is the related standard deviation. Intuitively, the z-score shows how many standard deviations above the mean the current outcome is. Figure 1 proposes the dates for which all our four indices have positive z-score. According to the strength of these z-scores, this can imply that broad dislocations across asset classes were witnessed. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv [1] https://www.dowjones.com/professional/factiva/

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Data

Authors:
(1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk);
(2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Data Framework Results Conclusions, Acknowledgements, and References ABSTRACT Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news. 1 Introduction In today’s fast-paced digital age, the world is inundated with an unprecedented volume of information, particularly from news sources. The sheer magnitude of data generated daily has reached staggering proportions, making it increasingly challenging for individuals to parse and process this information accurately solely through human capabilities. News is constantly flowing from countless channels and platforms, and the need for advanced tools and technologies to sift through this vast sea of data has never been more apparent. In the realm of financial markets, the potential gain from efficiently handling such an enormous amount of news data, and extract quantitative signals from it, is even more pronounced. Financial markets are highly sensitive to information, and characterising narratives within news is surely one task that can enhance our knowledge on news’ impact on asset prices, trading strategies, and investor sentiment. Some examples of interesting research on the topic follow. In [1], the authors investigate the high-frequency interdependent relationships between the stock market and statistics on economic news in the US context. Then, [2] investigates how news affects the trading behaviour of different categories of investors in a financial market, while [3] finds evidence that market makers demand higher expected returns prior to earnings announcements, because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. In [4], the authors measure the correlation between the returns of publicly traded companies and news about them as collected from Yahoo Financial News. Then, [5] tries to quantify how topics discussed within news influence the stock market. The authors of this paper apply a topic modeling technique called Latent Dirichlet Allocation (LDA) [6], in order to extract the keywords of information (i.e. “topics”) that synchronise well with trading activity, measured by the daily trading volume. However, LDA is a primitive technique that cannot deliver a systematic and predictive advantage. Relatedly, the very recent research in [7] investigates the field of topic modeling in the context of finance-related news impact analysis, and further stresses how very limited literature exists. The authors compare three state-of-the-art topic models, namely LDA, Top2Vec [8], and BERTopic [9], and show that the latter performs best. The framework focuses on extracting topics and related sentiment of specific stocks, whose time series of returns are in connection investigated. However, the actual identification and interpretation of topics is still of questionable level, and the framework limits itself to facilitate efficient news selection based on topics, but do not properly analyse the latter. On the other hand, the recent emergence of novel Large Language Models (LLMs), such as OpenAI’s Generative Pre-trained Transformer (GPT) ones, clearly marks a significant departure from earlier language processing techniques. These models leverage the power of deep learning and vast amounts of text data to achieve unprecedented levels of language understanding and generation, achieving outstanding improvements in Natural Language Processing (NLP) tasks, and opening countless novel paths of research. The paper in [10] presents a study on harnessing LLMs outstanding knowledge and reasoning abilities for explainable financial time series forecasting of NASDAQ-100 stocks. Then, [11] leverages GPT parsing of companies’ annual reports, to get suggestions on investment targets. On the other hand, [12] shows the potential improvement of the GPT4 LLM in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. There is a clear increasing interest in understanding how to optimally leverage on the capabilities of GPT models, since the focus has otherwise lied on their qualitative responses. For now, quantitative applications have been very constrained in breadth, and we are also not aware of any existing research that tries to leverage GPTs for topic extraction and narrative detection within news. Thus, we believe that our research provides a strongly innovative approach to the solution of such tasks, by leveraging both GPT models and network analysis techniques. Main contributions. Our research introduces a novel framework for topic identification within economic news. Indeed, we leverage on GPT3.5 to extract the main entities (both concrete or abstract) of reference for each article in an available corpus, and aggregate entities’ co-occurrence among articles in weekly graphs. Studying the resultant set of graphs allows us to identify clusters of entities that can be mapped back to interpretable non-trivial topics. Furthermore, basic metric such as degree and eigenvector centrality of nodes are shown to characterise the evolution over time of central themes of discussion. Beyond that, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. One final contribution of our study is the investigation of news’ features in relation to financial market dislocations. We design attributes that characterise both the local and global structure of news, and their sentiment and interconnections. This allows us to find quantitative evidence of high entropy in the high-dimensional space of interconnected news, when the latter are associated to moments of unusually high volatility across asset classes. Structure of the paper. Section 2 introduces the data we collect, and some initial related processing. Section 3 clarifies the theoretical knowledge that is necessary to understand the approach taken, which is mainly comprised by notions from both NLP and graph theory, and on related embedding techniques. Then, Section 4 highlights our analyses on narratives and market dislocations, and describes the results achieved. Finally, we conclude this work with some last remarks in Section 5. