Narrow Results

We propose an agent-based computational model for a financial system consisting of a network of banks with interconnected balance sheets comprising fixed assets (e.g. loans to agents outside the network), liquid assets (e.g. cash or central bank reserves), general collateral (e.g. government debt), unsecured interbank loans and reverse-repos to other banks as assets, as well as deposits, unsecured interbank loans and repos from other banks as liabilities. Importantly, we allow banks to use reverse-repo assets as collateral for obtaining repo loans from other banks, that is to say, rehypothecation. Banks need to satisfy liquidity, collateral, and solvency constraints. If the first two constraints are violated because of internal or external shocks, solvent banks attempt to restore them by rebalancing their assets, which might lead to the propagation of the shock because of fire-sale effects (if fixed assets are sold) or liquidity hoarding (if secured or unsecured loans are recalled). Insolvent banks, as well as banks that failed to restore the liquidity and collateral constraints after rebalancing, are removed from the network using a resolution algorithm that includes a netting step (i.e. removal of closed cycles of liabilities) and a novation step (i.e. redistribution of repo assets and liabilities to remaining banks). We show analytically that this proposed resolution algorithm has several desirable properties, most importantly the order-independence of the novation step, and we investigate the stability properties of the network through a series of numerical experiments.

We establish a link between illiquidity and positive autocorrelation in asset returns among a sample of hedge funds, mutual funds, and various equity portfolios. For hedge funds, this link can be confirmed by comparing the return autocorrelations of funds with shorter vs. longer redemption-notice periods. We also document significant positive return-autocorrelation in portfolios of securities that are generally considered less liquid, e.g., small-cap stocks, corporate bonds, mortgage-backed securities, and emerging-market investments. Using a sample of 2,927 hedge funds, 15,654 mutual funds, and 100 size- and book-to-market-sorted portfolios of US common stocks, we construct autocorrelation-sorted long/short portfolios and conclude that illiquidity premia are generally positive and significant, ranging from 2.74% to 9.91% per year among the various hedge funds and fixed-income mutual funds. We do not find evidence for this premium among equity and asset-allocation mutual funds, or among the 100 US equity portfolios. The time variation in our aggregated illiquidity premium shows that while 1998 was a difficult year for most funds with large illiquidity exposure, the following four years yielded significantly higher illiquidity premia that led to greater competition in credit markets, contributing to much lower illiquidity premia in the years leading up to the Financial Crisis of 2007–2008.

This paper studies dynamic asset allocations across stocks, Treasury bonds, and corporate bond indices. We employ a new model where liquidity plays an important role in forecasting excess returns. We document the significant utility benefits an investor gains by optimally including corporate bond indices in his portfolio. The benefits are bigger for lower-grade bonds. We also find that investment-grade indices are different from high-yield indices in that different risks are priced in these two asset classes. One important difference is that there exist positive "flight-to-liquidity" premia in investment-grade bonds, but we find no such premia in high-yield bonds. We calculate the portfolio behavior and the utility benefits for three types of investors, the "sophisticated", the "average" and the "lazy" investor. We provide practical portfolio advice on investing throughout the business cycle and we study how the total allocations and hedging demands vary with the business conditions. In addition, utilizing our model, we evaluate the significance of the liquidity variable information for the investor. We find that the liquidity information greatly enhances the investor's portfolio performance. Finally, further support in the optimality of the strategies is provided by calculating their in- and out-of-sample realized returns for the last decade.

This paper explores the role of liquidity risk in the pricing of corporate bonds. We show that corporate bond returns have significant exposures to fluctuations in treasury bond liquidity and equity market liquidity. Further, this liquidity risk is a priced factor for the expected returns on corporate bonds, and the associated liquidity risk premia help to explain the credit spread puzzle. In terms of expected returns, the total estimated liquidity risk premium is around 0.6% per annum for US long-maturity investment grade bonds. For speculative grade bonds, which have higher exposures to the liquidity factors, the liquidity risk premium is around 1.5% per annum. We find very similar evidence for the liquidity risk exposure of corporate bonds for a sample of European corporate bond prices.

