C25 Discrete Regression and Qualitative Choice Models; Discrete Regressors (Updated!)
Early life conditions and financial risk–taking in older age
Loretti I. Dobrescu
- Using life-history survey data from eleven European countries, we investigate whether childhood conditions, such as socioeconomic status, cognitive abilities and health problems influence portfolio choice and risk attitudes later in life. After controlling for the corresponding conditions in adulthood, we find that superior cognitive skills in childhood (especially mathematical abilities) are positively associated with stock and mutual fund ownership. Childhood socioeconomic status, as indicated by the number of rooms and by having at least some books in the house during childhood, is also positively associated with the ownership of stocks, mutual funds and individual retirement accounts, as well as with the willingness to take financial risks. On the other hand, less risky assets like bonds are not affected by early childhood conditions. We find only weak effects of childhood health problems on portfolio choice in adulthood. Finally, favorable childhood conditions affect the transition in and out of risky asset ownership, both by making divesting less likely and by facilitating investing (i.e., transitioning from non-ownership to ownership).
Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
- We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions.
On the dark side of the market: identifying and analyzing hidden order placements
- Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders’ use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ, we propose a simple method to conduct statistical inference on the location of hidden depth and to test economic hypotheses. Analyzing a wide cross-section of stocks, we show that market conditions reflected by the (visible) bid-ask spread, (visible) depth, recent price movements and trading signals significantly affect the aggressiveness of ’dark’ liquidity supply and thus the ’hidden spread’. Our evidence suggests that traders balance hidden order placements to (i) compete for the provision of (hidden) liquidity and (ii) protect themselves against adverse selection, front-running as well as ’hidden order detection strategies’ used by high-frequency traders. Accordingly, our results show that hidden liquidity locations are predictable given the observable state of the market.