A key ingredient to understanding price elasticity is knowing which assets are close substitutes and which investors tend to behave similarly. However, observed characteristics such as sector or balance sheet variables do not tell the whole story. For example, before the Covid-19 pandemic, data on companies’ sensitivities to lockdowns were not available, but investors responded in real time to their assessment of “winners” and “losers”.
A recent paper from the Bank for International Settlements (BIS) explains how the artificial intelligence method of “embedding” assets and investors in a vector space helps uncover such market reactions. Asset embeddings enable central banks to better understand how asset prices change. And investor embeddings offer insights on investors’ likely response to central bank interventions or other market movements.
The paper illustrates the use of embedding techniques in a number of use cases. Since asset embeddings represent investors’ views of securities that are close substitutes, it can predict what investors buy after selling some of their portfolio to central banks in asset purchase programmes. Similarly, these embeddings offer a more nuanced glimpse into so-called “crowded trades”, by finding companies that investors judge to be exposed to the same factors, even in the absence of data showing direct similarity. These models can also be used to design stress testing models. And beyond financial markets, embeddings uncovered using these techniques can provide insights on the dynamics of relative prices and consumer heterogeneity.