Employing an instrumental variable approach based on the regulatory change of tick sizes, I examine the link between the liquidity of a firm's equity and activism by large shareholders. I find that liquidity increases the likelihood of block formation. Blockholders of more liquid securities take smaller stakes that do not precommit them to monitor. I find evidence that the threat of exit from a block can discipline managers and that this threat is more effective when liquidity is higher. While liquidity increases exit from existing blocks, I find no evidence that share illiquidity that forces blockholders to actively monitor.

I propose a friction measure of bond round-trip liquidity costs that is robust to outliers and accounts for the idiosyncratic information behind trading decisions. Particularly effective with investment-grade bonds, the proposed measure displays properties consistent with the credit risk puzzle. Using transactions from January 2004 to December 2011, I find that liquidity costs display a strong correlation with credit conditions and peaked during the sub-prime crisis. After controlling for equity volatility with high-frequency measures, liquidity costs explain a substantial fraction of the variation in the yield spreads of highly rated bonds, but become less important for speculative-grade bonds.

We estimate the non-default component of corporate bond yield spreads and examine its relationship with bond liquidity. We measure bond liquidity using intraday transactions data and estimate the default component using the term structure of credit default swaps (CDS) spreads. With swap rate as the risk free rate, the estimated non-default component is generally moderate but statistically significant for AA-, A-, and BBB-rated bonds and increasing in this order. With Treasury rate as the risk free rate, the estimated non-default component is the largest in basis points for BBB-rated bonds but, as a fraction of yield spreads, it is the largest for AAA-rated bonds. Controlling for the unobservable firm heterogeneity, we find a positive and significant relationship between the non-default component and illiquidity for investment-grade bonds but no significant relationship for speculative-grade bonds. We also find that the non-default component comoves with indicators for macroeconomic conditions.

This paper studies the determinants of trading volume and liquidity of corporate bonds. Using transactions data from a comprehensive dataset of insurance company trades, our analysis covers more than 17,000 US corporate bonds of 4,151 companies over a five-year period prior to the introduction of TRACE. Our transactions data show that a variety of issue- and issuer-specific characteristics impact corporate bond liquidity. Among these, the most economically important determinants of bond trading volume are the bond’s issue size and age — trading volume declines substantially as bonds become seasoned and are absorbed into less active portfolios. Stock-level activity also impacts bond trading volume. Bonds of companies with publicly traded equity are more likely to trade than those with private equity. Further, public companies with more active stocks have more actively traded bonds. Finally, we show that while the liquidity of high-yield bonds is more affected by credit risk, interest-rate risk is more important in determining the liquidity of investment-grade bonds.

I examine whether a short-term reversal is attributed to past intraday or overnight price movements. The results show that intraday returns significantly reverse in the following week, while overnight returns do not, indicating that the short-term reversal is attributed to past intraday price movements. In addition, the reversal of intraday returns is stronger for more illiquid stocks and during more volatile market conditions, while the reversal is unaffected by fundamental news. This result supports the view that short-term reversals are attributable mainly to price concessions for liquidity providers to absorb intraday uninformed transactions, rather than intraday price reactions to fundamental information.

We show that pass-through funding of mortgages with covered bonds supported by strong creditor rights is one way of providing highly liquid mortgage bonds. Despite a 30% drop in house prices during the 2008 crisis, these mortgage bonds remained as liquid as comparable government bonds with high trading volume and low bid-ask spreads. Market liquidity of these covered bonds is primarily driven by the availability of funding liquidity. Funding liquidity is the main concern because the pass-through funding approach effectively eliminates other types of risks from the investor’s perspective. Banking regulators should take into account the implications of these findings, particularly when it comes to the interplay between liquidity and capital requirements.

