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Journal of Economic Interaction and Coordination

04.01.2024 | Regular Article

When firms buy corporate bonds: an agent-based approach to credit within firms

verfasst von: Jlenia Di Noia

Erschienen in: Journal of Economic Interaction and Coordination

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Abstract

Recent trends in the market for corporate bonds -such as its increase in size, the significant presence of low-quality debt instruments and the trading activity of non-financial corporations- call for the need of an investigation of the possible implications on the real economy. Although the phenomenon is still minor, the present paper aims at giving some early insights concerning the introduction of a corporate bond market, where also nonfinancial firms can invest. To do so, we build on an existing macroeconomic agent-based model of the CATS tradition and we introduce the possibility for firms to raise money through an alternative credit channel, i.e., the corporate bond market- and the opportunity for households and firms to invest part of their financial wealth into a portfolio of Government bonds and corporate bonds. Experiments based on single runs or on 100 Monte Carlo simulations suggest that the introduction of a bond market may exacerbate existing crisis and recessions and that when firms buy bonds, inequality may escalate. To the best of our knowledge, this paper constitutes one of the first attempts to incorporate a corporate bond market into a macro ABM.

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To cite some examples about the scale of the phenomenon, in 2017 Apple’s asset side in the balance sheet was composed by 66% of financial assets –they were 3% in 1995 and grew dramatically up to 41% in 2005– Microsoft held 54% of assets in the form of financial instruments and Facebook the 40%.

In a first summarizing review on macro ABMs, Dawid and Delli Gatti (2018), identify eight different families of models and carry a brief but comprehensive illustration of their main characteristics that help mapping the main ideas behind these models.

About this family of models a preliminary work can be traced back to Delli Gatti et al. (2005) whilst other extensions include: Gualdi et al. (2015), which explored the causes of macroeconomic fluctuations; Klimek et al. (2015), that focused on different crisis resolution mechanisms and Delli Gatti and Desiderio (2015), which perform different monetary policy experiments.

The issue of initial calibration has been extensively addressed by Caiani et al. (2016), paying particular attention to stock-flow consistency.

A seminal work on evolutionary theory can be found in Nelson and Winter (1982). For its application in macro ABMs, see for example (Caiani et al. 2019) or the K+S family of models ((Dosi et al. 2010) and later works).

On fiscal policies’ effectiveness to reduce inequality see also Palagi et al. (2017).

In Botta et al. (2022) commercial papers enter Investment Funds’ (IF) portfolios and bonds’ rents are transferred to rich households through their IF shares.

Source: FRED St.Louis statistics.

“Between 2009 and 2018, the combined value of corporate bond holdings by 25 large non-financial US companies tripled from USD 119 billion to USD 356 billion. The company with the largest portfolio alone held USD 124 billion in corporate debt securities. This equals the combined holdings of the world’s six largest corporate bond ETFs (exchange traded funds)", Çelik et al. (2020), page 6.

Monetary financial institutions, insurance and pension funds, investment funds and others. In 2018, US corporate bonds were held by insurance and pension funds for the 49%, by investment funds for the 28% and by non-financial corporations for the 13% (see Çelik et al. (2020), Table 14 at page 21). The increase in corporate bonds’ holding by (large) non-financial corporations –through foreign subsidiaries– occurred after the 2008 financial crisis.

See Çelik et al. (2020), Table 22 at page 33.

On that, see also Greenwood and Scharfstein (2013), who use the ratio between non-investment grade bonds’ issuance and total corporate bonds’ issuance to calculate the excess corporate bond returns.

C-firms buy bonds issued by k-firms or vice-versa, because in such a context it is not reasonable to assume that companies in the same sector buy each others’ bonds.

In recapitalizing new entrants, capitalists use their own deposits which go to zero. In forming new equity, c-firms are also initialized with the old c-firms’ stock of capital, whose value is calculated at average prices (it is as if the new c-firm buys capital at current average prices from bankrupted c-firm). The capital stock value is then subtracted from equity in order to calculate initial liquidity. Old firms’ debts (in the form of bank loans or issued bonds) are a loss to the bank and to investors.

See Assenza et al. (2015) for any detail and Dawid and Delli Gatti (2018) for a comprehensive overview of the relationship between the NK-DSGE vs the general ABM approach to consumption.

See Dynan et al. (2004).

Firms do not know their actual demand schedule and consequently they cannot calculate their marginal revenue function.

A measure of financial fragility that takes into account the “borrowers’ actual ability to generate net cash inflows to honor the debt", as pointed out in Caiani et al. (2016), should be preferred. However, the present model represents an extension of the CATS –although some main modifications are present– and since the focus is on the introduction of a bonds’ market in the credit market, we decided to keep Assenza et al. (2015) measure of firms’ financial fragility to compute their probability of bankruptcy (Eq. (4)).

