Retail Financial Advice: Does One Size Fit All?

Using unique data on Canadian households, we show that financial advisors exert substantial influence over their clients\u27 asset allocation, but provide limited customization. Advisor fixed effects explain considerably more variation in portfolio risk and home bias than a broad set of investor attributes that includes risk tolerance, age, investment horizon, and financial sophistication. Advisor effects remain important even when controlling flexibly for unobserved heterogeneity through investor fixed effects. An advisor\u27s own asset allocation strongly predicts the allocations chosen on clients\u27 behalf. This one‐size‐fits‐all advice does not come cheap: advised portfolios cost 2.5% per year, or 1.5% more than life cycle funds

How Active is Your Fund Manager? A New Measure That Predicts Performance

Abstract We introduce a new measure of active portfolio management, Active Share, which represents the share of portfolio holdings that di¤er from the benchmark index holdings. We compute Active Share for domestic equity mutual funds from 1980 to 2003. We relate Active Share to fund characteristics such as size, expenses, and turnover in the cross-section, and we also examine its evolution over time. Active Share predicts fund performance: funds with the highest Active Share signi…cantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence. Non-index funds with the lowest Active Share underperform their benchmarks. JEL classi…cation: G10, G14, G20, G2

Do Limit Orders Alter Inferences about Investor Performance and Behavior?

Individual investors lose money around earnings announcements, experience poor posttrade returns, exhibit the disposition effect, and make contrarian trades. Using simulations and trading records of all individual investors in Finland, I find that these trading patterns can be explained in large part by investors' use of limit orders. These patterns arise mechanically because limit orders are price-contingent and suffer from adverse selection. Reverse causality from behavioral biases to order choices does not appear to explain my findings. I propose a simple method for measuring a data set's susceptibility to this limit order effect. Copyright (c) 2010 The American Finance Association.

Weather and Time Series Determinants of Liquidity in a Limit Order Market

http://faculty.chicagobooth.edu/ioanid.rosu/research/lr_lom.pdfWorking Paper, University of Chicago, Booth School of BusinessWhen liquidity is measured by the bid-ask spread or price impact, markets with more trading activity are typically more liquid than markets with less trading activity. But showing a causal connection from trading activity to spreads is difficult because these variables are endogenous. In the case of Finland's fully electronic limit order market, we use deseasonalized sunshine as an instrument for trading activity, and find that indeed higher trading activity causes lower spreads in the time series. We introduce another instrument for spreads and show that causality runs the other way as well: lower bid-ask spreads invite more trading activity. By using the lagged CBOE Volatility Index as an instrument, we also find that an exogenous increase in intra-day volatility causes larger spreads