Ivan Yotzov, Nick Bloom, Philip Bunn, Paul Mizen, Pawel Smietanka and Greg Thwaites
Text data is often raw and unstructured, and yet it is the key means of human communication. Textual analysis techniques are increasingly being used in economic and financial research in a variety of different ways. In this post we apply these techniques to a new setting: the text comments left by respondents to the Decision Maker Panel (DMP) Survey, a UK-wide monthly business survey. Using over 20,000 comments, we show that: (i) these comments are a rich and unexplored data source, (ii) Brexit has been the dominant topic of comments since 2016, (iii) text-based indices match existing uncertainty measures from the DMP at both the aggregate and firm level, and (iii) sentiment among UK firms has been declining since 2016.
Giorgis Hadzilacos, Ryan Li, Paul Harrington, Shane Latchman, John Hillier, Richard Dixon, Charlie New, Alex Alabaster and Tanya Tsapko
The 2015/16 storms caused the most extreme flooding on record, with parts of the UK impacted by heavy precipitation and extreme wind over a four-month period. These extreme weather events occurred in quick succession, hindering relief efforts and accruing £1.3 billion in insured losses. Without adequate mitigation, such events may result in claims handling strain and capital risk for insurers. Recent research finds that above-average windstorm seasons are typically accompanied by above-average inland flooding. That raises a challenge for insurers: should they have adequate risk mitigation measures in place for periods that are both windy and wet? We argue that insurers need to reassess their model assumptions, especially as climate change might make wet years more frequent than in the past.
The right stance for monetary policy is highly uncertain, and so it is no surprise that members of monetary policy committees – like the Bank of England’s Monetary Policy Committee (MPC) – regularly disagree about the best course of action. Asking a committee to decide allows different opinions to be aired and challenged, with a majority vote needed to determine policy. But how should we expect those disagreements and votes to change in periods of higher uncertainty? Should we expect more 9–0 unanimous votes? Or more 5–4 close contests? We address these questions in this post and find that the degree of disagreement is little changed in periods of high uncertainty, and nor are dissenting votes. There is, however, some difference in how voting decisions are formed when uncertain, with both individual and committee-wide views having less explanatory power for votes.
Marcus Buckmann, Paula Gallego Marquez, Mariana Gimpelewicz and Sujit Kapadia
Bank failures are very costly for society. Following the 2007/2008 global financial crisis, international regulators introduced a package of new banking regulations, known as Basel III. This includes a wider range of capital and liquidity requirements to protect banks from different risks. But could the additional complexity be unnecessary or even increase risks, as some have argued? In a recent staff working paper, we assess the value of multiple regulatory requirements by examining how different combinations of metrics might have helped prior to the 2007/2008 crisis in gauging banks that subsequently failed. Our results generally support the case for a small portfolio of different regulatory metrics: having belts and braces (or suspenders) can strengthen the resilience of the banking system.
Data plays a central role in all technical aspects of insurance and actuarial work. However, utilisation is often still confined to aggregate premium and claims data. Not so in the case of telematics. Say the phrase ‘black box’ and most people will think of flight recorders fitted to aircraft. But Motor insurers also use the millions of data points generated by black boxes, fitted to more than a million cars in the UK, to price risks. What’s more Marine insurers are getting in on the act. In this post we take an actuarial vantage to explore the use of telematics data and consider whether insurers could be using this ‘gold mine’ of information even more widely.
Arthur Turrell, Eleni Kalamara, Chris Redl, George Kapetanios and Sujit Kapadia
Every day, journalists collate information about the world and, with nimble keystrokes, re-express it succinctly as newspaper copy. Events about the macroeconomy are no exception. So could there be additional valuable information about the economy contained in the news? In a recent research paper, we ask whether newspaper stories could help to predict future macroeconomic developments. We find that news can be used to enhance statistical economic forecasts of growth, inflation and unemployment — but only by using supervised machine learning techniques. We also find that the biggest forecast improvements occur when it matters most — during stressed periods.
Kristina Bluwstein, Marcus Buckmann, Andreas Joseph, Miao Kang, Sujit Kapadia and Özgür Şimşek
Financial crises are recurrent events in economic history. But they are as rare as a Kraftwerk album, making their prediction challenging. In a recent study, we apply robots — in the form of machine learning — to a long-run dataset spanning 140 years, 17 countries and almost 50 crises, successfully predicting almost all crises up to two years ahead. We identify the key economic drivers of our models using Shapley values. The most important predictors are credit growth and the yield curve slope, both domestically and globally. A flat or inverted yield curve is of most concern when interest rates are low and credit growth is high. In such zones of heightened crisis vulnerability, it may be valuable to deploy macroprudential policies.
Zahid Amadxarif, James Brookes, Nicola Garbarino, Rajan Patel and Eryk Walczak
The banking reforms that followed the financial crisis of 2007-08 led to an increase in UK banking regulation from almost 400,000 to over 720,000 words. Did the increase in the length of regulation lead to an increase in complexity?
Cristiano Cantore, Federico Di Pace, Riccardo M Masolo, Silvia Miranda-Agrippino and Arthur Turrell
The Covid-19 crisis has led to a swift shift in the emphasis of macroeconomic research. At the centre of this is a new field of inquiry called ‘epi-macro’ that combines epidemiological models with macroeconomic models. In this post, we give a brief introduction to some of the earliest papers in this fast-growing literature.
Zahid Amadxarif,Paula Gallego Marquez and Nic Garbarino
“We’ve done a lot to lower prudential barriers to entry into the banking sector […] but have we done enough to lower the equivalent barriers to growth?” asked PRA CEO Sam Woods in a recent speech. To make regulation proportionate, policymakers adapt regulatory requirements to the risks posed by each firm. But regulators face a trade-off between addressing systemic risks in a proportionate way and limiting regulatory complexity. New thresholds can create complexity and cliff-edge effects that can discourage healthy firms from growing. We identify regulatory thresholds for UK banks and building societies using textual analysis on a new dataset that contains the universe of prudential rules.