Big Ideas

Many asset class allocation, asset valuation/selection and asset return anomaly studies ignore or treat lightly the implications of liquidity constraints. What are those implications and how serious are they? In his December 2010 paper entitled “Comatose Markets: What If Liquidity is Not the Norm?”, Aswath Damodaran examines how introducing illiquidity into decision processes affects investors with different time horizons and investment strategies. He focuses on trading friction (brokerage fees, bid-ask spread and price impact) as an illiquidity measurement. Using results from prior research and recent data on liquidity variations within and across asset classes, he finds that: Keep Reading

Information technology has lowered barriers for creating/operating financial asset exchanges (venues for matching supply and demand). Proliferation of low-cost venues elevates competition for investor dollars and tends to depress transaction fees. Automated, broadened supply/demand matching tends to depress bid-ask spreads. This evolving market ecology attracts high-frequency traders (HFT), enabled by new technology to exploit short-term market predictability. Do such traders succeed, and how do they impact the markets in which they trade? In his December 2010 preliminary paper entitled “High Frequency Trading and The New-Market Makers”, Albert Menkveld examines the behavior of one large high-frequency trader (HFT) and the effects of associated trading on the exchanges in which this HFT participates. Using high-frequency (one-second) quote and trade data from two European exchanges during January 1, 2007 through June 17, 2008, he finds that: Keep Reading

Is the aggregate effect of technical trading visible and exploitable at the equity index level? In his March 2007 paper entitled “The Interaction between the Aggregate Behavior of Technical Trading Systems and Stock Price Dynamics”, Stephan Schulmeister investigates how S&P 500 Index futures prices relate to the aggregate trading signals of 2,580 widely used trend-following and contrarian technical trading rules (moving average, momentum and relative strength) implemented with 30-minute data. Using 30-minute data for S&P 500 Index futures over the period 1983-2000, he finds that: Keep Reading

How strongly should investors believe in models of financial markets and asset prices? In his contemplative November 2010 paper entitled “Metaphors, Models & Theories”, Emanuel Derman reflects on the tractability of finance in general and the degree to which financial models are useful. Largely by comparison to theorizing and modeling in physics, he observes that: Keep Reading

How has the dramatic increase in trading over the past decade materially affected the stock market environment? In their October 2010 paper entitled “Recent Trends in Trading Activity and Market Quality”, Tarun Chordia, Richard Roll and Avanidhar Subrahmanyam examine trends in trading activity and the impacts of these trends on market efficiency. Using trade and quote data for a broad sample of NYSE stocks over the period 1993 through 2008, they find that: Keep Reading

What are the implications for investors of a trend toward strategic and tactical allocation to index proxies (exchange-traded funds and derivatives) rather than individual securities? The July 2010 paper entitled “On the Economic Consequences of Index-Linked Investing” by Jeffrey Wurgler provides an overview of the effects of index-linked investing on stock prices, risk-return trade-offs, investor portfolio allocation decisions and fund manager skill assessments. The September 2010 paper entitled “Index Investment and Financialization of Commodities” by Ke Tang and Wei Xiong investigates the effects of increased investing during the last decade in commodity indexes. The October 2010 paper entitled “The Financialization of Commodity Futures Markets or: How I Learned to Stop Worrying and Love the Index Funds” by Scott Irwin and Dwight Sanders surveys research on the impact of commodity index fund growth on commodity price behavior. Using results of prior research and recent data on indexation investment levels, index returns and component asset returns, these papers find that: Keep Reading

Market capitalization is the most frequently used metric for weighting the individual stock components of market indexes. Other approaches range from equal weighting to weighting on firm fundamentals to weighting generated by return-risk optimization. How do such alternative metrics work empirically? In the October 2010 draft of their paper entitled “A Survey of Alternative Equity Index Strategies”, Tzee-man Chow, Jason Hsu, Vitali Kalesnik and Bryce Little examine several popular passive index weighting alternatives to market capitalization. They impose common assumptions to backtest these alternatives on U.S. and global equity data over long periods with either annual or quarterly rebalancing. They also apply the Fama-French three-factor model to investigate sources of outperformance relative to capitalization-weighted benchmarks. Using stock/firm data for the 1,000 largest global firms spanning 1987-2009 and for the largest 1,000 U.S. firms spanning 1964-2009, they find that: Keep Reading

Does high-frequency trading amplify noise and thereby reduce the signal-to-noise ratio in stock returns? In his August 2010 paper entitled “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Frank Zhang examines the effect of high-frequency trading on stock price volatility and on incorporation of fundamental news into price. He defines high-frequency trading as that driven by fully automated trading strategies with very high trading volume and extremely short holding periods ranging from milliseconds to minutes and possibly hours (typically not overnight). He estimates the volume of high-frequency trading as the residual after accounting for institutional and individual investor activities. Using price, trading and institutional holdings data for a broad sample of U.S. stocks from the first quarter of 1985 through the second quarter of 2009, he finds that: Keep Reading

How stable are risk premiums? How should investors respond to instabilities? In his August 2010 paper entitled “A New ‘Risky’ World Order: Unstable Risk Premiums: Implications for Practice”, Aswath Damodaran presents approaches for estimating equity, bond and real asset risk premiums that are imprecise, unstable and linked across markets. He also explores the implications of dynamic, linked premiums for asset allocation, market timing and asset valuation. Using long-run data for all three asset classes, he concludes that: Keep Reading

A reader commented and asked: “I frequently read that stock prices are not normally distributed, and that by assuming they are, an investor will tend to underestimate market risk. One paper I read says their distribution is leptokurtic, a distribution that has a more acute peak around the mean (that is, a higher probability than a normally distributed variable of values near the mean) and fatter tails (that is, a higher probability than a normally distributed variable of extreme values). My question is, given this fact, is there a practical way for retail investors who are not statisticians and who don’t have access to sophisticated tools, to better estimate risks than using functions that assume a normal distribution?” Keep Reading