SNSF Narrative Digital Finance

Title

Narrative Digital Finance: a tale of structural breaks, bubbles & market narratives

Abstract

Large fluctuations, instabilities, trends and uncertainty of financial markets constitute a substantial challenge for asset management companies, pension funds and regulators. Nowadays, most asset management companies and financial institutions follow a so-called systematic trading approach in their investment decisions. Systematic trading refers to applying predefined, rule-based trading strategies for buy- and sell orders. However, automated or rules-based trading activities bring certain risks for market participants and the whole financial market. In times of increased market volatility, market turmoil or so-called market sell-offs, investors applying similar trading rules might undertake the same actions, escalating and increasing systemic market risk through such behavior. Such situations have been frequently observed on financial markets for instance, in March 2020 (sell-off related to the Covid pandemic), during the European Sovereign Debt crisis and the global financial crisis 2007-08. Research in economics and management has begun to embrace the role that narratives play in guiding individual and collective decision-making. McCloskey (2011) describes unforeseen growth in economic development yet goes on to explain that no economic theory is able to capture this extent. She argues that a change in rhetoric had basically freed a social class (the bourgeoisie) and given it a sense of dignity and liberty. As such, economic change, she argues, depends to a great extent on social narratives that shape ideas and the beliefs of people. Yet, despite the notion that narratives, individual and collective actions, and market outcomes are inextricably linked, our knowledge about the mechanisms or processes through which they interact and how narratives can inform opinions or sway current thinking is still evolving. Entrepreneurs, for example, may use verbal communication to achieve plausibility (i.e., generate the sense that a given interpretation of events appears acceptable) or resonance (i.e., obtain alignment with the beliefs of the target audience; see van Werven et al., 2019). They may do so through rhetoric such as storytelling (Navis & Glynn, 2011) or crafting compelling arguments (van Werven et al., 2015) as well as employing combinations of figurative language and gesturing (Clarke et al., 2021) as they manage and conform with the expectation of their audience.

Outcomes of invoking narratives are consequential. The literature has indeed documented various forms of verbal communication–including written texts such as social media posts and blogs, or business plans or spoken text (Garud et al., 2014; Clarke et al., 2019, Clarke et al., 2021) – as a crucial means to secure support and investment. The narratives or rhetoric employed in these stories are used as vehicles for assembling and communicating details about ideas and future possibilities (Garud et al., 2014). In summary, narratives help audiences make sense of situations and situate the description into the audience's social and cultural framework (Lounsbury and Glynn, 2001).

In the following, we, therefore, explore computational techniques to predict financial market outcomes using text, speech, and video/picture data. Advances in data processing and machine learning allow new ways of analysing data and may have profound implications for empirical testing of lightly studied, yet complex, empirical financial relationships. This project therefore integrates various forms of narratives into the context of financial market analysis, leverages machine learning techniques, and aims to show how narratives are inextricably interwoven in the continuously unfolding financial market evolutions. We will extend quantitative research through novel measurement techniques, the creation of new data sets, offering new solutions towards prediction problems, and the induction of new theories (Obschonka & Audretsch, 2020). We will also contribute to recent works that demonstrated the potential of theoretical and methodological advancements through the application of machine learning in the research practice (Mullainathan & Spiess, 2017; von Krogh, 2018). In pursuit of both practical 'relevance' of our research (Wiklund et al., 2019) and the contribution of "AI-integrated" research (Levesque et al. 2020), our approach will provide actionable insights.

Grant Link

Main research questions

1. In what sense are financial markets (ex-ante) predictable? 2. Is the ex-ante forecastability persistent, can it be applied for real use cases and to which extent? 3. How can structural break detection and changes in financial time series improve and complement modern portfolio theory?

Approach

In a first step, we will design a tool allowing us to collect all relevant data from various data sources. Indeed, collecting purely financial data, such as stock prices or macroeconomic indicators, can be easily performed using subscription-based platforms such as Bloomberg, Reuters or Investing.com. However, textual data will constitute a substantial challenge in terms of (i) collecting from the web, (ii) formatting, and (iii) pre-processing, including dating and categorising. For this purpose, we will develop an automated tool which will collect textual data, categorise them, date and store them in an easy to analyse format. We will manage our database with SQL solutions. The second step will focus on our research questions and the four building blocks listed below.

