Redistribution or reproduction in whole or in part are prohibited without written permission of S&P Dow Jones Indices LLC. Copyright © 2021 S&P Dow Jones Indices LLC, its affiliates and/or their licensors. The S&P Global BMI and S&P Global Developed Aggregate Ex Collateralized Bond (USD) Total Return Index (the “S&P Indices”) are products of S&P Dow Jones Indices LLC, its affiliates and/or their licensors and has been licensed for use by Aurum Research Limited. Box size reflects the AUM of the hedge fund industry, as tracked by Aurum. Performance in the above chart is asset weighted. Information in the database is derived from multiple sources including Aurum’s own research, regulatory filings, public registers and other database providers. Source: Aurum’s proprietary Hedge Fund Data Engine database containing data on around 3,500 active hedge funds representing around $3 trillion of assets as at December 2022. based on P/E, P/B, cash flow, etc.) Quality (looking at metrics such as levels of debt, stability of earnings growth, balance sheet strength) momentum (looking at past returns over a preset timeframe ranging from days to months) however, these are common factors that are relatively easy to exploit/replicate – hence the proliferation of risk-premia products that operate in this space. Traditional QEMN portfolios consists of exposure to: Value (looking for stocks mispriced relative to their fundamental value, e.g. Managers may construct a portfolio using an optimisation process or by applying simpler rules combined with risk constraints so as to create a portfolio that is dollar and/or beta neutral, and typically with minimal sector exposure. The weights of the scores of the different fundamental data sources may be fixed or dynamic. Traditional QEMN strategies take fundamental data, such as analyst earnings estimates, balance sheet information and cash flow statement statistics, and systematically rank/score stocks against these metrics in varying proportions. Statistical arbitrage funds are typically run with a very low level of beta and are market neutral, however, this may not always be the case, with some funds able to take significant directional risk however, given the higher frequency trading nature of such funds, they are not expected to have significant correlation to markets over time. stock value models, growth, etc.), however, if these models are the more dominant driver of risk, then the fund is likely to be classified as Quantitative equity market neutral. Whilst statistical arbitrage funds tend to focus more on ‘technical’ models, some may also incorporate some longer-term models that are driven by fundamental data (e.g. publishing of analyst earnings estimates, news flow, etc.). Other statistical arbitrage funds will look to incorporate more discrete information into their process from events (e.g. Momentum models look for patterns in price data that suggest that price movements will be more persistent (i.e. ![]() Mean-reversion looks to take advantage of the phenomenon of short-term price movements occurring due to supply/demand imbalances then moving back to an equilibrium level. Typical signal types are: mean-reversion, momentum and event-driven. ![]() ![]() These patterns can help the manager forecast the future return of a stock, often over a relatively short timeframe. Statistical arbitrage funds typically take price data and its derivatives, such as correlation, volatility and other forms of market data, such as volume and order-book information to determine the existence of patterns. Some CTAs can also trade very short-term signals driven by market microstructure anomalies and patterns. Other models used in CTAs may include carry, seasonality, mean reverting or pattern recognition systems, models driven by fundamental data or non-traditional data sources. The strategy is known for running with profits and cutting losses. When rising markets slow down/stop rising, trend-followers typically reduce its position and will eventually reverse its position into a short position, which it will hold until the market starts to rally again. Many, but by no means all, CTAs are trend following (using historical prices to determine predictable ‘trending patterns’) buying into markets where prices are rising and selling where markets are falling. CTAs are typically extremely systematised with straight through processing from signal generation to execution. ![]() Technically, a CTA is a trader of futures contracts as defined by the CFTC and historically, there were many CTAs who were not systematic such traders are more likely to be classified as ‘Global Macro’. CTAs (Commodity trading advisors) take primarily directional positions in index level or macro instruments, such as futures or FX contracts, in a systematic fashion.
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