Our Value strategy saw an 6% increase in the valuation of our holdings during since its inception in September 2017.
This article summarizes the strategy’s current positions and documents those bought and sold to get where we are today.
We document how the strategy works and examine the risks we are taking on by following it.
Back in July last year, we published Building A Better Value Strategy, an article that outlined a data-driven trading strategy that invested in companies with improving earnings and dividend distributions and that were cheap compared to their cash flows and sales figures. The strategy backtested extremely well, showing 30% annual total returns over the last 17 years.
We have traded this strategy with real money from September 2017 and can report a total return of 6.1% over the 4 month period (you can see a wealth of risk and position statistics by clicking here):
Total Return (4 months)
Win Rate (Closed)
Two things stand out when you view the statistics from our InvestorsEdge.net model:
The first is that while the compound annual gains (CAGR) are estimated to be a respectable 19.6%, this figure remains below our forecasted 30%. This is mainly due to a uniform drop in the value of our positions during October and November that our strategy is still climbing out of. We believe that the strategy statistics will revert tick higher over the next couple of months. This movement also highlights the second takeaway from our chart – volatility. This can be best viewed through our drawdowns chart:
You can see from the above graph that our strategy experienced a drawdown of 10%. This may be too much based on your risk profile, however looking at our original research the last 17 years followed a similar pattern:
We can see from our research that our portfolio’s valuation regularly (i.e. 2-4 times a year) drops by 10% or more. Our conclusion is that the strategy is performing pretty much as expected, and volatility for this type of strategy should be expected.
The strategy has bought positions in companies representing 9 out of 28 sectors:
The historical allocations will increase and become more diversified as time goes by – for now our portfolio has been overweight technology stocks, which could explain at least some of its outperformance when compared to its S&P 500 benchmark.
As of 10th January 2018, the strategy held the following positions:
The strategy was built and tested using the InvestorsEdge.net platform – you can find more risk and strategy performance information the model, together with the actual strategy definition details, by clicking here.
As a quick summary, each month we rebalance our portfolio using the following rules – we begin by defining a universe of stocks that have:
EPS for the last twelve months greater than EPS reported 2 years ago.
Net Current Assets (NCAV) greater than 0 (NCAV is calculated as current assets – total liabilities – preferred stock).
On 1st January (our last rebalance point), this would have returned 571 stocks, which we then rank using the following factors:
Change in twelve month EPS from the previous quarter
Number of consecutive dividend increases over the last 10 years
Price to Sales
Price to Free Cash Flow
We then buy the top 10 stocks in our ranked universe of securities, dropping existing positions unless they continue to rank in the top 10.
Our original backtests displayed compounded average returns of 30% a year since 2000 with remarkably low volatility for a strategy that invests primarily in small cap companies.
A key risk that we always examine with mechanical investing strategies is that the data phenomenon that we are exploiting will simply stop working. To combat this, we look to see if a strategy intuitively makes sense – our model invests in companies with historical EPS growth, high yields and assets and that are cheap relative to their sales and free cash flows. To us, these are all logical and understandable factors as to why our system works and should continue to be profitable.
The average company our strategy invests in has a market capitalization of $50m-$2.5bn, leading to potential problems exiting positions at the lower end of our range in a market downturn. Risk appetite is an individual thing – for us the enhanced returns that come from focusing on smaller companies more than compensate the liquidity risk we take on. If this is a concern, operating the strategy with a higher market cap threshold would have resulted in smaller but still substantial historical profits.
Our Value strategy has had a great first few months, increasing the value of our funds by 6% since September 2017.
As you can see by the disclosure, we have invested in this strategy with our own funds – now that we’ve got a few months of history under our belt, we’ll be reporting how we are getting on a monthly basis.