The function \(\Xi (\phi ^L, 1/pr(\lambda _{f,t})) = 1-(1-\phi ^L)^{1/pr(\lambda _{f,t})+1}/\phi ^L\) stems from the geometric series expansion of the gross return rate \(R_{f,t} = (\phi ^L+r_{f,t})[1-(1-\phi ^L)^{1/pr(\lambda _{f,t})+1}/\phi ^L]\). See Assenza et al. (2015) for all detailed passages.

In this framework there is no room for new equity issuance given we are interested in the effects of bonds’ issuance and purchasing.

The rationale behind this assumption is that it seems unrealistic that firms competing in the same sector finance each other.

Government bonds’ purchase is introduced in order to remain anchored to reality and let investing firms have a more diversified portfolio.

Data on US corporations collected on FRED website suggest that nowadays firms split their financing gap almost equally between bank loans and new bonds’ issuance. Simulations’ experiments are in line with this observation.

Credit rating agencies (CRA) mainly consider 5 factors in determining the rating: (i) leverage and coverage, (ii) scale, (iii) profitability, (iv) business profile, (v) financial policy. To simplify things to the maximum extent, in this model a fictitious CRA sets the rating depending uniquely on financial fragility. Indeed, “leverage and coverage" hugely determines the final rating, as explained by Çelik et al. (2020). A credit cycle feedback is, however, introduced in the model: the final interest applied on bonds will take into account CRA tendency to relax rating accuracy in periods of economic growth, leading to higher quality ratings, while they worsen credit ratings in poor periods (see Bar-Isaac and Shapiro (2013); Auh (2015) and Lobo et al. (2017)).

\((1-\chi _{s,t+1})[(1-\alpha ^M_{d})\tau ^{B}_{s,t+1}M_{s,t}]\) is then the quota that will be invested in the safe bonds. In order to maintain things as simplified as possible, portfolio choice decisions are randomly given: that is to say that, in each period, the parameter \(\chi _{s,t}\) is randomly taken from a uniform distribution with support (0,0.5). Endogenous and more accurate decision rules on agents’ portfolio decisions will be introduced in future works.

It coincides with \(\hat{B}^{quota}_{s,t}\) when he visits the first firm. Due to the search and matching mechanism indeed, if the s-th investor has some liquidity left after having purchased bonds from the first firm he visited, he will continue visiting firms and buy available corporate bonds until either he finishes his available quota to invest, either there are no more corporate bonds to be placed for the visited firms.

In Assenza et al. (2015) the overall loans’ interests are defined as a weighted average of past interest rates applied by the bank on loans: \(\hat{r}^L_{f,t}=\hat{r}^L_{f,t-1}(1-\frac{\Delta L_{f,t}}{L_{f,t}})+r_{f,t}\frac{\Delta L_{f,t}}{L_{f,t}}\).

There are no costs associated with the financial activity.

Before dividends.

Individual financial fragility is instead represented by the probability of bankruptcy, which is computed using the leverage ratio as in Eq. (3)).

Other experiments using lower values of \(\tau ^B_{s,t}\) had been run, but resulted in an extremely high credit mismatch and were not aligned with data that suggest that US firms’ debt is constituted half by bank debt and half by corporate bonds debt.

The share of c-firms finding themselves in excess demand is indeed always greater than that in excess supply, even though the distance between them decreases in the interval t\(\in\)(195,254), with further convergence from t = 258 to t = 333.

This holds on average for all experiments, see Table 3 in the Appendix.

Notice that in Table 3 Exp.2b for the Gini coefficient is not statistically significant. It must be taken into account, however, that this result holds for the entire simulation. Figures 5 and 6, instead, suggest to look carefully at what happens in the second half of the simulations. As a non-reported exercise, we used a transient period that allows to concentrate on the second half of the simulations only and in that case Exp.2b and Exp.3 showed higher and significant coefficients with respect to the baseline (this does not hold true for Exp.2a).

In the baseline scenario households’ excess savings are stored into the bank’s deposits.

With due differences: for example in Exp.1a and 2a it takes longer because of the bank’s lower propensity to give loans and because of the lower overall liquidity investors can use to buy corporate bonds (prices start to drop, demand begins to increase but firms still have to face rationing).

In Botta et al. (2022) scenario 2 it is actually the Investment Funds that buy commercial papers in order to form their financial portfolios. Richer households indirectly invest their money in non-financial firms’ commercial papers through the purchase of Investment Funds’ shares, which return them rents.

For completeness, similarly to Botta et al. (2022), despite an initial circumvention of credit rationing due to a more market-oriented system –more favorable credit conditions thanks to the lower coupon rates with respect to the loan interest rates– we do not observe long-run positive effects on GDP (on the contrary, we observe significant reductions, as shown in Table 3 and Fig. 1).

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Titel: When firms buy corporate bonds: an agent-based approach to credit within firms
verfasst von: Jlenia Di Noia
Publikationsdatum: 04.01.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Economic Interaction and Coordination
Print ISSN: 1860-711X
Elektronische ISSN: 1860-7128
DOI: https://doi.org/10.1007/s11403-023-00399-4