Random Portfolio benchmarks

The growth of passive investing has a lot to do with investors migrating to lower-cost investment vehicles, but implicit in the migration to passive frameworks is a presumably lower risk metric. Investors want to be exposed to equities but don’t want to take risks beyond those inherent in the benchmark indexes they are investing in. Additionally, index investors are opting out of the risks of active management because the returns generated have recently not kept pace with the index benchmarks. But indexes are not what investors should be using as benchmarks. They don’t represent a truly passive approach to the market. The only truly passive approach to the markets is one that employs random portfolio construction.

Why random portfolios? Because all indexes are systematically biased by their factor premises. The prevalent index factor is size (market capitalization) but other factor frameworks include sector, industry, value, growth, momentum, and smart beta. Each of these index frameworks employs a systematic weighting of components based on a predetermined valuation that aims to minimize variability of returns based on defined factors.

Take a look at the S&P 500 index. It’s the most traded index in the world - through ETFs like SPY - but more importantly is the primary benchmark for U.S. equities. The performance S&P 500 index guides investors in terms of relative performance of actively managed funds and ultimately is the most broadly used compensation metric of the asset management industry. The fees investors pay these managers and, ultimately, the employment, compensation and rewards these fees fuel depend on the structure of the index.

The S&P 500 index is first and foremost a members club. Stocks are included in the index by decision of a committee. Yes, constituent stocks must meet certain primary criteria - market capitalization (float-adjusted weightings), liquidity, domicile, public float, sector classification, financial viability, and length of time publicly traded and stock exchange - but the criteria is set by a member board. And it’s set with a certain purpose in mind: the index is a gauge of large cap U.S. equities.

So we know that the SPX is a factor-based measure of U.S. equities - it tells us about the price movement of large-cap stocks. That should surprise few people. Implicit in this size factor is the fact that the SPX accounts for about 80% of the entire capitalization of the U.S. stock market. That’s a good chunk of the assets invested in this market.

However, the size factor weighting of the SPX is problematic when it comes to fulfilling the index’s role as a benchmark. The size bias distorts the benchmark performance and concentrates on the largest companies, often adding additional risk in those constituents because many are in the same sector or industry. In fact, the current weighting of the SPX shows that about 28% of the index weighting is in technology stocks. Further, the top 4 stocks by weight in the index are Apple, Microsoft, Amazon, and Facebook. The S&P 500 index has a decidedly tech bias at this time, a considerable sector risk for a benchmark index that is ostensibly supposed to track the overall performance of U.S. equities.

That may be a reasonably correct weighting of big cap U.S. equities, however, it’s not a correct benchmark measure of alpha. Alpha is the intelligence that extracts investment returns above the market performance. A portfolio of stocks constructed with a size bias only tells us about the performance of the bias. It does not represent the performance of a naive portfolio - a portfolio of stocks that has no factor bias. A naive portfolio would not have a selection criteria that restricts to a subset of a given universe of stocks. It would be a portfolio derived from a set of randomly selected stocks.

Random portfolio returns give us an estimate of the returns that are built into the broad market. Irrespective of factors that deliver alpha, a measure of random portfolio returns tells us the returns a given market generates without having any specific intelligence about how to generate those returns. They are the returns that would be generated by an untrained monkey.

What have been the random portfolio returns of the U.S. equity market? How do they compare to the returns of the S&P 500 index? Below is an annual comparison of 52-week returns (%) of the market cap index and the mean (average) return (%) of randomly selected portfolios. The last column shows the differences in returns (%) between the market cap index (SPX) and the random portfolios.

The random portfolio returns are the mean return (%) of 1,000 randomly selected portfolios of 100 common stocks selected at the beginning of the return period. That would be equivalent to 1,000 different monkeys picking a 100 stock portfolio, then taking the average return of those 1,000 portfolios after the 52-week period. The universe of stocks from which these random portfolios are selected includes all NYSE/Nasdaq listed common stocks that have traded for at least 40-weeks trading at a price above $2 and weekly trading volume above 100,000 shares. That would be a universe of about 3,900 stocks.

Annual returns (%) - SPX and random portfolios

This summary tells us that the S&P 500 index has only out-performed the random portfolios in 7 of the last 15 years (2003-2017) and that the sum of the differences in return is -26.3%. The monkey would have outperformed the S&P 500 index over the period by a significant amount. However, we can also see that much of this outperformance comes in 2003 and 2009, both years where the overall market enjoyed exceptional returns after a bear market. Clearly, those were periods where the size bias of the S&P 500 index excluded it from returns that were delivered elsewhere in the market (small-cap stocks, growth stocks, momentum stocks, etc.).

Presently, the S&P 500 index is providing excess returns above the benchmark represented by random portfolios. On the surface that tells us that portfolios weighted toward large-cap - specifically large-cap technology stocks - are outperforming the market. They will until they don’t. Investors should be aware of where their returns are coming from and where their risk is situated. The S&P 500 index is not a passive index. It is not market agnostic.

Introducing the 'Map of Stock Trends'

The Stock Trends Inference Model is a quantitative approach to interpreting the categorical data that is the core value-added analysis presented here. The Stock Trends indicators are derived from base tenets of the market technician’s encyclopedia - a toolset designed to reduce a complex market dynamic to a categorical, and hierarchical framework. By evaluating the statistical significance of this framework we can apply meaningful algorithmic trading methods.

However, the first step is to understand the data and interpret the Stock Trends Inference Model results. Every week we sample 30-years of data to assign a probability for future returns on over 7,000 North American stocks. Using combinations of categorical data and making assumptions about the distribution of returns, we apply statistical inference methods to differentiate stocks (ETFs and income trusts, too) by the estimated returns in the coming periods (4-weeks, 13-weeks, and 40-weeks). You can see the result of that analysis in the Profile section of each Stock Trends Report.