Hendershott et al. (2011, Does Algorithmic Trading Improve Liquidity? Journal of Finance 66, 1–33) show that algorithmic traders improve liquidity in equity markets. An equally important and unanswered question is whether they improve liquidity when information asymmetry is high. We use days surrounding earnings announcement as a period of high information asymmetry. First, we follow Hendershott et al. (2011, Does Algorithmic Trading Improve Liquidity? Journal of Finance 66, 1–33) to use introduction of NYSE autoquote as a natural experiment. We find that increased algorithmic trading (AT) as a result of NYSE autoquote does not improve liquidity around earnings announcements. Next, we use trade-to-order volume % and cancel rate as a proxy for algorithmic trading and find that abnormal spreads surrounding the days of earnings announcement are significantly higher for stocks with higher AT. Our findings indicate that algorithmic traders reduces their role of liquidity provision in markets when information asymmetry is high. These findings shed further light on the role of liquidity provision by algorithmic traders in the financial markets.

Using the 2016 SEC Tick Size Pilot Program, we study the effects of an increase in tick size on institutional trading, market making costs, profitability, and activities. We find that increasing the tick size deters institutional trading participation, as it results in unfavorable stock characteristics, such as greater price impact and depressed share prices. In particular, we show that the implementation of the pilot program creates a substitution effect, which causes mutual funds to migrate from pilot (wider-tick) stocks to control (narrower-tick) peers. Furthermore, we document that widening the tick size increases adverse selection and inventory costs and thus reduces market making profitability, leading to lower market-making activities. Further analysis shows that these adverse effects can be attributed to the trade-at rule that prevents price-matching in non-displaying trading centers, while the quote rule that mandates a minimum quote increment of five cents enriches market makers and promotes liquidity provision. Finally, we show that our results are more pronounced for tick-constrained stocks than for unconstrained ones. Overall, the evidence contradicts the SEC’s intent to use a larger tick size to incentivize market making in small-cap stocks and attract more investors to trade these stocks, and dispraises the “one-size-fits-all” approach undertaken by regulators.

Issuing activity does not result in superior post-issue liquidity. New issues are just as liquid as their peer non-issuers. Even the kinds of new issues that are supposed to be more liquid than others (initial public offerings (IPOs) backed by venture capital, new issues with high-prestige underwriters, severely underpriced IPOs) have the same liquidity as other similar issuers. The paper thus refutes the existing liquidity-based explanations of the new issues puzzle. The paper also shows that the low-minus-high turnover factor seems to explain the new issues puzzle and related anomalies only because it picks up volatility risk.

This study examines whether, and to what extent, SFAS166/167 changed the role of securitization in bank liquidity and lending activities. We compare the sensitivity of on-balance sheet loan growth to the loan portfolio liquidity index proposed by Loutskina [(2011) The role of securitization in bank liquidity and funding management, Journal of Financial Economics 100, 663–684] between affected banks and control banks. We find that SFAS166/167 is significantly associated with a reduction in the use of securitization to enhance on-balance sheet liquidity, consistent with the view that consolidation on balance sheets may render the securitization of loans too costly to be considered an effective source of liquidity. In addition, we find that affected banks with the highest increase in liabilities from consolidating Qualified Special Purpose Entities (QSPEs) experienced a significant decrease in lending activities relative to the control banks. This is likely because consolidating former QSPEs may adversely affect the ability and willingness of banks to engage in securitization and issue new loans. Taken together, our results suggest that SFAS166/167, requiring the consolidation of former QSPEs, led to a decline in the role of securitization as a liquidity management tool in banks and to a significant decline in lending.

Maintaining an optimal level of liquidity remains a crucial agenda for firms to minimise liquidity risks. Therefore, the study aims to identify the drivers of liquidity by utilizing firm-level data of 1,544 microfinance institutions (MFIs), covering a total of 112 developing countries and a period of 2010–2018. The data were then analysed using conventional econometric tools and techniques. Among others, the study found that board size, portfolio quality, donations and size of MFIs have a positive effect on the liquidity of MFIs, while gender diversity at the board level, operational self-sustainability, staff productivity, legal status and gross domestic product (GDP) growth revealed a negative effect. After conducting several robustness checks, including alternative proxies, sub-samples and endogeneity-corrected techniques, our findings remain mostly consistent. Policy implications are further discussed.