Read to gain insight from ITG perspectives and proprietary research on best practices in trading and portfolio analytics.
We introduce an activity-based approach to trading performance comparisons, in which the common denominator is an individual security. The framework faciliates answers to three questions. Is there valuable information in peer trading comparisons if one analyzes performance on a stock-by-stock basis? Aggregated comparisons across institutions and brokers disguise important differences in the effectiveness of trading process. How does a stock trade? An answer lies in the characterization of single-stock profiles. The approach exploits institutional trading activity, combining such information with market data, and repurposing old tools with the goal of reducing institutional trading cost. How does one characterize the liquidity profile of a portfolio? The answer relies on observed liquidity, as evidenced by institutional trading activity. The solution depends on building the portfolio profile up from individual securities, and yields not only the implementation costs of liquidation, for example, but also the horizon over which this may be accomplished.
This is a study of the effect of social media sentiment on pricing and trading fundamentals in the foreign exchange market. Examination of the predictability of prices, the cost and depth of liquidity and the topography of the global limit order book for a currency pair is the focus. Stand-alone forecasting exercises are combined with event studies in order to reach conclusions. There is some promise in price forecasting results, but work remains to be done in terms of strategy and hedging applications. Results with respect to FX market structure are less encouraging, with the possible exception of a tight correlation between sentiment and the cost of liquidity.
Fixed income execution costs are dependent on multiple, potentially nonlinear variables, so ITG has applied modern machine-learning methods to identify hidden relationships and patterns. ITG’s model shows that larger orders are less sensitive to trading volume and volatility, but that these “equity-type” characteristics explain a considerable variation in effective spread predictions as bond characteristics change.
A VERSION OF THIS ARTICLE ORIGINALLY RAN ON PENSIONS & INVESTMENTS ON JANUARY 27, 2017.
The Securities and Exchange Commission has voted to adopt new rules affecting the
management of liquidity risk in mutual fund portfolios. The rules are set to take effect in late
2018 for most mutual fund managers, with smaller managers (<$1B AUM) given until mid-
2019 to comply). While these regulations will impose restrictions on what assets funds can
hold and in what quantities, we believe that funds which adapt appropriately to the will be
rewarded with lower transaction costs and higher returns for their investors.
ITG’s size-adjusted spread (SaS) cost estimates provide guidance on the anticipated costs associated with instantaneous spot trade executions, measured relative to the prevailing mid-quote rate at the trade time. The underlying data for our model contains dealer quotes from 6 global banks and 5 major ECNs. By varying the manner in which we consolidate the limit order book across trading venues / liquidity providers, we are able to reflect different trading styles and credit tiers as well as varying degrees of sophistication of market participants. The use of the empirical limit order book enables us to construct cost estimates for instantaneous trading at various consolidation levels, deal sizes, as well as at various times of the day.
Incorporating the benefits of portfolio optimization doesn’t have to disturb the core beliefs of the fundamental manager. The problem is how to characterize the solution in a way that captures the variety of portfolio strategies in the market. The simplicity of doughnut portfolio construction solves the problem.
The ability to anticipate market volumes and volatility has considerable value in a
variety of trading applications. Accurate forecasts of market conditions allow buyand
sell-side traders to adjust their strategies to improve trading performance. In
addition, such information facilitates informed decisions regarding the difficulty of
trading on a given day, the size of an order that can be realistically completed,
and the true relative difficulty of trading the individual names in portfolio trades.
Looking back at the data we’ve collected from 2015, two themes emerge: ongoing market structure developments and continued market volatility, especially in Q3. This document highlights the impact of both of these factors on trading activity, market conditions and performance. We’ll employ detailed data analysis to answer common questions and give buyside traders insights into Asia Pacific markets.
I was on the trading floor when the CBOE 250 Index contract was launched in 1988. It was an innovation at the time, and a collaborative effort between the futures and options industries. The futures traders went wild within their group; the options traders quietly assessed things and then traded amongst themselves. Within five months, trading was nonexistent.
SUMMARY While Best Execution and Transaction Cost Analysis (TCA) are well-established in equity trading, other asset classes have been slower to adopt such techniques due to limitations in market data and market structure characteristics. In Over-thecounter (OTC) markets there has...