Category: Finance

Textbooks in finance are written about the benefits of diversification and how to achieve your portfolio objectives. If you can find two assets that you estimate have the same expected return, in theory it makes sense to split your portfolio 50/50 among them to reduce the risk to achieve the expected value. Implicit behind this is that the returns achieved by these assets are not correlated. For instance, if your two assets are CNR and CP, if Canada goes bust, your diversification is not going to help. But if your two assets are CP and some boring and stable power generation utility out in India, chances are that the returns from the two assets are likely to be much less correlated. Computer algorithms can sort out all of these historical correlations and give you a pretty good idea of the mathematical risk, just from historical trading data.

Then we get into the business of asset allocation. Traditionally, equities and government bonds are inversely correlated to each other, and it has been a layer of portfolio protection when equities rise, you sell a little bit and buy (relative to before, lower priced) treasuries and vice versa.

However, it all goes haywire when traditional correlations do not manifest themselves.

One example is the usage of gold as a “world is going to hell” hedge and also a hedge against inflationary monetary policy decisions. In panicked market conditions, gold is just as susceptible as other asset classes for being liquidated.

Another example is the market for unsecured debt (e.g. TSX debentures or any other corporate bond that trades publicly in a reasonably liquid manner) – although many of these companies are sure-guarantees to pay out at maturity, the value of their debt trades down in market panic conditions.

Finally, another example is the usage of Bitcoin. Since there is limited historical data, there is a considerably higher element of human intuition that goes behind what the true risk profile of this asset is.

When traditional correlations break, it forces portfolio managers to either stay the course (assuming it will regress to some sort of ‘mean’), or to adjust the asset allocation to reflect the new reality with the correlations between various assets. In general, my gut feel is that markets are moving ‘faster’ than they were before, which will make institutional managers that much more challenged to adjust their models to reflect market reality.

A week ago I posted this speculation:

Today, it’s basically a continuation of this. My guess is that there will be a “flush”, a pretty significant one under that red line, will occur (let’s say to around 9800-9900). Especially in light of the president election dominating the course of the next couple months, prepare for a wild ride!

Again, a caution: this post is purely for entertainment value. My capital is far from these high-flyers that dominate the index (in rank order, Apple, Microsoft, Amazon, Facebook, Tesla, Google, Nvidia, Adobe, Paypal, Netflix).

BMO and its advisors are getting sued by clients. In the article, it claims the advisor in question in 2017 and the first half of 2018 traded on behalf of clients a leveraged short treasury, long preferred share strategy.

Both client groups allege that throughout 2017 and the first half of 2018, Mr. Liu recommended a new investment strategy that “assured safety” of their principal and provided “reasonable” investment returns.

Shortly after, clients allege they were instead placed in a high-risk strategy that involved short-selling bonds – particularly Canadian government bonds – to purchase long positions in preferred shares, many of which had variable rates or rates that reset based on interest rate movement.

According to court documents, Mr. Liu further advised the clients to begin trading on margin – investing using borrowed money – in order to purchase a larger amount of preferred shares. In some instances, clients allege Mr. Liu engaged in this strategy without informing them or seeking their permission.

I’d love to read the court documents.

I’m guessing the pitch was that you could borrow around 1.5-2.0% (short treasuries) and re-invest the proceeds in (relatively high quality rate reset) preferred shares yielding around 5-5.5%. Just throw in some cash and we can leverage this thing 5:1 and earn you a cool 15% return on equity. Sounds great!

Canada’s 5 year bonds in the first half of 2017 spent most of their time around 100-125bps, and the second half around 150-175bps. In the first half of 2018, they were at 200-225bps. So this pair of the trade would have surely lost money, but it would have been more than offset by appreciation of the preferred shares. In fact, during 2017, the trade would have looked really good and I would not be shocked if clients added more money to it:

The second leg of the trade (preferred shares) didn’t do that badly until about the fourth quarter of 2018, where preferred shares lost about 15% of their capital value. The bonds during this time would have appreciated somewhat, but depending on the amount of leverage employed, the trade would have been a significantly losing one. By the third quarter of 2019, the preferred shares would have declined another 10%.

I’m guessing it would have been after the 4th quarter in 2018 that clients came asking why they were seeing negative returns in their accounts. “Oh, don’t worry, these are normal market fluctuations, just look at the yields you’re getting!”. By the time the third quarter in 2019 came along, it looks like client losses would have been another 10% times whatever leverage factor they engaged in.

Back in June 2019 I mused about this, but it looks like others actually engaged in this trade, which is a classic example of leveraged yield chasing! It rarely ends up well unless if you close out the trade when you least want to – when the trade is working.

Here is a classic “trap” situation for market participants, especially those that follow technical analysis. I personally think technical analysis is nearly useless, except for the fact that other people find value in it, which means that the TA playbook can be used against other participants. Here is what I think is going on, although this is about as much speculative quackery as the analysis of charts, and thus I suggest treating this post as entertainment value only:

Blue lines: So what’s happening is you see the trendline is broken – technical analysis 101 recommends bailing out once the trendline is broken and shorting.

Red lines: This is a classic “descending triangle” situation, where the Nasdaq is attempting to hold support at the 10,800 level. TA 101 says once this support line is broken, that things are going to sour.

Green line: All of these retail investors at this point, with their “education”, will decide this is the point to capitulate and sell out their technology holdings because clearly things are going down. And indeed, it will be for a little bit as they bail en masse.

Except it will stop and rebound and confuse the crap out of everybody that has taken their chart-reading education.

Probably due to Robinhood, retail investors are getting into the business of option selling. Almost nobody in the retail scope should be doing this. The new professed method to riches has been selling put spreads (likely due to the fact that margin requirements for spreads are lower than flat-out selling naked puts). Robinhood loves people engaging in these strategies since they make far more per trade. Put spread selling appears to have been, at least to the end of August, a viable manner of making untold amounts of gains as they are the recipient of both price appreciation and time decay (theta).

In fact, since early June, it would have been near-impossible to lose money employing such a strategy, which is why in the month of August, you probably had hordes of people self-educating each other on the virtues of selling put spreads for a limited risk method to making free money. Free money!

The issue with put selling is that when it works you make a little (especially in relation to what you could have made had you just bought the common stock directly), but when the trade goes against you, you lose a ton of money. Many retail investors fail to calculate their risk exposure, especially in market environments in the past few days where not only do you lose on price (the delta skyrockets) but also volatility (which inflates the price of a short position and makes it much more expensive to cover).

Now the tide has turned and people are finally seeing that such strategies can make a thousand dollars a week, but lose you ten grand in a day when you bet incorrectly.

Just reading the reddit group /r/thetagang, it’s pretty apparent that a lot of people viewed this as a low-capital perpetual money making machine, at least until now. The quantitative algorithms that take the other side of these option trades, for the most part, have basically won to a degree more than one would at a casino playing a reasonably fair game of blackjack.