Liquidation and Portfolio Similarity in the Insurance Industry

Investors and regulators are increasingly interested in identifying characteristics of entities that contribute to financial stability. 

A growing body of theoretical and empirical literature explores how interconnectedness affects the selling behavior of banks during liquidation shocks (Allen, Babus, Carletti 2012; Acharya, Thakor 2016; Silva 2019). 

In times of stress, other non-banking institutions, such as insurance companies, sell assets. These companies are subject to risk-based capital requirements. Moreover, these are linked to the broader financial system through their investments in specific asset types (Acharya et al., 2011). 

Insurance companies don’t need to fail to spread risk throughout the system for a negative impact to occur. It may suffice for them to “fire-sale” assets (Kartasheva 2014). 

Empirical research confirms that insurers’ trading behavior during stress can impact prices and cause spillovers to other market players (Ellul, Jotikasthira, Lundblad 2011; Ellul, Jotikasthira, Lundblad, Wang 2015; Merrill, Nadauld, Stulz, Sherlund 2013; Manconi, Massa, Yasuda 2012).

The concept of asset liquidation and portfolio summary 

Asset liquidation is more like a process where a company sells off a company asset and further converts the same into cash. The company can then use this asset to pay off debts, distribute all the remaining assets, and more. 

This can be a voluntary process or forced through bankruptcy. The risk of liquidation is the ultimate possibility that a company is not being able to meet its obligations. As a result, they had to sell the assets or go bankrupt. 

On the other hand, portfolio similarity is more of a measurement showing the similarity between the holdings of multiple funds and portfolios. 

For instance, let’s say there are two insurers who have identical portfolios. These portfolios would have a cosine similarity of one. On the other hand, if the insurers have different portfolios, then they will have a cosine similarity of zero. 

The regulators can use similar portfolios and measures in order to predict when the companies are going to sell their assets altogether. You can say this can be a great solution during the time of market stress. 

Methodology

This paper addresses this issue by examining whether insurers with more similar portfolios are more likely to sell the assets they share. We calculate the cosine similarity of a pair of insurers’ holdings. For the same, we will use 2002-2014 data from the National Association of Insurance Commissioners.

The cosine similarity is bounded between zero and one: a similarity of 1 indicates identical portfolios, while a similarity of 0 means completely different portfolios. 

We calculate the year-end similarities of each pair across broad asset classes and granular issuers. We demonstrate that portfolio similarity is related to insurer characteristics. These characteristics include 

• Joint size, 
• Portfolio composition, and 
• Similarity in business lines.

We also show that our measure can accurately predict the frequency. This also includes the amount of sales the company has made with similar portfolios. We construct a measure for joint sales using information from insurer trades. 

This measure is the dot product of the vectors of quarterly net sales at the asset class and security issuer levels. We find an inverse relationship between the portfolio similarity of a pair and their quarterly joint sales in the following year.

High-Risk vs. Low-Risk Assets

The overlap between insurers’ portfolios may be attributed to liability matching needs, risk-seeking behaviors (Becker and Ivashina 2015), or both. 

We decompose high-risk from each insurer’s portfolio and low-risk assets based on their likelihood to affect prices due to liquidity and credit quality. 

We calculate portfolio similarity across high-risk and low-risk assets. After that, we regress these similarities on liability similarity to determine the expected and unexpected portions of the similarities. 

High-risk portfolio similarity, which supports overlapping liabilities, increases joint sales the most. Conversely, the “safe” or expected portfolio similarity across low-risk assets is negatively related to standard sales. 

The effect of shared holdings on sales is due primarily to increased risk-taking by insurers within the constraints of asset-liability management. It makes it more challenging for regulators to mitigate this effect.

Price Impact

We examine the change in the value of a pair’s corporate bond holdings. This aims to determine if joint selling by exposed insurers due to these shocks has a price effect. 

We calculate the average change in each pair’s portfolio yield spread between the quarters before and after the shocks. We find that more remarkable portfolio similarity increases yield spreads of pairs’ joint corporate bond portfolios more for exposed pairs than unexposed ones. 

Exposed insurers tend to sell more corporate debt and liquid assets like equity, mutual funds, and US government bonds. Thus, overlaps in insurers’ holdings could lead to joint sales that may depress asset values under certain conditions.

We propose a portfolio-level similarity measure. It computes and compares the average portfolio of an insurance company with other insurers from our sample. 

This measure helps identify institutions that may contribute to financial instability due to their divestment behaviors. It accurately predicts how much an insurer will sell in common with others, even after adjusting for size. 

Our measure is a valuable portfolio overlap tool for regulators monitoring potential systemic risk contributions from specific institutions.

Regulators are mainly concerned with corporate and sovereign bond markets where insurers play a significant role. Our findings could help regulators identify and monitor joint insurer’s investments in these markets. This is where their divesting behavior can amplify systemic risk.

What does the paper contribute?

This paper contributes to the growing literature on institutional investors’ herding effects on asset allocation and liquidity. Moreover, prior studies have focused on corporate bonds. Meanwhile, we propose a measure of commonality in portfolio holdings that encompasses an insurer’s entire portfolio. 

This distinction is crucial as the sale of fixed-income securities (e.g., mortgage-backed securities) can spread risk among joint holders (Merrill et al., 2013). 

Insurers can strategically trade between asset classes to mitigate the impact of price changes (Ellul et al., 2015). Our measure provides a comprehensive view of the relationship between portfolio similarity and expected sales.

Final Thoughts!

We document a significant impact on the value of corporate bond portfolios due to insurers’ herding effects. It indicates a feedback loop between investors and asset prices, potentially destabilizing the market. 

Our portfolio similarity measurement can identify institutions. These institutions may affect asset liquidation channels and systemic risk transmission.

Besides, our measure of portfolio similarities can be calculated by any financial institution, regardless of whether they disclose their asset class or issuer holdings publicly or to regulators. 

This methodology can analyze the assets of hedge funds, money market funds, and banks, allowing regulators. Moreover, this can help to monitor potential common sale spillovers among various market participants. 

We find a positive correlation between portfolio similarity and SRISK, a measure available only to publicly traded companies. Therefore, our measure can be used alongside other risk metrics to monitor financial stability.