Read to gain insight from ITG perspectives and proprietary research on best practices in trading and portfolio analytics.
Once a fairly esoteric subject, risk analysis and measurement has become a critical function for both portfolio managers and traders. Yet, accurate measurement and analysis of risk presents many practical challenges including the choice of risk model, pitfalls in portfolio optimization, horizon mismatches, and out-of-sample testing.
Once a fairly esoteric subject, risk analysis and measurement has become a critical function for both portfolio managers and traders.
A hedge fund’s principals spend countless hours developing their investment strategy. After extensive backtesting and simulated trading, they finally “go live”—only to discover that actual performance falls far short of their return expectations.
Mutual fund transactions occur at the fund’s Net Asset Value (NAV), typically computed at 4:00 p.m. Eastern Time using closing prices for the day. For funds whose securities trade on a foreign exchange that close before the US market, this convention can result in stale prices. Some shortterm speculators take advantage of stale prices, trading on information signals observed after the close of the foreign market and before the US market closes, earning substantial profits at the expense of long-term shareholders.
The validity of the most commonly used measure of market quality using public data, the effective spread, depends on the accuracy of trade side classification and the benchmark quote. Using a combination of NYSE’s Trade and Quote Data (TAQ) and proprietary order data, we compare the performance and the consequent effective spread biases of two widely accepted algorithms for samples of Nasdaq and NYSE stocks in the post-decimalization environment.
For funds holding securities that trade on foreign exchanges that close before the US market, the usual method of computing Net Asset Value can result in stale fund prices. Some speculators profit from stale pricing to the detriment long-term shareholders. To solve the “mutual fund timing” problem and comply with SEC guidance, mutual fund companies are using fair value models to adjust the closing prices of foreign securities.
The previous installment of “Badges” equates them to definitions of market participants. The focus was on a CFTC-proposed definition of high frequency trading, or HFT. The conclusion reached is that strict classifications for the purpose of regulation are inappropriate in today’s environment. The idea is broadened here, with respect to market structure regulation. In this week's edition of The Blotter, the focus is on the exchange/broker divide.
Understanding of the magnitude and determinants of global execution costs is important to practitioners for several reasons. These include the prediction of trading costs, determining the effect of execution costs on “live” portfolio performance, inter-market cost comparisons, and quantifying international diversification benefits. This article examines the magnitude and determinants of trading costs across 42 countries.
Actual investment performance reflects the underlying strategy of the portfolio manager and the execution costs incurred in realizing those objectives. Execution costs, especially in illiquid markets, can dramatically reduce the notional return to an investment strategy.
We show that liquidity commonality is due to co-movements in supply and demand induced by cross-sectional correlation in order types (market and limit orders), while return commonality is caused by correlation in order flows (order direction and size). Since return and liquidity commonality are caused by different economic forces, it is possible for assets to have little return correlations but high liquidity commonality.