Asymmetric information, credit market condition, and entrepreneurial finance

The issue of imperfect information plays a much more important role in financing “informationally opaque” small businesses than in financing large companies.1 This chapter examines the asymmetric information issue in entrepreneurial finance from two perspectives: the effects of relationship lending and the impacts of credit market concentration on entrepreneurial financial behavior. These two perspectives are strongly linked to each other via the asymmetric information issue in entrepreneurial finance. Existing literature has recognized the important role played by relationship lending in alleviating the problem of asymmetric information. However, mixed empirical results have been reported. For example, it has been found that the development of relationship lending can improve the availability of finance for small businesses borrowers (Petersen and Rajan, 1994) and reduce the costs of finance (Berger and Udell, 1995). Meanwhile, with monopoly power, banks may extract rents, in terms of charging higher-than-market interest rates, from small businesscustomers who have very concentrated banking relationships (Ongena and Smith, 2001). In addition, both favorable and unfavorable effects of credit market concentration on financing small businesses have been acknowledged. Small business borrowers may have to pay a higher-than-market price on loans (Degryse and Ongena, 2005) and are more likely to be financially constrained (Cetorelli, 2004) than in competitive markets. On the other hand, empirical studies have shown that market concentration create a strong motive for lenders to invest in private information from small business customers, and therefore a concentrated market is more efficient in terms of private information acquisition (Han et al., 2009b). The objective of this chapter is to investigate, by reviewing existing literature, the role played by relationship lending and the effects of market concentration on financing entrepreneurial businesses that are supposed to be informationally opaque. In the first section we review literature on the important role played by asymmetric information in entrepreneurial finance from two perspectives: asymmetric information and relationship lending, and the theoretical modeling of asymmetric information. Then we examine the relationship between capital market conditions and entrepreneurial finance and attempt to answer two questions: Why is the capital market condition important for entrepreneurial finance? and What are the effects of capital market conditions on entrepreneurial financial behavior in terms of discouraged borrowers, cash holding, and the availability and costs of finance?

Credit and liquidity components of corporate CDS spreads

This paper investigates the role of credit and liquidity factors in explaining corporate CDS price changes during normal and crisis periods. We find that liquidity risk is more important than firm-specific credit risk regardless of market conditions. Moreover, in the period prior to the recent “Great Recession” credit risk plays no role in explaining CDS price changes. The dominance of liquidity effects casts serious doubts on the relevance of CDS price changes as an indicator of default risk dynamics. Our results show how multiple liquidity factors including firm specific and aggregate liquidity proxies as well as an asymmetric information measure are critical determinants of CDS price variations. In particular, the impact of informed traders on the CDS price increases when markets are characterised by higher uncertainty, which supports concerns of insider trading during the crisis.

The determinants of a cross market arbitrage opportunity: theory and evidence for the European bond market

This paper examines the determinants of cross-platform arbitrage profits. We develop a structural model that enables us to decompose the likelihood of an arbitrage opportunity into three distinct factors: the fixed cost to trade the opportunity, the level at which one of the platforms delays a price update and the impact of the order flow on the quoted prices (inventory and asymmetric information effects). We then investigate the predictions from the theoretical model for the European Bond market with the estimation of a probit model. Our main finding is that the results found in the empirical part corroborate strongly the predictions from the structural model. The event of a cross market arbitrage opportunity has a certain degree of predictability where an optimal ex ante scenario is represented by a low level of spreads on both platforms, a time of the day close to the end of trading hours and a high volume of trade.

Selecting from amongst non–nested conditional variance models: information criteria and portfolio determination

We consider the finite sample properties of model selection by information criteria in conditionally heteroscedastic models. Recent theoretical results show that certain popular criteria are consistent in that they will select the true model asymptotically with probability 1. To examine the empirical relevance of this property, Monte Carlo simulations are conducted for a set of non–nested data generating processes (DGPs) with the set of candidate models consisting of all types of model used as DGPs. In addition, not only is the best model considered but also those with similar values of the information criterion, called close competitors, thus forming a portfolio of eligible models. To supplement the simulations, the criteria are applied to a set of economic and financial series. In the simulations, the criteria are largely ineffective at identifying the correct model, either as best or a close competitor, the parsimonious GARCH(1, 1) model being preferred for most DGPs. In contrast, asymmetric models are generally selected to represent actual data. This leads to the conjecture that the properties of parameterizations of processes commonly used to model heteroscedastic data are more similar than may be imagined and that more attention needs to be paid to the behaviour of the standardized disturbances of such models, both in simulation exercises and in empirical modelling.

Asymmetric response of demand-supply mismatch to investor's sentiment

We look through both the demand and supply side information to understand dynamics of price determination in the real estate market and examine how accurately investors’ attitudes predict the market returns and thereby flagging off extent of any demand-supply mismatch. Our hypothesis is based on the possibility that investors’ call for action in terms of their buy/sell decision and adjustment in reservation/offer prices may indicate impending demand-supply imbalances in the market. In the process, we study several real estate sectors to inform our analysis. The timeframe of our analysis (1995-2010) allows us to observe market dynamics over several economic cycles and in various stages of those cycles. Additionally, we also seek to understand how investors’ attitude or the sentiment affects the market activity over the cycles through asymmetric responses. We test our hypothesis variously using a number of measures of market activity and attitude indicators within several model specifications. The empirical models are estimated using Vector Error Correction framework. Our analysis suggests that investors’ attitude exert strong and statistically significant feedback effects in price determination. Moreover, these effects do reveal heterogeneous responses across the real estate sectors. Interestingly, our results indicate the asymmetric responses during boom, normal and recessionary periods. These results are consistent with the theoretical underpinnings.