1. Aretz, Kevin, Shuwen Yang, and Yafei Zhang, Real Disinvestments & the Distress Anomaly: Evidence from Stocks, Bonds, and Loans.
We show that firms’ ability to disinvest real assets helps to explain the negative distress premiums in stocks, bonds, and, as we report, loans and firm assets. Using a real options model in which equityholders and debtholders share disinvestment proceeds, the model suggests that the equity (debt) distress premium becomes more negative with the proceeds paid to that class, with the premiums being jointly negative if debtholders receive most of the proceeds. Using hard-asset disinvestment-ability proxies, the stock (bond and loan) distress premiums become more positive (negative) with those proxies, possibly suggesting that shareholders benefit more strongly from soft-asset disinvestments.
Presented at the Annual Meeting of the European Finance Association and the Paris December Finance ("Eurofidai") Meeting.
First-stage Revise-and-Resubmit at the Journal of Financial & Quantitative Analysis (April 2024).
Most recent version (February 2024): HERE.
2. Aretz, Kevin, Hening Liu, and Kevin Schneider, Seasonal Inventory Leverage.
We develop a neoclassical theory of a firm with seasonal output prices and inventory building. Our theory suggests that seasonal firms optimally build up output inventories toward their high-price seasons. Doing so, they create endogenous similar (inverse) seasonality in their sales (seasonal inventory leverage or expected returns). Importantly, higher inventory holding costs reduce optimal inventory building, dampening the endogenous seasonalities. Supporting our theory, our empirical work reveals that seasonal firms build up inventories toward their high-sales seasons. Moreover, high seasonal inventory leverage predicts high stock returns and helps explain the seasonal-sales, same-calendar-month, momentum, and ROE anomalies.
Presented at the 2023 Annual Meeting of the Financial Intermediation Research Society (FIRS) in Vancouver.
Submitted to the Journal of Financial Economics (September 2024).
Most recent version (September 2024): HERE.
3. Aretz, Kevin, Hassan Ilyas, and Gaurav Kankanhalli, Technological Progress, Managerial Learning, and the Investment-Stock Price Sensitivity.
Motivated by a real options investment model in which managers learn about the unobservable production costs of brandnew capacity both through their firm's share price and installed capacity, we reveal that the investment-to-stock price sensitivity rises with the time since managers last invested into new capacity, as proxied through capacity overhang. Our evidence is robust to using various investment, employment, and Tobin's Q measures and not entirely subsumed by financial constraints. Interestingly, managers learn less from their share prices when they have better information, investors have worse information, and there are alternative information sources (as, e.g., trade associations). Connecting our empirical evidence to technological progress, we finally show that the managers of firms with outdated capital extract more investment-relevant information from share prices when they are more exposed to technological progress, as measured through patent citations and the exogenous R&D stocks of firms located in the firm's technology space.
Presented at the 2023 Annual Meeting of the American Finance Association in New Orleans.
To be submitted to the Journal of Financial Economics soon.
Most recent version (May 2024): HERE.
4. Aretz, Kevin, Murillo Campello, Gaurav Kankanhalli, and Kevin Schneider, Uncertainty and Corporate Zombification: Implications for Competition Dynamics and Creative Destruction.
We show how the threat of “uncertainty-induced zombification” — creditors’ willingness to keep distressed firms alive when faced with uncertainty — shapes various industry dynamics. Under a real options framework, we demonstrate that healthy firms become reluctant to invest and disinvest in anticipation that uncertainty induces creditors to convert rival defaulting firms into zombies. We validate our theory model using dynamic, product-market-specific estimates of uncertainty-induced zombification together with loan contract-level data. Empirically, higher uncertainty-led rival zombification prompts unlevered firms to reduce their investment, disinvestment, employment, and establishment-level openings and closures. Notably, these policies are modulated by the anticipation of the extent to which distressed rivals will be subsidized by their creditors. These hard-to-reverse decisions depress healthy firms’ long-run sales revenues, profits, and market values. We confirm those dynamics using granular, near-universal data on the asset allocation decisions of global shipping firms. Our findings highlight a novel channel through which uncertainty influences firms’ capital accumulation, performance, and outcomes.
Accepted for presentation at the SFS Cavalcade Conference North America (May 2024) and the Annual Meeting of the European Finance Association in Slovakia (August 2024).
First version to be circulated soon.
5. Aretz, Kevin, Jiayu Jin, and Yifan Li, Estimating and Forecasting Skewness Using Affine Stochastic Volatility Models.
We derive an estimator of the physical skewness of an asset’s discrete return over any time horizon based on the assumption that the asset’s price follows a stochastic process from the affine stochastic volatility (ASV) model class. Conceptually, our estimator improves upon others by (i) focusing on discrete returns; (ii) allowing us to capture compounding and leverage effects; yielding (iii) horizon-consistent (iv) conditional (“forward-looking”) and unconditional (“historical”) estimates; and (v) not requiring ad-hoc conditioning variables. We use a simulation exercise to show our estimator is highly precise even when the true data-generating process partially deviates from that assumed by the estimator. The exercise further suggests that our estimator comfortably beats others advocated in recent studies. Using options data, we finally show that our estimator best captures time-series variations in the risk-neutral conditional skewness of the S&P 500 index.
First Draft to be Circulated by Mid-2024.