We will formulate numerous data-driven general/main and block-specific research questions within our hypothesis-driven project. The main research questions will be:

1. In what sense are financial markets (ex-ante) predictable?
2. Is the ex-ante forecastability persistent, can it be applied for real use cases and to which extent?
3. How can structural break detection and changes in financial time series improve and complement modern portfolio theory?

Block 1: Text data & text analytics

Text mining techniques are frequently used in scientific research for forecasting developments of various financial assets such as FX, equities, bonds, commodities see, for instance, Fung et al. 2003, Hajizadeh et al. 2010, Nassirtoussi et al. 2014, Kumar and Ravi 2016, Loughran and McDonald 2016 (Chan and Franklin 2011, Cambria and White 2014, Xing et al. 2017, Chen et al. 2020). Our solution uses NLP and text mining techniques for asset allocation and prediction to apply for structural breaks and change point detection combined with asset allocation methodology. For instance, those techniques are used for predicting cryptocurrency price bubbles using social media data (Biessey 2021). However, the field of classic financial assets tends to be under-researched. Therefore, we will collect relevant literature, review solution and answer the following research questions:

1. How can textual analysis and the application of natural language processing techniques be efficiently used for portfolio management, including risk management and asset allocation?
2. What are the most promising NLP / text analysis techniques?

Block 2: Structural breaks detection & asset price bubbles

The survey of econometric tests of asset price bubbles shows that, despite recent advances, econometric detection of asset price bubbles cannot be achieved with a satisfactory degree of certainty (Gürkaynak 2008). Furthermore, currently, there exist a relatively low number of scientific papers about the live detection of structural breaks in a systematic way. Most of the existing solutions have not been validated on real-world data. An obvious downside of such experiments is that the dynamics of the simulated data are often particular to the paper, and any model that corresponds to these dynamics has an unfair advantage (van den Burg and Williams 2020). Hence, we will tackle the issue of structural breaks and asset price bubbles in the three steps. In the first step, we will focus on post- ante structural detection methods for asset price bubbles to identify past breaks in real macroeconomic and financial time series. The breaks will be compared and based on consensus simplified if needed. In the second step, we will reapply well-established, known methods for live detection of breaks and check their ex-ante performance. Based on the current state of literature, we expect a relatively poor forecastability of breakpoints. Therefore, in the third step, we will involve NLP and text analysis techniques as a supporting or main method for detecting breakpoints. Within our research in this block, we will answer the following research questions:

1. How to detect, identify and date structural breaks in online and offline matters?
2. Detection of structural breaks / change points / asset prices bubbles in a live-matter using most recent (alternative) data (Twitter, News etc.)

Block 3: Narratives for structural breaks

The so-called "narratives block" will be highly dependent on results from block 1 and 2. Using newly acquired knowledge and experience with text analysis and NLP and insights into detecting structural changes, we will develop a framework with market narratives for detecting asset price bubbles. Narratives "go viral" and spread worldwide with economic impact (Shiller 2017). There is considerable evidence in the scientific literature showing that people respond strongly to narratives in the fields of marketing (Escalas 2007); journalism (Machill et al. 2007 ); education (McQuiggan et al. 2008); health interventions (Slater et al 2003); and philanthropy (Weber et al. 2006). We will answer the following research questions:

1. Can market narratives help predict financial market bubbles and their bust?
2. Can market narratives help detect financial market bubbles?
3. Can narratives sway investment opinions?

