Koen Inghelbrecht

How Do Investment Banks Value Initial Public Offerings (IPOs)

We investigate the valuation and the pricing of initial public offerings (IPOs) by investment banks for a unique dataset of 49 IPOs on Euronext Brussels in the 1993-2001 period. We find that for each IPO several valuation methods are used, of which Discounted Free Cash Flow (DFCF) is the most popular. The offer price is mainly based on DFCF valuation, to which a discount is applied. Our results suggest that DDM tends to underestimate value, while DFCF produces unbiased value estimates. When using multiples, investment banks rely mostly on future earnings and cash flows. Multiples based on post-IPO forecasted earnings and cash flows result in more accurate valuations.

A Unified Market Liquidity Measure

We introduce a novel method (based on Illing et al. (2006) and popularized by Hollo et al. (2012) through the CISS measure) to aggregate different groups of liquidity measures (percent-cost proxies, cost-per-volume proxies, etc.), in order to accommodate for the ‘different dimensions of liquidity’ (Amihud et al., 2005) through a single ‘unified’ market-wide aggregate liquidity metric. The weights for the multiple dimensions are time-varying and depend on three components: the correlation between groups, the pressure conveyed through the measure (threshold), and their conditional variance. We evaluate the performance of our market liquidity measure in various ways. Most importantly, our liquidity measure succeeds in tracking the most important historic episodes of financial stress and has a close relation with many crisis indicators. Moreover, our unified liquidity measure shows the expected macroeconomic and financial relationships mentioned in the literature, and even has some predictive power for future growth rates of traditional variables. Finally, our methodology allows to gauge the individual importance of each liquidity group over time. Our results unveil the spread and effective tick liquidity groups as the main protagonist during turbulent financial periods.

Good Luck, Bad Luck. Can Mutual Funds Really Pick Stocks?

The literature examining mutual fund performance is vast and researchers have studied the performance of professional money managers extensively, but there is still an ongoing debate whether or not mutual fund managers have stock picking skills. This article investigates the profitability of institutional trades using a unique dataset comprising the transactions executed by a sample of mutual funds during the years 2002-2007. We implement an innovative bootstrap technique to examine the non-normal distribution of our characteristics-based performance measure across the trades of mutual funds to precisely separate luck from skill. Moreover, we apply a new nonlinear technique based on cluster analysis to construct matching benchmark portfolios. Results indicate that, controlling for luck, a negligible minority of managers pick stocks well enough to beat the market. The underperformance of managers can mainly be attributed to ‘bad luck’, and not to the absence of genuine stock picking skills. Finally, funds trading in illiquid stocks with higher book-to-market ratio and smaller size perform better.