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In an experimental setting in which investors can entrust their money to traders, we investigate how compensation schemes affect liquidity provision and asset prices. Investors face a trade-off between risk and return. At the benefit of a potentially higher return, they can entrust their money to a trader. However this investment is risky, as the trader might not be trustworthy. Alternatively, they can opt for a safe but low return. We study how subjects solve this trade-off when traders are either liable for losses or not, and when their bonuses are either capped or not. Limited liability introduces a conflict of interest because it makes traders value the asset more than investors. To limit losses, investors should thus restrict liquidity provision to force traders to trade at a lower price. By contrast, bonus caps make traders value the asset less than investors. This should encourage liquidity provision and decrease prices. In contrast to these predictions, we find that under limited liability investors contribute to asset price bubbles by increasing liquidity provision and that caps fail to tame bubbles. Overall, giving investors skin in the game fosters financial stability.
From 1963 through 2015, idiosyncratic risk (IR) is high when market risk (MR) is high. We show that the positive relation between IR and MR is highly stable through time and is robust across exchanges, firm size, liquidity, and market-to-book groupings. Though stock liquidity affects the strength of the relation, the relation is strong for the most liquid stocks. The relation has roots in fundamentals as higher market risk predicts greater idiosyncratic earnings volatility and as firm characteristics related to the ability of firms to adjust to higher uncertainty help explain the strength of the relation. Consistent with the view that growth options provide a hedge against macroeconomic uncertainty, we find evidence that the relation is weaker for firms with more growth options.
Do firms buy their stock at bargain prices? : Evidence from actual stock repurchase disclosure
(2011)
We use new data from SEC filings to investigate how S&P 500 firms execute their open market repurchase programs. We find that smaller S&P 500 firms repurchase less frequently than larger firms, and at a price which is significantly lower than the average market price. Their repurchase activity is followed by a positive and significant abnormal return which lasts up to three months after the repurchase. These findings do not hold for large S&P 500 firms. Our interpretation is that small firms repurchase strategically, whereas the repurchase activity of large firms is more focused on the disbursement of free cash. JEL Classification: G14, G30, G35 Keywords: Stock Repurchases, Stock Buybacks, Payout Policy, Timing, Bid-Ask Spread, Liquidity
The interbank market is important for the efficient functioning of the financial system, transmission of monetary policy and therefore ultimately the real economy. In particular, it facilitates banks' liquidity management. This paper aims at extending the literature which views interbank markets as mutual liquidity insurance mechanism by taking into account persistence of liquidity shocks. Following a theory of long-term interbank funding a financial system which is modeled as a micro-founded agent based complex network interacting with a real economic sector is developed. The model features interbank funding as an over-the-counter phenomenon and realistically replicates financial system phenomena of network formation, monetary policy transmission and endogenous money creation. The framework is used to carry out an optimal policy analysis in which the policymaker maximizes real activity via choosing the optimal interest rate in a trade-off between loan supply and financial fragility. It is shown that the interbank market renders the financial system more efficient relative to a setting without mutual insurance against persistent liquidity shocks and therefore plays a crucial role for welfare.
We use a unique data set from the Trade Reporting and Compliance Engine (TRACE) to study liquidity effects in the US structured product market. Our main contribution is the analysis of the relation between the accuracy in measuring liquidity and the potential degree of disclosure. Having access to all relevant trading information, we provide evidence that transaction cost measures that use dealer specific information such as trader identity and trade direction can be efficiently proxied by measures that use less detailed information. This finding is important for all market participants in the context of OTC markets, as it fosters our understanding of the information contained in transaction data. Thus, our results provide guidance for improving transparency while maintaining trader confidentiality. In addition, we analyze liquidity in the structured product market in general and show that securities that are mainly institutionally traded, guaranteed by a federal authority, or have low credit risk, tend to be more liquid.
Exploiting NASDAQ order book data and difference-in-differences methodology, we identify the distinct effects of trading pause mechanisms introduced on U.S. stock exchanges after May 2010. We show that the mere existence of such a regulation constitutes a safeguard which makes market participants behave differently in anticipation of a pause. Pauses tend to break local price trends, make liquidity suppliers revise positions, and enhance price discovery. In contrast, pauses do not have a “cool off” effect on markets, but rather accelerate volatility and bid-ask spreads. This implies a regulatory trade-off between the protective role of trading pauses and their adverse effects on market quality.
Asset transaction prices sampled at high frequency are much staler than one might expect in the sense that they frequently lack new updates showing zero returns. In this paper, we propose a theoretical framework for formalizing this phenomenon. It hinges on the existence of a latent continuous-time stochastic process pt valued in the open interval (0; 1), which represents at any point in time the probability of the occurrence of a zero return. Using a standard infill asymptotic design, we develop an inferential theory for nonparametrically testing, the null hypothesis that pt is constant over one day. Under the alternative, which encompasses a semimartingale model for pt, we develop non-parametric inferential theory for the probability of staleness that includes the estimation of various integrated functionals of pt and its quadratic variation. Using a large dataset of stocks, we provide empirical evidence that the null of the constant probability of staleness is fairly rejected. We then show that the variability of pt is mainly driven by transaction volume and is almost unaffected by bid-ask spread and realized volatility.
We develop a state-space model to decompose bid and ask quotes of CDS into two components, fair default premium and liquidity premium. This approach gives a better estimate of the default premium than mid quotes, and it allows to disentangle and compare the liquidity premium earned by the protection buyer and the protection seller. In contrast to other studies, our model is structurally much simpler, while it also allows for correlation between liquidity and default premia, as supported by empirical evidence. The model is implemented and applied to a large data set of 118 CDS for a period ranging from 2004 to 2010. The model-generated output variables are analyzed in a difference-in-difference framework to determine how the default premium, as well as the liquidity premium of protection buyers and sellers, evolved during different periods of the financial crisis and to which extent they differ for financial institutions compared to non-financials.
Retail investors pay over twice as much attention to local companies than non-local ones, based on Google searches. News volume and volatility amplify this attention gap. Attention appears causally related to perceived proximity: first, acquisition by a nonlocal company is associated with less attention by locals, and more by nonlocals close to the acquirer; second, COVID-19 travel restrictions correlate with a drop in relative attention to nonlocal companies, especially in locations with fewer fights after the outbreak. Finally, local attention predicts volatility, bid-ask spreads and nonlocal attention, not viceversa. These findings are consistent with local investors having an information-processing advantage.