24.03 Liontrust Global Innovation Report - The Rise of AI 04.24 - Flipbook - Page 36
The chart below shows US real GDP since 1994 and our LLM
outlook score taken at the end of the prior year. While of course far
from perfect – the model, for example, failed miserably to navigate
the volatility of the Covid period – the forecast is surprisingly good
given the simplicity of the approach.
The two series have a correlation of 0.58, which is quite a good
correlation with GDP growth for a forward looking variable. Indeed,
the same correlation for the prior year-end Bloomberg consensus US
GDP forecast was 0.63. So it appears that using an LLM one can
build a pretty respectable economic forecasting tool within the hour.
US GDP and LLM Forecast 1994–2023
7%
GDP (left axis)
LLM forecast (right axis)
6%
5%
4%
3%
2%
1%
0%
-1%
-2%
-3%
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
-4%
Source: Liontrust research and Bloomberg, as at end of January 2024.
The obvious next step, and beyond our ambitions for the exercise,
would be to add the LLM forecast into a broader forecast
framework, such as adding it as an explanatory variable alongside
others in a regression model to combine the quantitative analysis
and qualitative debate in harmony.
But one could also go much further than we have here to incorporate
qualitative information into the forecast using LLMs. In particular, the
potential to measure and weigh sentiment from online text in the
news, financial analysis and social media using LLMs is interesting.
Existing approaches using natural language processing are well
known, but LLMs are likely to prove significantly better due to their
far superior handling of language and context. They may finally
36 - The rise of AI: Technology and Innovation Report
provide a suitable way to incorporate the economic impact of
Keynes’s famous though long-elusive “animal spirits” into forecasts.
Moreover, the example of such a simple application within a
domain of expertise with a non-trivial payoff serves as a small
indication of the power of LLMs to drive productivity in the coming
years. LLMs’ accessibility plus their efficacy and extremely wide
applicability is a powerful economic formula.
Forecasting will no doubt remain a difficult and perilous job, but
LLMs may help drive the frontier forward as they are beginning to
do for many challenging tasks across the economy.
