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This paper provides a joint analysis of household stockholding participation, stock location among stockholding modes, and participation spillovers, using data from the US Survey of Consumer Finances. Our multivariate choice model matches observed participation rates, conditional and unconditional, and asset location patterns. Financial education and sophistication strongly affect direct stockholding and mutual fund participation, while social interactions affect stockholding through retirement accounts only. Household characteristics influence stockholding through retirement accounts conditional on owning retirement accounts, unlike what happens with stockholding through mutual funds. Although stockholding is more common among retirement account owners, this fact is mainly due to their characteristics that led them to buy retirement accounts in the first place rather than to any informational advantages gained through retirement account ownership itself. Finally, our results suggest that, taking stockholding as given, stock location is not arbitrary but crucially depends on investor characteristics. JEL Classification: G11, E21, D14, C35
Renewed interest in fiscal policy has increased the use of quantitative models to evaluate policy. Because of modeling uncertainty, it is essential that policy evaluations be robust to alternative assumptions. We find that models currently being used in practice to evaluate fiscal policy stimulus proposals are not robust. Government spending multipliers in an alternative empirically-estimated and widely-cited new Keynesian model are much smaller than in these old Keynesian models; the estimated stimulus is extremely small with GDP and employment effects only one-sixth as large.
The global financial crisis has lead to a renewed interest in discretionary fiscal stimulus. Advocates of discretionary measures emphasize that government spending can stimulate additional private spending — the so-called Keynesian multiplier effect. Thus, we investigate whether the discretionary spending announced by Euro area governments for 2009 and 2010 is likely to boost euro area GDP by more than one for one. Because of modeling uncertainty, it is essential that such policy evaluations be robust to alternative modeling assumptions and different parameterizations. Therefore, we use five different empirical macroeconomic models with Keynesian features such as price and wage rigidities to evaluate the impact of fiscal stimulus. Four of them suggest that the planned increase in government spending will reduce private spending for consumption and investment purposes significantly. If announced government expenditures are implemented with delay the initial effect on euro area GDP, when stimulus is most needed, may even be negative. Traditional Keynesian multiplier effects only arise in a model that ignores the forward-looking behavioral response of consumers and firms. Using a multi-country model, we find that spillovers between euro area countries are negligible or even negative, because direct demand effects are offset by the indirect effect of euro appreciation.
We investigate the effects of both trust and sociability for stock market participation, the role of which has been examined separately by existing finance literature. We use internationally comparable household data from the Survey of Health, Ageing and Retirement in Europe supplemented with regional information on generalized trust from the World Value Survey and on specific trust to financial institutions from Eurobarometer. We show that trust and sociability have distinct and sizeable positive effects on stock market participation and that sociability is likely to partly balance the discouragement effect on stockholding induced by low generalized trust in the region of residence. We also show that specific trust in advice given by financial institutions represents a prominent factor for stock investing, compared to other tangible features of the banking environment. Probing further into various groups of households, we find that sociability can induce stockholding among the less well off in Sweden, Denmark, and Switzerland where stock market participation is widespread. On the other hand, the effect of generalized trust is strong in countries with limited participation and low average trust like Austria, Spain, and Italy, offering an explanation for the remarkably low participation rates of the wealthy living therein.
Algorithmic trading engines versus human traders – do they behave different in securities markets?
(2009)
After exchanges and alternative trading venues have introduced electronic execution mechanisms worldwide, the focus of the securities trading industry shifted to the use of fully electronic trading engines by banks, brokers and their institutional customers. These Algorithmic Trading engines enable order submissions without human intervention based on quantitative models applying historical and real-time market data. Although there is a widespread discussion on the pros and cons of Algorithmic Trading and on its impact on market volatility and market quality, little is known on how algorithms actually place their orders in the market and whether and in which respect this differs form other order submissions. Based on a dataset that – for the first time – includes a specific flag to enable the identification of orders submitted by Algorithmic Trading engines, the paper investigates the extent of Algorithmic Trading activity and specifically their order placement strategies in comparison to human traders in the Xetra trading system. It is shown that Algorithmic Trading has become a relevant part of overall market activity and that Algorithmic Trading engines fundamentally differ from human traders in their order submission, modification and deletion behavior as they exploit real-time market data and latest market movements.
We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown. JEL-Classification: G14, C32
We merge administrative information from a large German discount brokerage firm with regional data to examine if financial advisors improve portfolio performance. Our data track accounts of 32,751 randomly selected individual customers over 66 months and allow direct comparison of performance across self-managed accounts and accounts run by, or in consultation with, independent financial advisors. In contrast to the picture painted by simple descriptive statistics, econometric analysis that corrects for the endogeneity of the choice of having a financial advisor suggests that advisors are associated with lower total and excess account returns, higher portfolio risk and probabilities of losses, and higher trading frequency and portfolio turnover relative to what account owners of given characteristics tend to achieve on their own. Regression analysis of who uses an IFA suggests that IFAs are matched with richer, older investors rather than with poorer, younger ones.
Despite their importance in modern electronic trading, virtually no systematic empirical evidence on the market impact of incoming orders is existing. We quantify the short-run and long-run price effect of posting a limit order by proposing a high-frequency cointegrated VAR model for ask and bid quotes and several levels of order book depth. Price impacts are estimated by means of appropriate impulse response functions. Analyzing order book data of 30 stocks traded at Euronext Amsterdam, we show that limit orders have significant market impacts and cause a dynamic (and typically asymmetric) rebalancing of the book. The strength and direction of quote and spread responses depend on the incoming orders’ aggressiveness, their size and the state of the book. We show that the effects are qualitatively quite stable across the market. Cross-sectional variations in the magnitudes of price impacts are well explained by the underlying trading frequency and relative tick size.
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results.
Analyzing interest rate risk: stochastic volatility in the term structure of government bond yields
(2009)
We propose a Nelson-Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the factor volatilities follow highly persistent processes. We show that slope and curvature risk have explanatory power for bond excess returns and illustrate that the yield and volatility factors are closely related to industrial capacity utilization, inflation, monetary policy and employment growth. JEL Classification: C5, E4, G1