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

Current state of research in the field

Starting point and basis for the planned studies

Global financial markets and forecasting of financial assets have been consistently one of the most frequently discussed topics in the newer scientific and industry-related literature. In practice, the exact prediction of market movement and investors' behaviour remains almost impossible due to the high complexity of financial markets (Levis 1995, Brunnermeier and Oehmke 2009, Sornette and Huber 2017, Walter 2020). Also, from the scientific point of view, market efficiency theories shed new light on financial markets and investment decision processes. The efficient-market hypothesis that dates back to 1900, states that prices of financial assets reflect all information, so, in other words, it is impossible to outperform the market consistently (Bachelier 1900). Various researchers in the 1950s and 1960s have found mixed evidence supporting this theory (Ball and Brown 1968 and Fama, Fisher, Jensen, and Roll 1969). However, efficient market theory remains controversial and frequently criticised by market practitioners and researchers (Campbell and Shiller 1988, Jegadeesh and Titman 1993, Campbell 2000, Degutis and Novickyte 2014, Naseer and Bin Tariq 2015).

Financial markets are characterised by non-stationarity, large fluctuations, diverse market participants, instabilities, structural breaks and exaggerations (Kregel 2007, Andreou and Ghysels 2009, Schmitt et al 2013, Casini and Perron 2018). Breaks such as the dot-com bubble or the global financial crisis of 2007-08 in financial and macroeconomic time series can significantly affect modelling and forecasting activities with econometrician models and lead to huge forecasting errors and unreliability of the model in general (Valentinyi-Endrész 2004, Casini and Perron 2018, Boot and Pick 2020). The term 'structural breaks' is frequently used in the literature concerning the so-called asset (price) bubbles, their collapses and detection in financial markets. There are two types of bubbles depending on their background: (i) rational and (ii) irrational. Prices are typically well approximated by a random walk in the absence of bubbles, while periods of bubbles are characterised by explosive price paths (Breitung and Kruse 2013).

The human brain has always been highly tuned towards narratives, whether factual or not, to justify ongoing actions, even such basic actions as spending and investing. Stories motivate and connect activities to deeply felt values and needs. As a result, narratives "go viral" and spread worldwide with economic impact (Shiller 2017).

Our project is use-inspired: With an expected rapid growth of algorithmic trading and robo-advisory, numerous financial institutions attempt to complete quantitative-based strategies in addition to traditional sources of financial time series with some additional data such as alternative data (Denev and Amen 2020, Hansen and Borch 2022). Overall, Twitter, Social Media news, and financial news constitute a critical source of unstructured information possessing substantial information about the market environment, market sentiment and asset prices which is not necessarily incorporated in the market prices of assets (Sul et al. 2014, Sul 2017, Wan et al. 2021).

Areas where research is needed

Both academic and non-academic literature is evolving following the rapid development of artificial intelligence and computing power's evolution in their application to finance and financial markets (Milana and Ashta 2021). New data and technologies have the potential to disrupt and refine the existing financial services industry (Buchanan 2019, Hilpisch 2020, Moloi and Marwala 2020). Textual data covering Twitter, Telegram, social media platforms, financial and economic news provide a fine-grained real-time information channel that includes major news stories and minor events that, if properly modelled, can provide ex-ante information about the market even before the main newswires (Souza et al 2015). Text mining has emerged as an important field of research in the domain of Finance (Gupta et al. 2020). NLP offers the possibility of tracking the public's mood about a particular company or topic, influenced by published media information. From the perspective of financial use cases, sentiment analysis, text classification and keyword extraction are most frequently associated with financial applications in the scientific literature (Kalyanathaya et al., 2019, and Gupta et al. 2020). Text mining is frequently used in scientific research for forecasting of 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). Using the ever-expanding pool of textual data to improve the market dynamics has long been a practice in the financial industry. Uhl (2014) examines the statistical power of fundamental and behavioural factors with a novel sentiment dataset from over 3.6 million Reuters news articles regarding stock returns of the Dow Jones Industrials Index. Similarly, Wu et al. (2012) propose a model that combines the features of technical analysis of stocks with sentiment analysis, as stock prices also depend on the decisions of investors who read stock news articles.

Empirical evidence suggests that many macroeconomic and financial time series are subject to structural breaks (Pesaran and Timmermann 2004), for instance, financial returns and volatility (e.g. Lamoureux and Lastrapes, 1990, Horvath et al., 2006), the shape of the option implied volatility smile (Bates 2000), asset allocation (Pettenuzzo and Timmerman, 2005) and the equity premium (Pastor and Stambaugh 2001), the tail of the distribution and risk management measures such as Value at Risk (VaR) and Expected Shortfall (ES) (Andreou and Ghysels, 2005) as well as credit risk models and default measures (Andreou and Ghysels 2006). Ignoring structural breaks in time series might be highly costly for forecasting approaches. There is considerable evidence showing that forecasting approaches that condition on the most recent break are likely to perform better over unconditional approaches that use expanding or rolling estimation windows (Pesaran and Timmermann 2004). "Since structural change is pervasive in economic time series relationships, it can be quite perilous to ignore. Inferences about economic relationships can go astray, forecasts can be inaccurate, and policy recommendations can be misleading or worse" (Bruce Hansen (2001)). The current state of the literature groups methods for detection of structural breaks into two categories: (i) online detection approaches, methods detecting breaks in a live environment and (ii) offline methods identifying breakpoints post-ante (Aminikhanghahi and Cook, 2017).

