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Positioning in volatile times

By Guest Blogger Ryan Lewenza

Is it me or are we in a particularly volatile and uncertain time right now? The headlines are downright scary these days. In no particular order I’m concerned about the impact from Trump’s ongoing trade wars, the newly elected UK PM Boris Johnson and his willingness to leave the EU with no deal, the crumbling Iran nuclear agreement and their recent transgressions (e.g., shooting down a US drone and seizing a British oil tanker), climate change, which is leading to record temperatures across Europe, increased flooding, wildfires and droughts, and the slowing US/global economy, as evidenced by the big drop in government bond yields and currently an inverted yield curve.

It’s no wonder then that we’ve seen an increase in market volatility over the last few years. While 2017 was a dream with very little market volatility, since then we’ve seen some material market declines. As seen below, the S&P 500 has experienced three 10% corrections and one 7% sell-off since the beginning of 2018. So given this environment the two key questions are: 1) is this increase in volatility the precursor to a more significant economic slowdown and market decline; and 2) how should investors be positioning their portfolios?

The S&P 500 Has Experienced Increased Volatility

Source: Stockcharts.com, Turner Investments

First, despite my stated concerns above, I remain fundamentally bullish and see a recession and bear market as remote over the next 9-12 months. This view is predicated on: 1) while the US/global economy is slowing it continues to grow a healthy clip, likely around 2.5% in the US and 3.5% globally for this year; 2) the global central banks are responding to the slowing global economy by cutting interest rates, thus adding stimulus and support to the economy; 3) I am not seeing any significant credit/market excesses like that seen during the tech crash of 2000 and the subprime meltdown of 2007; 4) any resolution to the Trump/China trade war could provide a catalyst to the global economy and equity markets; and 5) technically, the equity markets look great with the US equity markets recently breaking out of their year-and-half long trading range, making new all-time highs. Given this we continue to position portfolios for more upside.

However, risks remain elevated, as noted above, and therefore we continue to reduce risk in client portfolios by switching higher risk positions for lower risk ones. Below is a great visual from Blackrock that essentially captures how we construct and adjust portfolios over time.

Steps to De-Risk Portfolios

Source: Blackrock

Our first line of defense to these risks is our balanced portfolio of 60% in growth equities and 40% in safer fixed income. The bonds help to smooth out portfolio volatility and provide a hedge to our growth equities.

Then, based on our economic and fundamental outlook we adjust our security holdings. When things look great (economy is surging, earnings are rising, central banks are keeping rates low) we favour higher growth investments like US small caps, technology, and high yield bonds. I call this phase 1 of cycle investing.

As the business/market cycle matures and central banks begin to hike rates to help moderate the economy and inflation, we move on to phase 2, which entails de-risking the portfolio by investing in lower risk, lower volatility stocks. This is where we stand today. Specifically, this includes buying ETFs of low volatility stocks and REITS, a focus more on high-quality blue chip companies that pay dividends, investment grade corporate bonds and some government bonds, and a slightly higher cash balance than normal. This is exactly how we’ve been positioning portfolios over the last year or two. For example, we’ve been locking in profits on our small and mid-cap positions and adding to lower risk equities like blue chip dividend and healthcare stocks.

The final phase is phase 3, which entails positioning portfolios for the dreaded recession and bear market. This would include buying more government bonds and increasing cash. During these periods we’re not trying to make clients 6%, we’re trying not to lose them 20%. Fortunately, these dreaded bear markets are short in nature (they last on average 11 months), and are always followed by recovering bull markets.

Now to be clear, in this phase I’m not talking about selling all equities and going to cash since that’s timing the markets, which is very difficult to do consistently. Rather, I’m talking about tweaking the portfolio and taking extra precautions in a very challenging macro environment.

In summary, I view investing in 3 phases and believe we’re currently in phase 2, which entails positioning portfolios for continued growth, but taking smaller ‘bets’ by investing in more defensive investments like low volatility stocks, REITs, healthcare and high-quality bonds.

That’s where we stand today, how about you?

Ryan Lewenza, CFA, CMT is a Partner and Portfolio Manager with Turner Investments, and a Senior Vice President, Private Client Group, of Raymond James Ltd.


The Mission

Let’s forget Trump, China, the Fed and the markets for a day. We have bigger questions to deal with. Like: what if a powerful Artificial Intelligence program were used to analyze, say, a pathetic blog? What would it think?