In order to achieve this, one needs to devise experiments for measuring narratives and then incorporate those measurements into predictive models aimed at explaining different aspects of financial market behaviour. Looking at the 3rd research question within this block, we can investigate the effectiveness of specific components of narrative strategies by carrying out an experiment with potential investors who will be given different investment options that: a) incorporate a narrative structure and b) emphasise different positive or negative emotions in the text. Put differently, the participants will have text and information presented to them, where the level of the manipulated component (e.g. emotional content) is low/high and a narrative appears/does not appear in the description of the investment. Once the data is collected, we can employ pre-trained Transformer models (GloVe Embeddings with Long Short-Term Memory Network (GVEL) or Bidirectional Encoder Representations from Transformers (BERT)) and finetune them to model the presence of narratives. This will also enable us to test how text impacts investment option perceptions by using text matching approaches that employ lower dimension summaries of texts (Roberts, Stewart, and Nielsen, 2020) or use low-dimensional representations as causally sufficient embeddings (Veitch, Sridhar, and Blei, 2020). Such experiments will provide evidence of the causal influence that different components of narratives and emotions exert on the appeal of these investments by influencing the judgements of potential investors. Furthermore, for narratives to be effective they need to resonate with potential investors (van Werven et al., 2019). We thus use a study to test the effectiveness of specific components of narrative strategies in an experimental design. We will introduce participants to different investment options that a) incorporate a narrative structure b) emphasise different positive or negative emotions in the text. Due to practical considerations (sample size), we will employ a 2x2 experimental design for each of the components tested (e.g. narrative element present vs. purely informational text; emotionality of communication low vs. high). The experiments will provide evidence of the causal influence that different components of narratives and emotions exert on the appeal of these investments by influencing the judgements of potential investors. This experiment might also inform us about narratives and biases, where for example, investors forego certain investment options (overlook correlations that may improve their risk-return profile in the portfolio) because of the narratives presented to them.

Block 4: Multidimensional AI and ML solutions in a fully integrated framework

As already mentioned in the literature review, AI and ML techniques possess a substantial potential to revolutionise financial markets (Milana and Ashta 2021). New technologies transform business models and markets for trading, credit and blockchain-based Finance, generate efficiencies, reduce friction, enhance product offerings, and refine the existing financial services industry (Buchanan 2019, Hilpisch 2020, Moloi and Marwala 2020). Since, in previous blocks, we look at detecting structural breaks and asset price bubbles from various perspectives and apply different techniques, it seems to be self-explanatory and expected to check if those methods can be combined into a fully integrated framework. Research questions:

1. Can a combined ML approach outperform each single method?
2. Do complex AI and ML approaches outperform simple forecast combinations?

Objectives

Our study will provide an extensive overview of structural breaks and asset prices, bubbles detection mechanisms and novel methods based on existing solutions, including their applicability documented for real-world case scenarios. Documentation will allow academics, financial authorities and regulators to perform better risk management and understand financial markets. Our solution for detecting structural breakpoints will be unique in numerous matters. First, we will exclusively focus on ex-ante forecasting (live detection), easily adjustable to a rapidly changing market environment. Second, combining existing data sources, developing new quantitative models, and new frameworks to understand markets.

Relevance and impact

Scientific relevance

Expected impacts for research in Finance

Potentially, the impact on Finance will be substantial. First and foremost, our approach will help to better understand financial markets, the role and impact of different market participants and how the dynamics of markets arise and can be explained. Our research can lead to a significant transformation of the way we model many of the research questions in Finance, most notably any that are related to risk management, new techniques and modelling, particularly in all the major fields of quantitative Finance, such as modelling financial markets, risk management, systemic risks, asset pricing, portfolio optimization, trading strategies, but also

political considerations and the interplay of market participants that have a substantial impact on markets such as governments and central banks. As a prime example, we expect that our academic results can help the ECB and governments to explain the dynamics of financial markets, and certain irrationalities and mitigate future risks. Furthermore, traditional linear econometric models are more and more challenged with our ever-more complex, non-linear world and our approach will help to augment classical theories and give insights from a different angle, reducing the dependency on models and their associated risks, that have shown up several times in the previous years, such as the financial crisis (2007-2009) or the European debt crisis in Europe (2010-2011).

Broader scientific impact

Impact for research in other related domains.

