Maximizing Returns through Strategic Equity Intelligence

 Strategy Overview

At BlackIV, we believe a balanced portfolio is critical to achieving steady, long-term performance and returns. Most of our product offerings center around options trades, as that’s where the most money can be gained in a short amount of time, and in a single trade. However, given the high risk nature of options trading, we never enter any position with more than 2-5% of our total portfolio value. We always have about a 20-50% cash position to ensure liquidity is available for when our signals start flashing, but what do we do the rest of the time? One portion of our strategy is standard, long equity positions.

Equity positions are determined by evaluating valuation and performance ratios of companies. The most commonly used valuation ratio is Price/Earnings, the price of the stock divided by the profit generated in a given quarter or year. While we agree that this ratio is valuable, there is an extensive list of such metrics that are needed determine the best equity investments.

Data analysis and backtesting are always performed prior to implementing any new strategy, and equity trading is no exception. Below is the backtest performance of a portfolio with a $100,000 initial investment, starting in February 1998. The portfolio is updated on a quarterly basis, as new financial reports, such as 10-K (annual reports) and 10-Q (quarterly reports), are released. The portfolio is divided in half, with each equity position typically held for six months. Q1 positions roll over into Q3, and new positions are established, while Q2 positions roll into Q4. The graph below shows the overall portfolio value at half-year increments.

 The Equity Strategy above , v1.0, total portfolio performance from February, 1998 to May, 2024. NOTE: This does NOT show the performance of an actual portfolio.

Below are the performance metrics for each half-year period. Said another way, the below returns are NOT annualized! These are the returns for a given half year, as each stock position is held for 6 months. Note that the Average of 17% is higher than the median value of 7%, meaning that the return distribution has a positive skew and is pulled up by a few, high performing stocks.

Strategy Components

The stocks that constitute the strategy are divided into Large, Mid, Small, and Micro Cap. The dollar values used to define these ranges are below.

The performance of each of these categories is somewhat independent of one another, which is ideal for a balanced portfolio.

Because there is a higher historical return for smaller capitalization stocks, we use the following allocations to each market cap category every quarter:

Large Cap

The Large Cap data begins in May, 2007. There is a gap in available stocks between November, 2013 and May, 2016. There are a total of 73 stocks in the Large Cap category found in the backtest, with an average 6 month return of 10.3%. Because the median is 10.5%, very close to the average, there is little skew in the return distribution of the Large Cap component. A total of 73% of individual stocks bought and held for the 6 month period had a positive return, while 27% had a negative return.

If investing strictly using the Large Cap component of the overall strategy, the total return is 1122% from May 2007 to today.

Large Cap Component, Total Return

Mid Cap

The Mid Cap data begins in May, 2001. There are 2 notable gaps in available stocks between November, 2006 and May, 2009, and November, 2013 and May, 2017. There are a total of 104 stocks in the backtest, with an average return of 9.9%. Because the median value is smaller than the average, there is a slight positive skew in this dataset, but it is still relatively small. A total of 66% of individual stocks bought and held for the 6 month period had a positive return, while 33% had a negative return.

If investing strictly in the Mid Cap component of the strategy, the overall return is 503% from May, 2001 to today.

Mid Cap Component, Total Return

 Small Cap

The Small Cap data begins in February, 1999. There are no significant gaps in the dataset, as there are many more Small Cap stocks than Large and Mid available at any given time. There are a total of 221 stocks in the backtest, with an average return of 22%. It is in the Small Cap component that we begin to see a significant positive skew in the dataset, with overall performance being improved by a few very well performing stocks. A total of 62% of individual stocks bought and held for the 6 month period had a positive return, while 38% had a negative return.

If investing strictly in the Small Cap component of the strategy, the overall return is 7242% from February, 1999 to today. NOTE: See the Discussion section below for the limits of this component, as this return cannot be fully realized!

