Find out how pension funds can build custom equity portfolios that fully align with their investment and ESG policies.
Watch a step by step demonstration from Navdeep Sahote, Product Manager at SigTech as he builds a live custom equity index.
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The investment management industry is undergoing tremendous change. Indexing and ESG are reshaping investor portfolios, whereas digitisation is impacting the industry’s entire value chain. These developments represent a golden opportunity for asset owners to empower their investment processes and enhance long-term investment results.
In this video we are going to showcase how you can capitalise from custom equity portfolios by tailoring your investment strategies to your requirements using SigTech’s next-gen investment platform.
The first step to create a custom equity index is defining and creating the investment universe for the backtesting period.
The initial single stock investment universe is created by applying the EquityUniverseFilter building block in which we can specify various filters. In this example, we use the 500 largest US stocks by market capitalisation without any additional filters or constraints as a starting point.
Next, we exclude certain stocks as part of a negative ESG selection. In this example, we choose to exclude stocks from the industries oil and gas, as outlined in the exclusion list EXCLUDED_SECTORS.
We have now defined the investment universe for any point in time for the entire backtesting period.
The next step is to define the strategy’s rebalancing schedule and in this case, we will use the 3rd Friday of March, June, September and December, respectively. Additionally, we change the March 2020 rebalancing date to April 2020 to replicate the approach chosen by most index providers during the market turmoil in March and April that year.
Before we go into creating the investment strategy, we must create a historical total return time series for each stock. The SigTech platform models the full trade lifecycle of each stock, as opposed to using an approximate 'weights times returns' approach. This manifests itself in the ReinvestmentStrategy building block, which creates tradable stock objects that allow a user to handle corporate actions and dividends according to the real-world setting of its specific portfolio.
All these details are handled by the building block Create ReinvestmentStrategy.
It is worth pointing out that this method of creating a tailored historical time series for each stock in the investment universe can be called in parallel, meaning the backtest's runtime can be reduced significantly.
To review the investment universe historically, we generate indicators (ISON) of whether a stock is included in the investment universe on a given date. We do this for the starting universe, as well as for the universe excluding oil and gas stocks.
We can see here an example of the excluded stocks on any given day, as well as a graph showing the evolution of our blacklist over time. This showcases the impact on our investment universe by excluding certain stocks.
After having defined and tailored the investment universe according to the user-specific requirements, it is time to start creating the investment strategy. This can either be done from scratch, or the user can start out with various building blocks that are available on the platform.
In this example, we will create three custom index strategies using available building blocks. They are a traditional market capitalisation-weighted index, an equal-weighted index, and a minimum volatility index.
To develop and customise these three custom index strategies, we will use The SignalStrategy building block. This will convert raw strategy signals into precise strategy backtests. Here we can for instance specify what transaction costs should be applied.
The first custom index strategy is the traditional market capitalisation-weighted index. We use the (ISON) indicator to know which stock to include at what time historically and map that with the historical market capitalisation data.
Next out is an equal-weighted index where we again use the (ISON) indicator and make the calculations for both investment universes.
The last custom index that we create is a minimum volatility index. To produce this strategy we need to specify certain constraints of the optimisation that it will apply. For instance, what is the lookback window to calculate the covariance matrices used.
We have now created three customised equity index strategies using available building blocks and applying a fully customisable, coherent and transparent methodology mimicking an investor’s real-world setting. Next out is to conduct a detailed performance analysis.
We start by showing the performance and some traditional risk-/return data for the three indices applying the ESG exclusion list. These are the ones shown by default, but they can of course be fully customised by either accessing a list of predefined metrics to choose from or to create them according to your preferred specifications.
Over the chosen time period, the market capitalisation- and equal-weighted indices exhibit the highest - and actually very similar - return over the full-time period. However, from a risk-adjusted perspective (as shown by the Sharpe ratio), the performance of the three indices are quite aligned. The reason is of course the - as per construction - much lower volatility of the minimum volatility index.
We continue by showcasing the results in various rolling plots and heat maps.
To finish off this subset of performance analyses, we show some tracking error and correlation data followed by various portfolio allocation overviews.
Because of our platform’s flexibility, there is no limit to the number of analyses it can show.
The last thing we want to show you is the current portfolio allocation of the three indices. By simply defining a list of sectors, we can display a table of allocations for each of our strategies.
Looking at the sector weights, the market capitalisation-weighted index currently has a very high exposure to the technology sector, whereas the lower risk minimum volatility index has a much higher allocation to conservative sectors such as consumer non-cyclicals and healthcare.
The last thing to showcase is how these individual index strategies can be combined to construct multi-factor portfolios. We apply an equal-weighted method, meaning that each index strategy has the same weight at any given rebalancing date and a mean-variance optimisation.
This plot shows the optimisation in action for all three custom index strategies. Finally, we can display the performance, as well as a summary of statistics, for our multi-factor portfolio.
‘To summarise, the SigTech platform allows investors to empower their investment processes and enhance long-term investment results. With custom equity portfolios, you can finally tailor your index investments according to your specific requirements.’
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