Our results will show how to build a structured, consistent framework for structural breaks detection using various methods and various data sets. On top of numerous approaches, we will propose a machine learning or artificial intelligence-based technique which would help to combine all information in a consistent manner. Since our entire approach is heavily driven by data and quantitative research questions, our research can impact other fields than Finance. We will research and investigate various approaches for text analysis which can be useful for any field requiring working with textual data. The detection of structural breaks or of any highly negative events can be easily transformed to various political and social science research activities.

Impact for education and teaching.

As it pertains to the development of PhD students, the hired PhDs working on this project will be integrated into an international, interdisciplinary and intersectoral network community of experts providing training and collaboration opportunities for a broad range of skills. For education and teaching, the research will be integrated into PhD courses on data science, machine learning and quantitative Finance. Specifically, we are planning to have a first PhD course available by spring 2023, that combines all the interdisciplinary fields we are considering here. Also, we are planning to organize several training schools for PhD students and young researchers, investigating the interdisciplinary fields of our research. The first one will take place in Switzerland in September 2023 and will be jointly with several participants of our COST Action, most notably Profs. Poti, Härdle, Hirsa, Marazzina. The research results will also be integrated into a new European Industrial Doctorate on financial data science, that we are planning jointly with University College Dublin, Humboldt-University Berlin, University of Bucharest, to be submitted to the Marie Sklodowska- Curie Action in the European Horizon Europe framework programme. We are confident that this project might lay the foundation not subsequently help to inspire a next generation of finance researcher. Early career researchers will find themselves in the position where they will develop theoretical and technical skills that allow them to contribute expertise at the intersection of finance and data science, but that also will enable them to support the research of other network partners and scientifically contribute to theories and methods to neighbouring areas and research fields.

Broader impact

Need of research for finance practitioners/industry.

The finance industry is challenged by high risks, ongoing and repetitive financial crises and market efficiencies. Despite many years of financial markets research, substantial regulatory investments and efforts over the last 20 years, we are far from a sufficient understanding of financial markets. Our research will help to improve the general understanding of financial markets. The research question is directly defined and shaped by the Finance industry (JP Morgan, Credit Suisse, UBS, Goldman Sachs), looking into new academic models on MARL to improve their trading strategies and risk management policies. As for reinforcement learning specifically, it has transformed several practice areas, but not yet in Finance, despite various ongoing efforts. We aim to solve the practical problem of optimal risk management during financial shocks in global markets by producing scientific insights concerning the behaviour of differently motivated agents in financial ecosystems.