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Authors: Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Abstract and Intro Data Data Framework Framework Results Results Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References ABSTRACT Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news. 1 Introduction In today’s fast-paced digital age, the world is inundated with an unprecedented volume of information, particularly from news sources. The sheer magnitude of data generated daily has reached staggering proportions, making it increasingly challenging for individuals to parse and process this information accurately solely through human capabilities. News is constantly flowing from countless channels and platforms, and the need for advanced tools and technologies to sift through this vast sea of data has never been more apparent. In the realm of financial markets, the potential gain from efficiently handling such an enormous amount of news data, and extract quantitative signals from it, is even more pronounced. Financial markets are highly sensitive to information, and characterising narratives within news is surely one task that can enhance our knowledge on news’ impact on asset prices, trading strategies, and investor sentiment. Some examples of interesting research on the topic follow. In [1], the authors investigate the high-frequency interdependent relationships between the stock market and statistics on economic news in the US context. Then, [2] investigates how news affects the trading behaviour of different categories of investors in a financial market, while [3] finds evidence that market makers demand higher expected returns prior to earnings announcements, because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. In [4], the authors measure the correlation between the returns of publicly traded companies and news about them as collected from Yahoo Financial News. Then, [5] tries to quantify how topics discussed within news influence the stock market. The authors of this paper apply a topic modeling technique called Latent Dirichlet Allocation (LDA) [6], in order to extract the keywords of information (i.e. “topics”) that synchronise well with trading activity, measured by the daily trading volume. However, LDA is a primitive technique that cannot deliver a systematic and predictive advantage. Relatedly, the very recent research in [7] investigates the field of topic modeling in the context of finance-related news impact analysis, and further stresses how very limited literature exists. The authors compare three state-of-the-art topic models, namely LDA, Top2Vec [8], and BERTopic [9], and show that the latter performs best. The framework focuses on extracting topics and related sentiment of specific stocks, whose time series of returns are in connection investigated. However, the actual identification and interpretation of topics is still of questionable level, and the framework limits itself to facilitate efficient news selection based on topics, but do not properly analyse the latter. On the other hand, the recent emergence of novel Large Language Models (LLMs), such as OpenAI’s Generative Pre-trained Transformer (GPT) ones, clearly marks a significant departure from earlier language processing techniques. These models leverage the power of deep learning and vast amounts of text data to achieve unprecedented levels of language understanding and generation, achieving outstanding improvements in Natural Language Processing (NLP) tasks, and opening countless novel paths of research. The paper in [10] presents a study on harnessing LLMs outstanding knowledge and reasoning abilities for explainable financial time series forecasting of NASDAQ-100 stocks. Then, [11] leverages GPT parsing of companies’ annual reports, to get suggestions on investment targets. On the other hand, [12] shows the potential improvement of the GPT4 LLM in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. There is a clear increasing interest in understanding how to optimally leverage on the capabilities of GPT models, since the focus has otherwise lied on their qualitative responses. For now, quantitative applications have been very constrained in breadth, and we are also not aware of any existing research that tries to leverage GPTs for topic extraction and narrative detection within news. Thus, we believe that our research provides a strongly innovative approach to the solution of such tasks, by leveraging both GPT models and network analysis techniques. Main contributions. Our research introduces a novel framework for topic identification within economic news. Indeed, we leverage on GPT3.5 to extract the main entities (both concrete or abstract) of reference for each article in an available corpus, and aggregate entities’ co-occurrence among articles in weekly graphs. Studying the resultant set of graphs allows us to identify clusters of entities that can be mapped back to interpretable non-trivial topics. Furthermore, basic metric such as degree and eigenvector centrality of nodes are shown to characterise the evolution over time of central themes of discussion. Beyond that, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. One final contribution of our study is the investigation of news’ features in relation to financial market dislocations. We design attributes that characterise both the local and global structure of news, and their sentiment and interconnections. This allows us to find quantitative evidence of high entropy in the high-dimensional space of interconnected news, when the latter are associated to moments of unusually high volatility across asset classes. Main contributions. Structure of the paper. Section 2 introduces the data we collect, and some initial related processing. Section 3 clarifies the theoretical knowledge that is necessary to understand the approach taken, which is mainly comprised by notions from both NLP and graph theory, and on related embedding techniques. Then, Section 4 highlights our analyses on narratives and market dislocations, and describes the results achieved. Finally, we conclude this work with some last remarks in Section 5. Structure of the paper. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Abstract and Intro

