My academic research has largely been focused on applications of computational linguistics (a broader field of research that encompasses natural language processing, or NLP) in the areas of behavioural science and economics. Over the last couple of decades, the growing availability of large text archives and increase in (commoditised) computing power have given rise to new ways of testing out theories of behavioural psychology, and anthropology. Especially so in economics and finance.
In parallel, machine learning (and artificial intelligence more broadly) has gone mainstream and now facilitates very sophisticated classification and predictive analytics.
Selection of Research
BANK OF ENGLAND STAFF WORKING PAPER NO.704 -https://www.bankofengland.co.uk/working-paper/2018/news-and-narratives-in-financial-systems
This paper applies algorithmic analysis to large amounts of financial market text-based data to assess how narratives and sentiment play a role in driving developments in the financial system. We find that changes in the emotional content in market narratives are highly correlated across data sources. They show clearly the formation (and subsequent collapse) of very high levels of sentiment — high excitement relative to anxiety — prior to the global financial crisis. Our metrics also have predictive power for other commonly used measures of sentiment and volatility and appear to influence economic and financial variables. And we develop a new methodology that attempts to capture the emergence of narrative topic consensus which gives an intuitive representation of increasing homogeneity of beliefs prior to the crisis. With increasing consensus around narratives high in excitement and lacking anxiety likely to be an important warning sign of impending financial system distress, the quantitative metrics we develop may complement other indicators and analysis in helping to gauge systemic risk.
THE ROLE OF SENTIMENT IN THE US ECONOMY: 1920 TO 1934 -https://onlinelibrary.wiley.com/doi/10.1111/ehr.13160
This paper investigates the role of sentiment in the US economy from 1920 to 1934 using digitised articles from The Wall Street Journal. We derive a monthly sentiment index and use a 10-variable vector error correction model to identify sentiment shocks that are orthogonal to fundamentals. We show the timing and strength of these shocks and their resultant effects on the economy using historical decompositions. Intermittent impacts of up to 15 per cent on industrial production, 10 per cent on the S&P 500 and bank loans, and 37 basis points for the credit risk spread suggest a large role for sentiment.
NEWS AND NARRATIVES IN FINANCIAL SYSTEMS: EXPLOITING BIG DATA FOR SYSTEMIC RISK ASSESSMENT -https://doi.org/10.1016/j.jedc.2021.104119
This paper applies algorithmic analysis to financial market text-based data to assess how narratives and sentiment might drive financial system developments. We find changes in emotional content in narratives are highly correlated across data sources and show the formation (and subsequent collapse) of exuberance prior to the global financial crisis. Our metrics also have predictive power for other commonly used indicators of sentiment and appear to influence economic variables. A novel machine learning application also points towards increasing consensus around the strongly positive narrative prior to the crisis. Together, our metrics might help to warn about impending financial system distress.
A COMMON RISK FACTOR AND THE CORRELATION BETWEEN EQUITY AND CORPORATE BOND RETURNS -https://link.springer.com/article/10.1057/s41260-020-00151-8
A growing body of literature documents that security prices within and across asset classes behave similarly highlighting the importance of investors’ common expectations about future risk and returns in the asset pricing. Consequently, variations in the common expectations of investors have a major role in determining the correlation among asset prices. We examine the role of these common expectations in determining the relationship between firm-level equity and bond returns. We use a novel measure of the common expectations defined as the difference in relative frequencies of words signalling excitement and anxiety in a large dataset of articles published by Reuters. Further, we also consider the VIX index and the indices of Baker and Wurgler (J Finance 61(4):1645–1680, 2006) and Huang et al. (Rev Financ Stud 28(3):791–837, 2015) as potential common factors. The results show that changes in common expectations, proxied by our index and the VIX, are significant in predicting variations in the correlation between equity and bond returns. An improvement in investors’ optimism about future risk and returns causes a weaker correlation. The effect is stronger for the riskiest firms and flattens as firms’ credit risk improves. By decomposing our index into the excitement and anxiety components, we find that this predictive power is due to changes in the anxiety components.
BANK OF KOREA WORKING PAPER NO.2019-12 -http://papers.bok.or.kr/RePEc_attach/wpaper/english/wp-2019-12.pdf
We examine the causal dynamic relationship between economic policy uncertainty and economic activities, using a Local Projection model with external instruments. Based on the psychological theory of conviction narratives, we construct a Relative Sentiment Shift (RSS) index and use it as an instrumental variable that captures exogenous variations in economic policy uncertainty. Our empirical results suggest that an increase in economic policy uncertainty induces recessionary pressures in the economy: reductions in production and employment, a sharp stock market downturn, and a constrained financial market.