References

Adams, Ryan Prescott, and David JC MacKay. "Bayesian Online Changepoint Detection." ArXiv Preprint ArXiv:0710.3742, 2007. Aminikhanghahi, Samaneh, and Diane J. Cook. "A Survey of Methods for Time Series Change Point Detection." Knowledge and Information Systems 51, no. 2 (2017): 339–67. Andreou, Elena, and Eric Ghysels. "Quality Control for Financial Risk Management: Monitoring Disruptions in the Distribution of Risk Exposure." University of Cyprus/University of North Carolina, 2002. ———. "Quality Control for Structural Credit Risk Models." Journal of Econometrics 146, no. 2 (2008): 364–75. ———. "Structural Breaks in Financial Time Series." Handbook of Financial Time Series, 2009, 839–70. Andrews, Donald WK. "Tests for Parameter Instability and Structural Change with Unknown Change Point." Econometrica: Journal of the Econometric Society, 1993, 821–56. Bachelier, Louis. “Théorie de La Spéculation.” In Annales Scientifiques de l’École Normale Supérieure, 17:21–86, 1900. Bai, Jushan, and Pierre Perron. "Estimating and Testing Linear Models with Multiple Structural Changes." Econometrica, 1998, 47–78. Bates, David S. "Post-'87 Crash Fears in the S&P 500 Futures Option Market." Journal of Econometrics 94, no. 1–2 (2000): 181–238. Boot, Tom, and Andreas Pick. "Does Modeling a Structural Break Improve Forecast Accuracy?" Journal of Econometrics 215, no. 1 (2020): 35–59. Brunnermeier, Markus, Martin Oehmke, and G. Jel. "Complexity in Financial Markets." Princeton University 8 (2009). Buchanan, Bonnie. "Artificial Intelligence in Finance," 2019. Cambria, Erik, and Bebo White. "Jumping NLP Curves: A Review of Natural Language Processing Research." IEEE Computational Intelligence Magazine 9, no. 2 (2014): 48–57. Campbell, John Y. "Asset Pricing at the Millennium." The Journal of Finance 55, no. 4 (2000): 1515–67. Campbell, John Y., and Robert J. Shiller. "Stock Prices, Earnings, and Expected Dividends." The Journal of Finance 43, no. 3 (1988): 661–76. ———. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors." The Review of Financial Studies 1, no. 3 (1988): 195–228. Casini, Alessandro, and Pierre Perron. "Structural Breaks in Time Series." ArXiv Preprint ArXiv:1805.03807, 2018. Chan, Samuel WK, and James Franklin. "A Text-Based Decision Support System for Financial Sequence Prediction." Decision Support Systems 52, no. 1 (2011): 189–98. Chen, Chung-Chi, Hen-Hsen Huang, and Hsin-Hsi Chen. "NLP in FinTech Applications: Past, Present and Future." ArXiv Preprint ArXiv:2005.01320, 2020. Chow, Gregory C. "Tests of Equality between Sets of Coefficients in Two Linear Regressions." Econometrica: Journal of the Econometric Society, 1960, 591–605. Clarke, J. S., Llewellyn, N., Cornelissen, J., & Viney, R. (2021). Gesture analysis and organizational research: The development and application of a protocol for naturalistic settings. Organizational Research Methods, 24(1), 140-171. Clarke, J.S., Cornelissen, J. & Healey, M.P. (2019). Actions speak louder than words: How figurative language and gesturing in entrepreneurial pitches influences investment judgments. Academy of Management Journal, 62, 355-360 Cochrane, John H. "Explaining the Variance of Price–Dividend Ratios." The Review of Financial Studies 5, no. 2 (1992): 243–80. Denev, Alexander, and Saeed Amen. The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers. John Wiley & Sons, 2020. Escalas, Jennifer Edson. "Self-Referencing and Persuasion: Narrative Transportation versus Analytical Elaboration." Journal of Consumer Research 33, no. 4 (2007): 421–29. Fama, Eugene F., Lawrence Fisher, Michael Jensen, and Richard Roll. "The Adjustment of Stock Prices to New Information." International Economic Review 10, no. 1 (1969). Fama, Eugene F., and Kenneth R. French. "Common Risk Factors in the Returns on Stocks and Bonds." Journal of Financial Economics 33, no. 1 (1993): 3–56. Fearnhead, Paul, and Zhen Liu. "Online Inference for Multiple Changepoint Problems." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, no. 4 (2007): 589–605. Fung, G. Pui Cheong, J. Xu Yu, and Wai Lam. "Stock Prediction: Integrating Text Mining Approach Using Real-Time News." In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings., 395–402. IEEE, 2003. Fusari, Nicola, Robert Jarrow, and Sujan Lamichhane. "Testing for Asset Price Bubbles Using Options Data." Johns Hopkins Carey Business School Research Paper, no. 20–12 (2020). Garnett, Roman, Michael A. Osborne, and Stephen J. Roberts. "Sequential Bayesian Prediction in the Presence of Changepoints." In Proceedings of the 26th Annual International Conference on Machine Learning, 345–52, 2009. Garud, R., Schildt, H. A., & Lant, T. K. (2014). Entrepreneurial storytelling, future expectations, and the paradox of legitimacy. Organization Science, 25(5), 1479-1492. Gupta, Aaryan, Vinya Dengre, Hamza Abubakar Kheruwala, and Manan Shah. "Comprehensive Review of Text-Mining Applications in Finance." Financial Innovation 6, no. 1 (2020): 1–25. Gürkaynak, Refet S. "Econometric Tests of Asset Price Bubbles: Taking Stock." Journal of Economic Surveys 22, no. 1 (2008): 166–86. Hamed, AL-Rubaiee, Renxi Qiu, and Dayou Li. "Analysis of the Relationship between Saudi Twitter Posts and the Saudi Stock Market." In 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), 660–65. IEEE, 2015. Hansen, Kristian Bondo, and Christian Borch. "Alternative Data and Sentiment Analysis: Prospecting Non-Standard Data in Machine Learning-Driven Finance." Big Data & Society 9, no. 1 (2022): 20539517211070700. Hilpisch, Yves. Artificial Intelligence in Finance. O'Reilly Media, 2020. Hopp, C., Antons, D., Kaminski, J., & Salge, T. O. (2018a). The topic landscape of disruption research—A call for consolidation, reconciliation, and generalization. Journal of Product Innovation Management, 35(3), 458-487. Hopp, C., Antons, D., Kaminski, J., & Oliver Salge, T. (2018b). Disruptive innovation: Conceptual foundations, empirical evidence, and research opportunities in the digital age. Journal of Product Innovation Management, 35(3), 446-457. Hopp, C., Kaminski, J., & Piller, F. (2019). Accentuating lead user entrepreneur characteristics in crowdfunding campaigns–The role of personal affection and the capitalization of positive events. Journal of Business Venturing Insights, 11, e00106. Horvath, Lajos, Piotr Kokoszka, and Aonan Zhang. "Monitoring Constancy of Variance in Conditionally Heteroskedastic Time Series." Econometric Theory 22, no. 3 (2006): 373–402. Inclan, Carla, and George C. Tiao. "Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance." Journal of the American Statistical Association 89, no. 427 (1994): 913–23. Jarrow, Robert A., Philip Protter, and Kazuhiro Shimbo. "Asset Price Bubbles in Incomplete Markets." Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics 20, no. 2 (2010): 145–85. Jegadeesh, Narasimhan, and Sheridan Titman. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." The Journal of Finance 48, no. 1 (1993): 65–91. Kaminski, J., Hopp, C., & Tykvova, T. (2019). New technology assessment in entrepreneurial financing–Does crowdfunding predict venture capital investments?. Technological Forecasting and Social Change, 139, 287-302. Kaminski, J. C., & Hopp, C. (2020). Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals. Small Business Economics, 55(3), 627-649. Knoblauch, Jeremias, and Theodoros Damoulas. "Spatio-Temporal Bayesian online Changepoint Detection with Model Selection." In International Conference on Machine Learning, 2718–27. PMLR, 2018. Kregel, Jan A. "The Natural Instability of Financial Markets," 2007. Kumar, B. Shravan, and Vadlamani Ravi. "A Survey of the Applications of Text Mining in Financial Domain." Knowledge-Based Systems 114 (2016): 128–47. Lamoureux, Christopher G., and William D. Lastrapes. "Persistence in Variance, Structural Change, and the GARCH Model." Journal of Business & Economic Statistics 8, no. 2 (1990): 225–34. Lei, Vivian, Charles N. Noussair, and Charles R. Plott. "Nonspeculative Bubbles in Experimental Asset Markets: Lack of Common Knowledge of Rationality vs. Actual