The TechBeat: Mastering the Craft of Transforming Data into Engaging Visual Narratives in User Interfaces (5/5/2024)

As a society, we've always been drawn to a good story. From ancient myths to modern-day blockbusters, the power of narrative has the ability to captivate, inspire, and transform. In the world of technology, where innovation is the name of the game, storytelling has become the secret weapon behind some of the most buzzed-about startups. And the unexpected heroes of this new era? None other than the creative directors, the ad agency alchemists turned tech founders who have traded in their storyboards for the chance to rewrite the future. Armed with a knack for storytelling and a flair for the dramatic, these former ad agency stars are trading in their Cannes Lions for the chance to launch the next big thing. But as the hype around projects like Humane's AI Pin reaches fever pitch, it's worth asking: are these visionaries revolutionizing the tech landscape, or are they just spinning tales? Take Humane's AI Pin, for example. This little device, dreamt up by former Apple creative directors Imran Chaudhri and Bethany Bongiorno, has made quite the splash with its promise of a screenless, AI-driven future. But let's be real - it's not just the tech that's got everyone talking. It's the story. The vision of a world where technology seamlessly integrates into our lives, as natural as breathing. That's the kind of narrative that gets investors reaching for their checkbooks, the media clamoring for a headline and secures millions in funding. And Humane is far from alone in this new world order. Just look at the rise and fall of Elizabeth Holmes and Theranos. Sure, the revolutionary blood-testing technology never quite materialized, but damn if Holmes didn't spin one hell of a story. With her Steve Jobs-esque turtlenecks and promises of disruption, she had Silicon Valley eating out of the palm of her hand. Or what about Adam Neumann and WeWork? The man could sell a vision like no other, painting a picture of a workplace revolution that would “elevate the world's consciousness” (and the company's valuation to a cool $47 billion). But here's the thing about stories - they're only as good as the reality they're built on. When the curtain gets pulled back and the wizard is revealed to be just a guy with a smoke machine, things can get ugly fast. Just ask the folks who bought into the Theranos dream or the WeWork hype. When the story doesn't match the substance, it's not just disappointment that follows - it's disaster. Moreover, storytelling is not just about hype. Used responsibly, it can be a powerful tool for education and inspiration. When Steve Jobs introduced the iPhone, he didn't just sell a product; he painted a picture of how it would transform our lives. That narrative resonated because it was grounded in a genuine, revolutionary product. So what's the lesson here for the creative directors turned tech visionaries? The use of narrative as a launchpad is not without its risks: with great storytelling power comes great responsibility.The ability to craft a compelling narrative is a gift, but it's one that needs to be used wisely. The best stories, the ones that truly change the game, are the ones that are grounded in something real. Something tangible. Something that doesn't just tickle the imagination but actually follows through on its promises. This is the tightrope that creative directors in the tech world must walk. As for Humane's AI Pin? Only time will tell if it can live up to the hype. But one thing's for sure - in the wild west of tech startups, the storytellers are the new sheriffs in town. They're the ones who can make us believe in a brighter future, a more connected world, a reality where anything is possible. And in a world that can often feel like it's spinning out of control, that's a pretty powerful thing. But as we watch these narratives unfold, it's on us to remember that not every story has a happy ending, like . We've got to approach each new tale with a healthy dose of wonder and a hearty helping of skepticism. Because in the end, the most compelling story isn't always the most true - but it might just be the one that changes everything. As a society, we've always been drawn to a good story. From ancient myths to modern-day blockbusters, the power of narrative has the ability to captivate, inspire, and transform. In the world of technology, where innovation is the name of the game, storytelling has become the secret weapon behind some of the most buzzed-about startups. And the unexpected heroes of this new era? None other than the creative directors, the ad agency alchemists turned tech founders who have traded in their storyboards for the chance to rewrite the future. Armed with a knack for storytelling and a flair for the dramatic, these former ad agency stars are trading in their Cannes Lions for the chance to launch the next big thing. But as the hype around projects like Humane's AI Pin reaches fever pitch, it's worth asking: are these visionaries revolutionizing the tech landscape, or are they just spinning tales? Take Humane's AI Pin, for example. This little device, dreamt up by former Apple creative directors Imran Chaudhri and Bethany Bongiorno, has made quite the splash with its promise of a screenless, AI-driven future. But let's be real - it's not just the tech that's got everyone talking. It's the story. The vision of a world where technology seamlessly integrates into our lives, as natural as breathing. That's the kind of narrative that gets investors reaching for their checkbooks, the media clamoring for a headline and secures millions in funding. And Humane is far from alone in this new world order. Just look at the rise and fall of Elizabeth Holmes and Theranos. Sure, the revolutionary blood-testing technology never quite materialized, but damn if Holmes didn't spin one hell of a story. With her Steve Jobs-esque turtlenecks and promises of disruption, she had Silicon Valley eating out of the palm of her hand. Or what about Adam Neumann and WeWork? The man could sell a vision like no other, painting a picture of a workplace revolution that would “elevate the world's consciousness” (and the company's valuation to a cool $47 billion). But here's the thing about stories - they're only as good as the reality they're built on. When the curtain gets pulled back and the wizard is revealed to be just a guy with a smoke machine, things can get ugly fast. Just ask the folks who bought into the Theranos dream or the WeWork hype. When the story doesn't match the substance, it's not just disappointment that follows - it's disaster. Moreover, storytelling is not just about hype. Used responsibly, it can be a powerful tool for education and inspiration. When Steve Jobs introduced the iPhone, he didn't just sell a product; he painted a picture of how it would transform our lives. That narrative resonated because it was grounded in a genuine, revolutionary product. So what's the lesson here for the creative directors turned tech visionaries? The use of narrative as a launchpad is not without its risks: with great storytelling power comes great responsibility.The ability to craft a compelling narrative is a gift, but it's one that needs to be used wisely. The best stories, the ones that truly change the game, are the ones that are grounded in something real. Something tangible. Something that doesn't just tickle the imagination but actually follows through on its promises. This is the tightrope that creative directors in the tech world must walk. As for Humane's AI Pin? Only time will tell if it can live up to the hype. But one thing's for sure - in the wild west of tech startups, the storytellers are the new sheriffs in town. They're the ones who can make us believe in a brighter future, a more connected world, a reality where anything is possible. And in a world that can often feel like it's spinning out of control, that's a pretty powerful thing. But as we watch these narratives unfold, it's on us to remember that not every story has a happy ending, like . We've got to approach each new tale with a healthy dose of wonder and a hearty helping of skepticism. Because in the end, the most compelling story isn't always the most true - but it might just be the one that changes everything.