On the other hand, tests developed by Andrews (1993) and Andrews (2003) can test for parameter instability when the number and the location of structural breaks are unknown. Hornik et al. (2002) present ideas and methods for testing for structural change in linear regression models from the generalised fluctuation test framework and the F-test (Chow-test) framework. Similarly, Bai and Perron (1998) provide the standard framework for structural break models. Some, but not all, of the model parameters are allowed to break at multiple possible breakpoints. Inclan and Tiao (1994) propose a procedure to detect variance changes based on an iterated cumulative sums of squares algorithm. From the industry- and regulator-oriented approach, the online detection of structural breaks is more relevant. There exists broad literature covering Bayesian online change points and structural break detection (Fearnhead and Liu 2007, Adams and MacKay 2007, Knoblauch and Damoulas 2018). For instance, models proposed by Fearnhead and Liu 2007 and Adams and MacKay 2007 learn a probability distribution over the "run length", which is the time since the most recent change point. The work of Adams and MacKay (2007) has been extended in several works to include online hyperparameter optimization (Turner et al. 2009) and Gaussian Process segment models (Garnett et al. 2009).

One scenario of structural breaks are asset price bubbles. A large and growing number of papers propose methods to detect "rational" bubbles. Shiller 1981 and LeRoy and Porter 1981 developed variance bounds tests in the context of the present value of dividends model. A milestone test of equity price bubbles that explicitly put a bubble in the alternative hypothesis was West's test (West 1987) which tries to tackle the "simultaneous test of model specification and bubbles" problem by testing the model and no-bubbles hypotheses sequentially. (West 1987 and Gürkaynak 2008). Campbell and Shiller 1988 and Campbell and Shiller 1989 provide a log-linear approximation to the dividend/price ratio and estimate a VAR system allowing for time-variation in the discount rates. They claim that the dividend/price ratio will be stationary in the absence of a bubble even if dividends and prices are unit-roots. Similarly, Cochrane 1992 explicitly tests for a bubble using the dividend/price ratio variance. Fusari et al 2020 use option price data to detect bubbles.

Narratives can help: The human brain has always been highly tuned towards narratives, whether factual or not, to justify ongoing actions, even such basic actions as spending and investing. Stories motivate and connect activities to deeply felt values and needs. 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). However, the field of economics should be expanded to include serious quantitative study of changing popular narratives.

Current state of own research

Detailed research plan

The proposed work program will comprise of four work packages (WP). Our suggested approach is conceptually new compared to the existing research in financial market analysis. First, there is a limited number of studies employing narratives and textual information in financial market studies. Second, studies on rational and irrational bubbles have largely ignored the evolution of emotions and narratives underlying these times of change. Third, as it concerns method employed in finance research there is a scarcity of automated text analyses and research has by and large not kept up with methodological advances in computer science to make explicit use of large text corpora for empirical analyses. The research topics and methodological contributions are timely and relevant for scientific, technological and but also practical/societal needs. While different prior research has studied these topics in isolation, our empirical set up will allow us to study the interplay of narratives, language, evolutions of language patterns, financial innovation, and financial market performance/outcomes, in a comprehensive broad but also cross-layer and cross-aspect framework where evolutions, causality, and potential interactions can be measured explicitly.

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?

Output: Bolesta, K., Taibi, G., Mare, C., Hadji Misheva, B., Osterrieder, J., Hopp, C. Hypothesizing Multimodal Influence: Assessing the Impact of Textual and Non-Textual Data on Financial Instrument Pricing Using NLP and Generative AI (January 17, 2024). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4698153

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.

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

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.

Ensuring implementation of research results

Structural break detection, their validation and precise dating is a topic of significant importance for the financial industry.. Our results can be directly put into practice by testing with new datasets that banks have access to and deploying the simulation engine to the IT environment of banks. Banks can, e.g. simulate the impact of their trading behaviour, conditioned on the actions of other significant players. Regulators, central banks and governments can apply the methodology to simulate stress scenarios, imbalances in the market and systemic risks. We have extensive industry contacts, such as with the research teams from ING Group, Credit Suisse, UBS, and multi-national organizations such as the ECB and ESM. All of them are explicitly interested in this research and have expressed their wish to be involved. A near-term implementation of the results and a continuation via an Innosuisse project (Swiss-based use-inspired funding) is not only expected but already firmly planned. About 50% of our research projects are use-inspired, and we have vast experience implementing our results within the industry. Changes outside science and their nature.

Our research also has the potential to be particularly relevant for governments, central banks and regulatory authorities. We will work with BIS and about 10 European regulators (from our COST and EU H2020 network) to show the results. In addition, we will write two policy papers and two position papers on a) how to better model asset price bubbles in financial markets and b) steps and measures to undertake that can lead to a more resilient financial system and fewer risks.

From a citizen's perspective, we expect our results to lead to a) a more stable financial system with fewer imbalances and risks and b) improved investment products with better performance and a more efficient finance industry. In addition, the European Commission has recently published three strategy plans on the capital market union, the approach to artificial intelligence, and a digital strategy. All three show the need for more quantitative solutions and more focus on artificial intelligence methods. In line with COST Goals.

Our research setting will also substantially contribute to additional goals of the COST Action 19130. We have envisioned many measures of knowledge transfer and cooperation and the training and support of young researchers. We will use the COST network to disseminate our results and have an impact on policymakers (e.g. by exchanging ideas with DGFISMA: Directorate General Financial Markets of the European Commission) We organise an annual conference and a monthly research seminar in Switzerland. In addition, we use the short-term scientific missions to invite researchers to Switzerland and visit researchers and companies abroad, all within the context of Action 19130. As a flagship event, we will host the annual management committee meeting at our university in September 2023. We expect more than 100 researchers from all over Europe to join us for one week of research events.

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