I’ve already introduced the Stock Trends Inference Model in previous editorials. Subscribers to Stock Trends Weekly Reporter can interpret this information weekly, as well as review the reports on issues with the best expected returns. The Stock Trends ‘Select’ report, as well as the Top 4-wk/13-wk/40-wk returns expectations reports give users a new way to make the Stock Trends reports actionable.

However, these reports can be augmented by data visualizations. Graphical presentations of data are always useful in translating vast data points into more accessible interpretations. A good graph saves us time and points us in the right direction.

The Stock Trends Profile reports include heatmaps which help us compare returns expectations among industry group member stocks. Another useful display method for this data, especially when we want to broaden the use of the data hierarchy, is a treemap. A treemap is specifically designed for hierarchical data and is commonly used. A popular example in our equity analysis space is the Map of the Market.

Today I am introducing a treemap of the Stock Trends Inference Model - the Map of Stock Trends. It takes the data results from the weekly analysis, sorting 4-week and 13-week returns expectations by trend category.

In the treemaps displayed below large capitalization stocks (U.S. stocks with a market cap greater than $1-billion, Canadian stocks with market cap greater than $500-million) are grouped by Stock Trends indicator (Bullish , Weak Bullish , Bearish , Weak Bearish , Bullish Crossover , Bearish Crossover ). Each stock within these groups are visually differentiated in two ways: spatially by their relative probability of a return greater than the base 13-week mean random return (2.19%) , with larger cells (higher probabilities) sorted and displayed from the upper left quadrant and moving down to the lower right corner for the lower value. Secondly, the 4-week returns expectations are differentiated visually by color gradation, with darker green hues representing stocks with higher probabilities of exceeding the base average 4-week random return (0%) and darker red hues representing the stocks with the poorest probabiltity of a positive return in 4-weeks.

 

 

Dark green cells in the upper left of each trend category are stocks with the best statistical trend characteristics. Dark red cells in the lower right quadrant of each trend category are stocks with the worst statistical trend characteristics.

Below are the current Map of Stock Trends treemaps for the U.S. and Canadian stock markets. Each Stock Trends trend indicator category grouping is identified by the translucent indicators in the background of each box. In the future the treemap will be developed in an application that allows users to click on an individual cell and go directly to individual Stock Trends Reports, but for now the visualizations help direct us to the stocks with the most favourable current Stock Trends Reports.

 

U.S. stock exchanges - big cap stocks

Toronto Stock Exchange - big cap stocks

Beyond the Bull

A colleague of mine, Ken Norquay of Castlemoore Inc., has published a new book – Beyond the Bull.  Available at Amazon.

 

Synopsis of Beyond The Bull

The old stock market is dead. What worked in the 1980s and 1990s hasn’t worked in the new century. You don’t believe me? Check the 10-year rate of return on your equity mutual funds. What went wrong?

Old Paradigm: clients have financial needs. The industry has licensed salesmen to sell to those needs. Banks, mutual fund corporations, brokerage firms and insurance companies have financial products to sell to those clients. It’s a salesman’s world. It’s all about telling customers what they should buy and shy they should buy it.

New Paradigm: it’s financial war. There are winners and losers. Win, don’t lose.

Beyond the Bull helps modern investors understand the truth about modern stock markets. Ken Norquay, a 33-year veteran of the financial wars, applies ancient wisdom to modern financial markets. The “salesmen” aspect of the stock markets clouds the reality of modern finance; sugar coated reality exposes investors to unnecessary financial risk. Beyond the Bull will help investors see through the bullmanship.

But let’s not blame the salesmen. Beyond the Bull wants you to look at yourself first. It’s your money that’s at risk. Are you your own worst enemy in the financial wars? Is there a gap between the reality of today’s markets and what you think about today’s markets?

This is not a time for despair and inaction. Beyond The Bull will help its readers adjust their financial thinking to suit the reality of today’s financial markets.

Outline:

1. The dominant figure in the stock market is the salesman. The salesman’s art is persuasive bullmanship. In the salesman’s world, everyone wins.

2. But the stock market is more like military combat:

-Luck counts.

-Deception is a key feature of the stock market.

-It is complex, not simple.

3. Because our human nature is to seek pleasure and avoid pain, we can be our own worst enemy in the stock market.

4. The ‘Theory of Contrary Opinion’ illustrates why most investors lose money over the long term. We explain this in detail.

5. Beyond the Bull [BTB] explains in detail how to succeed in investing by understanding our own primitive brain functions and learning to think objectively.

6. BTB introduces a new paradigm of objective thinking about the markets and deal with the salesman’s bullmanship.

7. In financial combat, there are five key spheres we must master in order to be winners. BTB reviews these five things in detail and encourages readers to take more responsibility for their own financial fortunes.

Launch of Stock Trends Traders Network

In an effort to create a more integrated investor community, Stocktrends Publications would like to invite you to join Stock Trends Traders Network, a social networking site open to investors who use trend analysis in their trading decisions. Like other popular networking sites Stock Trends Traders Network allows users to communicate about shared interests. The site will offer you an opportunity to share your investment perspectives with other users. Member forums and groups allow for users to engage and interact with other community members, including Stock Trends Analyst, Skot Kortje. Other features include blogging, as well as photo and video uploads, which can be used to share investment-related media. We hope that Stock Trends followers and other investors interested in joining this new community will accept this invitation and be among the first to join. A successful Stock Trends Traders Network requires active involvement from its users.

To join Stock Trends Traders Network click on the following link:
http://stocktrends.ning.com/?xgi=5vleWGk