Exploring new methodologies for strengthening macroeconomic models


By examining the older macroeconomic models, used between 1776 and the 1990s, based on the classical macroeconomic frameworks, we see that they assume complete rationality - that humans will always attempt to maximise their utility and organisations will always attempt to maximise their profits. These models tend to be strictly positivist, using deductive approaches that use single methodologies with large quantities samples. Assuming complete rationality however does not always make sense, as human behaviour is not always rational. We all have biases, various motives for our behaviours, we all make mistakes and behave irrationally every now and then. 

Arianna ArzeniClassical, rationality-assuming macroeconomic models come into question when we observe boom and bust cycles and where the only possible cause of those cycles, human behaviour not being accounted for, is exogenous shocks. External shocks are notoriously difficult to integrate into macroeconomic models. Rare, with an extreme impact, and only hindsight aided predictability, are the three attributes of so-called ‘Black Swan’ events

These events shape our world and the adage that we live in unpredictable times becomes far more poignant when we account for the fact that such an event could occur out of the blue, and we would have no way of ever predicting it. An example of a Black Swan event is the dot.com bubble burst. It had an extreme impact – a rough calculation estimates that the cost amounted to US$1.75 trillion. Black Swan events are also not restricted to one specific sector, but can rather occur in many, including weather, technical, economic, political, internal fraud, technological and many more. However, due to such events’ unpredictability, it is nearly impossible to factor them into useful models.


Nevertheless, the observation that boom and busts occur with some regularity allows to deduce that actual macroeconomic cycles are the result of human behaviour with its own limitations. They lead to a strong empirical regularity, i.e. that output gaps and output growth are non-normally distributed.

Previous macroeconomic models attempted to explain this phenomenon only by invoking external shocks such as Black Swan events, which are non-normally distributed. However, models more recently proposed, offer an explanation based on a behavioural macroeconomic model, in which agents are assumed to have limited cognitive abilities and thus develop different beliefs. Such models produce waves of optimism and pessimismin in an endogenous way and therefore provide a better explanation of the observed non-normality of the output movements. 

Recently, central banks and financial institutions, in an attempt to reduce risk and the volatility of the boom and bust cycles, have started using models that are more flexible towards making assumptions on behaviour and policy. For example the OECD, ECB and BoE are using software that allows for movements between forward-looking, rational explanations and adaptive learning for consumers, firms and labour and financial markets. These models have the advantages of allowing for stochastic shocks which means different scenarios can be analysed based on the effects of a given shock on factors such as trade, FDI etc. 


However, in order to re-evaluate these prediction methods, one should look to the advances being made in behavioural economics and how it can help understand how people behave and how it is possible to anticipate their reactions. 

Alongside this, one should look to the spread of social media and the internet and how this could represent an opportunity for newer prediction models. From a purely statistical viewpoint, social media analytics models are more robust than those based on surveys as the samples are bigger and people are less exposed to the bias issue. In other words, behaviours are not influenced by the data collection process. For example, there are more than 200 million Facebook users in the United States which roughly represents half of the total population. No survey could ever be based on such a large sample.

The question is how to integrate such data into macroeconomic models for prediction purposes. Major advances in technology,such as natural language processing can provide an answer, as they have the ability to process vast sets of text data into meaningful information using sentiment analysis techniques. This data can then be incorporated into macroeconomic models and enable prediction accuracy to be significantly improved. 


What are the benefits that new methodologies used in prediction models can bring to asset managers? The benefits can be broken down into three areas: Investments, Compliance and regulation, Operations and Clients. 

Firstly, investor sentiment on social media can be analysed in order to make better decisions and improve product performance, and machine learning can be used to generate trading ideas. CACEIS’s new data analytics service is already incorporating social media data to benefit clients.

Secondly, advances in natural language processing allow us to better define investor suitability under new regulations being introduced under MiFID II. Models will also help asset managers better predict fund performance in the event of another financial crisis, which is required due to European regulations aimed at strengthening investor protection levels.

Finally, such models will enable a better analysis of client data, helping asset managers improve their client experience and retain/attract new assets. Alongside this, internal machine learning and big data capabilities will increase internal efficiency and reduce costs.