Irrationality." Econometrica 69, no. 4 (2001): 831–59. Lévesque, M., Obschonka, M., & Nambisan, S. (2020). Pursuing impactful entrepreneurship research using artificial intelligence. Entrepreneurship Theory and Practice, 1042258720927369. Loughran, Tim, and Bill McDonald. "Textual Analysis in Accounting and Finance: A Survey." Journal of Accounting Research 54, no. 4 (2016): 1187–1230. Lounsbury, M., & Glynn, M. A. (2001). Cultural entrepreneurship: Stories, legitimacy, and the acquisition of resources. Strategic management journal, 22(6‐7), 545-564. Machill, Marcel, Sebastian Köhler, and Markus Waldhauser. "The Use of Narrative Structures in Television News: An Experiment in Innovative Forms of Journalistic Presentation." European Journal of Communication 22, no. 2 (2007): 185–205. McCloskey, D. N. (2011). The Rhetoric of the Economy and the Polity. Annual Review of Political Science, 14, 181-199. McQuiggan, Scott W., Jonathan P. Rowe, Sunyoung Lee, and James C. Lester. "Story-Based Learning: The Impact of Narrative on Learning Experiences and Outcomes." In International Conference on Intelligent Tutoring Systems, 530–39. Springer, 2008. Milana, Carlo, and Arvind Ashta. "Artificial Intelligence Techniques in Finance and Financial Markets: A Survey of the Literature." Strategic Change 30, no. 3 (2021): 189–209. Moloi, Tankiso, and Tshilidzi Marwala. Artificial Intelligence in Economics and Finance Theories. Springer, 2020. Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. Nassirtoussi, Arman Khadjeh, Saeed Aghabozorgi, Teh Ying Wah, and David Chek Ling Ngo. "Text Mining for Market Prediction: A Systematic Review." Expert Systems with Applications 41, no. 16 (2014): 7653–70. Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics, 55(3), 529-539. Pástor, L’luboš, and Robert F. Stambaugh. "The Equity Premium and Structural Breaks." The Journal of Finance 56, no. 4 (2001): 1207–39. Pesaran, M. Hashem, and Allan Timmermann. "How Costly Is It to Ignore Breaks When Forecasting the Direction of a Time Series?" International Journal of Forecasting 20, no. 3 (2004): 411–25. Peterson, James D., and Cheng-Ho Hsieh. "Do Common Risk Factors in the Returns on Stocks and Bonds Explain Returns on REITs?" Real Estate Economics 25, no. 2 (1997): 321–45. Pettenuzzo, Davide, and Allan Timmermann. "Predictability of Stock Returns and Asset Allocation under Structural Breaks." Journal of Econometrics 164, no. 1 (2011): 60–78. Quandt, Richard E. "Tests of the Hypothesis That a Linear Regression System Obeys Two Separate Regimes." Journal of the American Statistical Association 55, no. 290 (1960): 324–30. Roberts, M. E., Stewart, B. M., & Nielsen, R. A. (2020). Adjusting for confounding with text matching. American Journal of Political Science, 64(4), 887-903. Rose, S., Wentzel, D., Hopp, C., & Kaminski, J. (2021). Launching for success: The effects of psychological distance and mental simulation on funding decisions and crowdfunding performance. Journal of Business Venturing, 36(6), 106021. Schmitt, Thilo A., Desislava Chetalova, Rudi Schäfer, and Thomas Guhr. "Non-Stationarity in Financial Time Series: Generic Features and Tail Behavior." EPL (Europhysics Letters) 103, no. 5 (2013): 58003. Shah, Darsh, Rutvik Dixit, Aneri Shah, Priyam Shah, and Manan Shah. "A Comprehensive Analysis Regarding Several Breakthroughs Based on Computer Intelligence Targeting Various Syndromes." Augmented Human Research 5, no. 1 (2020): 1–12. Shiller, Robert J. "Narrative Economics." American Economic Review 107, no. 4 (2017): 967–1004. Singh, Sudhir. "Investor Irrationality and Self-Defeating Behavior: Insights from Behavioral Finance." Journal of Global Business Management 8, no. 1 (2012): 116. Slater, Michael D., David B. Buller, Emily Waters, Margarita Archibeque, and Michelle LeBlanc. "A Test of Conversational and Testimonial Messages versus Didactic Presentations of Nutrition Information." Journal of Nutrition Education and Behavior 35, no. 5 (2003): 255–59. Sokol, Nathaniel E. "High Frequency Litigation: SEC Responses to High Frequency