What is the probability that an investment of yours will be profitable? If it is lucrative, what is the return that it will generate?
One of the main complications that the finance industry faces is the problem of not being able to answer these questions beforehand. The inability of being certain in these situations leads analysts to put in a lot of effort to reduce uncertainty to the maximum of their capabilities.
However, there are two problems with this outlook. The first is that we don’t understand the concept of uncertainty very well, and the second is that profitable situations only exist when the results are uncertain which leaves us with nothing but a paradox.
Frank Knight, an economist, wrote about this in 1921 in his book-“ Risk, Uncertainty, and Profit”. He highlighted the thin line that separates the words- ‘risk’ and ‘uncertainty’, as he talked about the two types of uncertainty that surround us. The first type of uncertainty was the measurable one which he called “risk” and the second one he called “true uncertainty” which cannot “by any method be reduced to an objective, quantitatively determined probability”.
In the book, Frank mentions:
“Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated…. The essential fact is that ‘risk’ means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character... It will appear that a measurable uncertainty, or ‘risk’ proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all.”
To understand risk, consider rolling a pair of fair dice. Even before the dice are rolled, we are aware of the odds of every possible outcome. This awareness forms the basis of all dice games that we play. Knightian uncertainty (true uncertainty) on the other hand is different from risk. It is present in difficult situations wherein one is unable to even ascertain the possible outcomes of an event, let alone its probabilities. It exists in complex systems where lots of variables are at play- the economy, for example.
Knight viewed the role of entrepreneurship and profit-making in the economic process as a reward for the entrepreneur’s willingness to take a decision in the unpredictability inherent in the process of production that cannot be readily quantified. In places where the risks can be quantified then theoretically, it is possible to hedge them or diversify them away, i.e., they no longer remain ‘risks’ or at least not ones that should yield excess return to the entrepreneur.
From a business point of view, risk can be managed. Consider the case of a portfolio with unsystematic and systematic risk. While the unsystematic risk can be hedged or minimised by proper diversification of the portfolio, the systematic risk can be managed through beta hedging using the futures of indices. However, uncertainty in the business environment can neither be cardinally expressed nor be managed perfectly. Imperfectly, one can protect themselves from such situations by purchasing insurance on their assets such that even if any unforeseeable situation occurs, their assets are protected and losses are minimised.
TIMES OF KNIGHTIAN UNCERTAINTY
Great Financial Crisis
During the time of the global economic crisis of 2007-08, ‘uncertainty’ became a buzzword, as the world economy transitioned from one with insurance to one without. The recession stemmed partly due to the inability of financial institutions to effectively judge the riskiness of their investments. The banks heavily relied on their own models assuming that they were free of fault and all the known unknowns were taken care of.
However, after the bubble burst, the accuracy of the information they relied upon was called into question as it was realised that they were operating in the conditions of Knightian Uncertainty. Trading ceased due to the fact that ask prices were seen as unsuitable metrics for establishing ‘fair value’ of financial assets. This excessive uncertainty culminated in the freezing up of credit markets. As liquidity in the market decreased, the impact of the crisis was further aggravated.
Knightian Uncertainty was also largely prevalent during the Covid-19 Pandemic, it being a historically unique event responsible for the unforeseeable change. KU events associated with the COVID-19 pandemic, such as cases, deaths, and vaccination trials, were most often reported as driving stock prices in 2020 and contributed to stock market volatility.
In the case of such a non-repetitive event, market players could not use probability and rely on past data to assess market expectations and forecast returns. Instead, they resort to forming qualitative interpretations about whether such KU events will have a bullish or bearish impact on expected returns.
Knightian Uncertainty and Stock market volatility
A research paper published by INET on Stock Market Volatility due to Knightian Uncertainty in the year of the pandemic introduces a novel index based on expectations concordance using narrative analytics of Bloomberg News stock-market news reports over the 200-plus trading days in 2020. The researchers developed an Expectations Concordance Index (ECI) measuring the degree to which market participants' interpretations are more similar across an aggregate of historical events during the year 2020.
When market participants’ interpretations are more similar in the aggregate, whether bullish or bearish, their market expectations are reinforcing and lead to greater stock-market volatility. Conversely, when expectations are more conflicting, the views are somewhat offset, reducing volatility and producing a stabilising effect. The main empirical finding of this study is that greater ECI across an aggregate of all interpretations of KU events leads to greater stock market volatility.
For example, when periods of skyrocketing COVID-19 new cases and mortality rates coincide with the approval of stimulus packages, aggregate ECI is low, reflecting the opposing bearish and bullish viewpoints, respectively.
Overall, this research demonstrates the value of narrative analytics in understanding the role of Knightian uncertainty in determining financial market outcomes. It also emphasises how crucial it is to comprehend the distinction between risk and uncertainty. Each of them requires different responses - and if we confuse the two, we won’t be able to use the right approach.
And, that is what really increases the risks that we face!
Author: Aaliya Gambhir and Neeraj Agarwal
Illustration by: Kayna Arora