Small Cap Component, Total Return

Micro Cap

The Micro Cap data begins in February, 1999. There are no significant gaps in the dataset, as there are many more Micro Cap stocks than Large, Mid, and even Small Cap stocks available at any given time. There are a total of 513 stocks in the backtest, with an average return of 46%. The Micro Cap component exhibits even greater positive skew, with overall performance being greatly improved by a few very well performing stocks. A total of 59% of individual stocks bought and held for the 6 month period had a positive return, while 41% had a negative return.

If investing strictly in the Micro Cap component of the strategy, the overall return is 9,473,923% from February, 1999 to today. NOTE: See the Discussion section below for the limits of this component, as this return cannot be fully realized!

 Micro Cap Component, Total Return 

Discussion

This section discusses our approach to developing and analyzing this strategy, the limits of the strategy, and some assumptions we made along the way.

Analysis Approach

Because there are more companies with smaller market capitalization, the performance and valuation metric requirements are stricter the smaller the company is. Careful tolerancing was performed to find the ideal balance between number of stocks and maximum returns. In data modelling, it is easy to overfit a model to the input dataset. In this case, implementing strict ratios to the point that the strategy only consists of a handful of stocks, or even 1 stock, with ~100% returns per year. With such a model, one cannot expect to see that kind of return going forward.

Limits of the Strategy 

It is immediately apparent that the Small and Micro Cap components perform far better, on average, than the Large and Mid Cap components. So why do we keep the Large and Mid Cap components? The main reason is stability. From 2013-2016, the Small and Micro Cap components experience a drop of -59% and -62%, respectively. Realistically, most traders would consider abandoning the overall strategy after experiencing a loss of this magnitude, and would then miss out on the rebound in the following years. When appropriately blended with the Large and Mid Cap, this drop is reduced to -31%, a significant improvement and easier for the average trader to stomach (not easy, mind you).

The other reason to include the Large and Mid Cap components in the strategy is the need to reduce the Small and Micro Cap constituents of the portfolio over time. As the overall portfolio value increases, it will become less viable to buy into Micro Cap stocks and then exit in 6 months time. While the upper limit on Micro Cap stocks is $300,000,000, many stocks have a much smaller market cap. If you have a portfolio size such that each Micro Cap position is worth $1 million, and the target company only has a market cap of $25 million, you will be buying 4% of the overall company. This will significantly affect how the company is traded in ways that are not modeled here, and isn’t advisable as part of this method that assumes an index-like approach to investing.

If this applies to you, where your portfolio is large enough that a Micro Cap allocation causes you to buy more than 0.5% of the overall company, it is recommended to NOT participate in that component of the strategy. This also applies to the Small Cap components, and even the Mid and Large Cap components for very wealthy investors.

Assumptions

A few notable assumptions and decisions were made during the analysis and backtesting process. Some of these have been mentioned before in this analysis, they are included here for easier reference:

• There will not always be stocks available for a given market cap category, and money for a given market cap should instead be placed in short term treasuries or a money market fund. Note that this was not done for the backtest, we assumed no growth on cash positions for simplicity.

• This analysis focuses on stocks traded on the New York Stock Exchange, and the NASDAQ. This was done due to data availability and cost, but more importantly market access. While there are many other exchanges around the world, there are significant barriers to trading in those markets and international rules and regulations that can complicate the process. It was easiest to assess trading only in the most open, and liquid, capital markets.

• The dataset includes equity data going back to the mid 1990s. Ideally, we would include data going back 100 years or more, but this becomes cost prohibitive. Given that general value investing is a well understood and time tested method, we reasonably assume our methods would work in those time periods.

Conclusion

Past performance is never an indicator of future results. However, this strategy performed well through the pandemic crash and the financial crisis. It shows positive returns in periods of time with low interest rates, and with inflation. The strategy is also based on value and fundamental investing, which is one of the most well established strategies for generating long term wealth. For these reasons, the strategy may be able to achieve alpha, or abnormal positive returns not explained by broader market movements, over the medium to long term.

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