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How does that apply here? While we are not looking to capture the spread, the strategy does seek to avoid taking liquidity and paying the spread. Where it can do so, it will offset the bid-offer spread by earning rebates. In many cases we are able to mitigate the spread cost altogether. So, while it cannot accomplish what a HFT market-making system can achieve, it can mimic enough of its characteristics — even at low frequency — to produce substantial gains in terms of cost-reduction and return enhancement.
But this feature, while important, is not really the heart of the matter. Rather, the central point is this: that the overall strategy is an assembly of individual, independent strategies for each component stock. And it turns out that the diversification benefit of a portfolio of strategies is generally far greater than for an equal number of stocks, because the equity processes themselves will typically be correlated to a far greater degree than will corresponding trading strategies.
To take the example of the pair of stocks discussed earlier, we find that the correlation between HD and PFE over the period from to is around 0. By comparison, the correlation between the strategies for the two stocks over the same period is only 0. This is generally the case, so that a portfolio of, say, 30 equity strategies, might reasonably be expected to enjoy a level of risk that is perhaps as much as one half that of a portfolio of the underlying stocks, no matter how constructed.
This may be due to diversification in the time dimension, coupled with differences in the alpha generation mechanisms of the underlying strategies — mean reversion vs. There are, of course, many different aspects to our approach to strategy risk management. Some of these are generally applicable to strategies of all varieties, but there are others that are specific to this particular type of strategy.
A good example of the latter is how we address the issue of strategy robustness. One of the principal concerns that investors have about quantitive strategies is that they may under-perform during adverse market conditions, or even simply stop working altogether. Our approach is to stress test each of the sub-strategy models using Monte Carlo simulation and examine their performance under a wide range of different scenarios, many of which have never been seen in the historical data used to construct the models.
But we also randomize the start date of each strategy by up to a year, which reduces the likelihood of a strategy being selected simply on the strength of a lucky start. Finally, we are interested in ensuring that the performance of each sub-strategy is not overly sensitive to the specific parameter values chosen for each model. Only if the worst outcomes — the 1-in results in the left tail of the performance distribution — meet our performance criteria will the sub-strategy advance to the next stage of evaluation, simulated trading.
This gives us — and investors — a level of confidence in the ability of the strategy to continue to perform well regardless of how market conditions evolve over time. The answer is simple: it involves too much research. In a typical portfolio strategy there is a single investment idea that is applied cross-sectionally to a universe of stocks factor models, momentum models, etc.
In the strategy portfolio approach, separate strategies must be developed for each stock individually, which takes far more time and effort. Consequently such strategies must necessarily scale more slowly. Another downside to the strategy portfolio approach is that it is less able to control the portfolio characteristics.
For instance, the overall portfolio may, on average, have a beta close to zero; but there are likely to be times when a majority of the individual stock strategies align, producing a significantly higher, or lower, beta. The key here is to ask the question: what matters more — the semblance of risk control, or the actual risk characteristics of the strategy?
And while I agree that this is hardly a widely-held view, my argument would be that one cannot expect to achieve above-average performance simply by employing standard approaches at every turn. Actually, no. It is certainly unusual. But it follows quite closely the model of a proprietary trading firm, or a Fund of Funds.
There, as here, the task is to create a combined portfolio of strategies or managers , rather than by investing directly in the underlying assets. A Fund of Funds will seek to create a portfolio of strategies that have low correlations to one another, and may operate a meta-strategy for allocating capital to the component strategies, or managers. But the overall investment portfolio cannot be as easily constrained as an individual equity portfolio can be — greater leeway must be allowed for the beta, or the dollar imbalance in the longs and shorts, to vary from time to time, even if over the long term the fluctuations average out.
We estimate beta using the historical realized covariance matrix and use an ensemble approach varying the lookback window of our covariance matrix calculation to generate weights. Isolating different periods, however, provides a more nuanced perspective. Here we will pause to note a key difference in the first portfolio we constructed and the target beta portfolios.
For example, USMV has a beta of approximately 0. This is where capital efficiency enters the equation. On its own, we would have to hold a near dollar-for-dollar amount in our long equity exposures as PSTIX to hedge out beta, making it inefficient. If, however, we think from a total portfolio balance sheet perspective, things become much more interesting. At first, this appears to be a massive reduction in bonds.
Below we plot the exposure from each position as well as the net resulting exposure. This approach is not without its trade-offs. This means we must not only be incredibly comfortable with the portfolio construction of PSTIX, but we must also be comfortable in the foregone opportunity cost to allocate to other fixed income managers.
This approach may also not be effective for investors who do not currently hold much fixed income. Newfound Research and Corey Hoffstein do not take a position as to whether these securities should be recommended for any particular investor. Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds.
At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. You can connect with Corey on LinkedIn or Twitter. We know investors care deeply about protecting the capital they have worked hard to accumulate.
Newfound Research is a quantitative asset management firm with a focus on risk-managed, tactical asset allocation strategies. We were founded in August and are based out of Boston, MA. You can unsubscribe at any time by clicking the link in the footer of our emails. For information about our privacy practices, please visit our website. We use Mailchimp as our marketing platform. By clicking below to subscribe, you acknowledge that your information will be transferred to Mailchimp for processing.
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Long-short equity is. Long/short equity hedge funds typically have net long market exposure, because most managers do not hedge their entire long market value with short positions. Long/Short Equity is an investing strategy comprised of long positions on equities anticipated to rise in share price paired with short positions.