Narrative as a Launchpad: How Creative Directors Propel Tech Ventures Like Humane's AI Pin

একটি লঞ্চপ্যাড হিসাবে আখ্যান: কিভাবে ক্রিয়েটিভ ডিরেক্টররা হিউম্যানের এআই পিনের মতো টেক ভেঞ্চারকে এগিয়ে দেয়

Erzählung als Startrampe: Wie Kreativdirektoren Technologieunternehmen wie Humanes KI vorantreiben Pin

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Le récit comme rampe de lancement : comment les directeurs créatifs propulsent les entreprises technologiques comme l'IA de Humane

लॉन्चपैड के रूप में कथा: कैसे क्रिएटिव डायरेक्टर्स ह्यूमेन के एआई पिन जैसे तकनीकी उपक्रमों को आगे बढ़ाते हैं

物語を発射台として: クリエイティブ ディレクターが Humane の AI Pin のようなテクノロジー ベンチャーを推進する方法

런치패드로서의 내러티브: 크리에이티브 디렉터가 Humane의 AI 핀과 같은 기술 벤처를 추진하는 방법

Narrativa como plataforma de lançamento: como os diretores criativos impulsionam empreendimentos tecnológicos como o AI Pin da Humane

Нарратив как стартовая площадка: как креативные директора продвигают технологические предприятия, как пин от Humane об искусственном интеллекте

Bir Başlatma Paneli Olarak Anlatı: Yaratıcı Yönetmenler, Humane'nin Yapay Zeka Pinini Benzeri Teknoloji Girişimlerini Nasıl İlerletir?

Tường thuật như một bệ phóng: Cách các Giám đốc Sáng tạo Thúc đẩy các dự án mạo hiểm công nghệ giống như Ghim AI của Humane

叙事作为启动板：创意总监如何推动 Humane 的 AI Pin 等科技企业的发展

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