Trading as a Case Study in Misplaced Regulatory Priorities." Colum. Sci. & Tech. L. Rev. 17 (2015): 402. Souza, Thársis Tuani Pinto, Olga Kolchyna, Philip C. Treleaven, and Tomaso Aste. "Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry." ArXiv Preprint ArXiv:1507.00784, 2015. Sul, Hong Kee, Alan R. Dennis, and Lingyao Yuan. "Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns." Decision Sciences 48, no. 3 (2017): 454–88. Sul, Hongkee, Alan R. Dennis, and Lingyao Ivy Yuan. "Trading on Twitter: The Financial Information Content of Emotion in Social Media." In 2014 47th Hawaii International Conference on System Sciences, 806–15. IEEE, 2014. Talaviya, Tanha, Dhara Shah, Nivedita Patel, Hiteshri Yagnik, and Manan Shah. "Implementation of Artificial Intelligence in Agriculture for Optimisation of Irrigation and Application of Pesticides and Herbicides." Artificial Intelligence in Agriculture 4 (2020): 58–73. Tur, B., Harstad, J., & Antonakis, J. (2021). Effect of charismatic signaling in social media settings: Evidence from TED and Twitter. The Leadership Quarterly, 101476. Turner, Ryan, Yunus Saatci, and Carl Edward Rasmussen. "Adaptive Sequential Bayesian Change Point Detection." In Temporal Segmentation Workshop at NIPS, 1–4. Citeseer, 2009. Uhl, Matthias W. "Reuters Sentiment and Stock Returns." Journal of Behavioral Finance 15, no. 4 (2014): 287–98. Valentinyi-Endrész, Marianna. “Structural Breaks and Financial Risk Management.” Magyar Nemzeti Bank (Central Bank of Hungary), 2004. Van Werven, R., Bouwmeester, O., & Cornelissen, J. P. (2015). The power of arguments: How entrepreneurs convince stakeholders of the legitimate distinctiveness of their ventures. Journal of Business Venturing, 30(4), 616-631. van Werven, R., Bouwmeester, O., & Cornelissen, J. P. (2019). Pitching a business idea to investors: How new venture founders use micro-level rhetoric to achieve narrative plausibility and resonance. International Small Business Journal, 37(3), 193-214. Veitch, V., Sridhar, D., & Blei, D. (2020, August). Adapting text embeddings for causal inference. In Conference on Uncertainty in Artificial Intelligence (pp. 919-928). PMLR. Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries. Vijayan, R., and M. A. Potey. "Improved Accuracy of FOREX Intraday Trend Prediction through Text Mining of News Headlines Using J48." Int J Adv Res Comput Eng Technol 5, no. 6 (2016): 1862–66. Wan, Xingchen, Jie Yang, Slavi Marinov, Jan-Peter Calliess, Stefan Zohren, and Xiaowen Dong. "Sentiment Correlation in Financial News Networks and Associated Market Movements." Scientific Reports 11, no. 1 (2021): 1–12. Weber, Keith, Matthew M. Martin, Members of COMM 401, and Michael Corrigan. "Creating Persuasive Messages Advocating Organ Donation." Communication Quarterly 54, no. 1 (2006): 67–87. West, Kenneth D. "A Specification Test for Speculative Bubbles." The Quarterly Journal of Economics 102, no. 3 (1987): 553–80. Wiklund, J., Wright, M., & Zahra, S. A. (2019). Conquering relevance: Entrepreneurship research's grand challenge. Entrepreneurship Theory and Practice, 43(3), 419-436. Woodward, Megan. "The Need for Speed: Regulatory Approaches to High Frequency Trading in the United States and the European Union." Vand. J. Transnat’l L. 50 (2017): 1359. Wu, Jheng-Long, Chen-Chi Su, Liang-Chih Yu, and Pei-Chann Chang. "Stock Price Predication Using Combinational Features from Sentimental Analysis of Stock News and Technical Analysis of Trading Information." International Proceedings of Economics Development and Research 55 (2012): 39–43. Xing, Frank Z., Erik Cambria, and Roy E. Welsch. "Natural Language Based Financial Forecasting: A Survey." Artificial Intelligence Review 50, no. 1 (2018): 49–73. Zeileis, Achim, Friedrich Leisch, Kurt Hornik, and Christian Kleiber. “Strucchange: An R Package for Testing for Structural Change in Linear Regression Models.” Journal of Statistical Software 7 (2002): 1–38. Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power: Barack Obama's Books of 2019. Profile books, 2019.