Now that people and computers are connecting in ways never before dreamed of something called ‘sentiment analysis’ is all the rage. And because cheap hormones and fake thinking are what this site is all about, how could it not be a perfect subject?

That was the conclusion of techie phenom Keith Stoute. He’s the founder of Visual Antidote, an IT consulting outfit and software manufacturer with a global clientele. He’s also a blog dog who has just AI’d Greater Fool. Imagine. This is like NASA looking for life on a distant, weird, freaky dead star.

“I am an avid fan of your blog (the term ‘addict’ may be more adept) and also learning technologies related to Artificial Intelligence and Machine Learning,” he writes. “I’ve decided to merge the two, and have had IBM’s Watson technology read one year’s worth of posts (including comments) and provide feedback on what it found.? Surprisingly it did not break!

“To do this I scraped all blogs (365) and comments (50,521) from August 1st 2018 to July 31, 2019, stored them in a database, and passed them to Watson one at a time (50,643 records – you’re busy).? Watson returned a measurement of what it analysed in terms of sentiment (overall positive, or negative?), sadness, joy, anger, fear and disgust. ? The results are pretty fun.

“This type of technology – sentiment analysis – is one of many that is currently changing the way humans and computers can interact, and how our futures will look.? Big organisations like banks and telcos are already using it to monitor their customers interactions in call logs, chatbots and surveys. ? ?There are implications, both positive and negative, for humanity, which makes me excited to learn and apply it (hopefully for good). I had a lot of fun compiling this and I hope your readers get some enjoyment from it too.”

Well, what follows is Stoute’s summary of Watson’s findings. By the way, this is the same program that won a million bucks beating everyone on Jeopardy. But today it’s parsing the steerage section. Put on your safety googles. Here we go.

For each blog and comment we received a score for overall sentiment (positive or negative), joy, anger, fear, disgust and sadness.? What we identified was that blogs with a ‘negative’ sentiment generally seemed to garner the most comments, which could be assumed to be a proxy for engagement.

However, it does seem that being ‘too negative’ drives comments down – see farthest left bubble.

It should also be noted that Guest-Bloggers in general seemed to be more positive than average, and they post on weekends, which might account for some of the lower comment counts on the positive end of the scale.? For example, guest bloggers account for 19 of the 50 most positive blogs, but only 6 of the 50 most negative.? The 10 most positive blogs had an average of 97 comments, while the 10 most negative blogs had an average of 130 comments.

The most commented-on blog ranked number?20 out of 365 total, as measured by fear (0.59797).

The real Insanity: the Comments Section:

The has a lively comments section, filled with a wide variety of characters who opine on and off topic, often without decorum, taste or manners.? This makes for a compelling challenge for Watson. Luckily we did not break it with the insanity it had to endure!? ?Sadly, the most prolific commenter has a pseudonym I will not repeat on my blog, but pertains to something that can occupy a crowded elevator you do not want to be in (how’s that for a Jeopardy question?).

Below is a chart of the top 10 most prolific commenters.

Click to enlarge

If you can believe it, “Mr. Elevator” posted 1,577 comments in 12 months, or an average of 4.3 comments every day.? This is just over double? the amount of the 3rd most prolific commenter.

The regular contributer?‘For those about to Flop’ would have been in the top 10 had he posted consistently under one name, but seems to flop back and forth between his long formal name (463 comments) and ‘flop’ (183 comments). So he is aptly named.

Speaking of Flop, who likes to post regularly about ‘how much’, let’s rank our top comments as evaluated by IBM’s Watson.

Just a brief note about ranking: Watson returns a value between -1 and 1 as a measure of a sentiment or emotion.? So an anger value of 0.9 means you are really angry, and -0.9 means you are not angry AT ALL.

Top Positive Comment
“Thank you so much for your excellent answer to my question! I really enjoy your writing and I look forward to every post. Kind of you to share your knowledge with us. (The pictures are cool too!)” – Ian “Blog Dog”, Nov 26, 2018. Sentiment rank: 0.999947

Top Negative Comment
“Yet another pic of a stupid canine failing the IQ test. (Too bad dog owners are equally of lower intelligence, or they could save their poor dumb puppies from their own stupidity.)” – Felix, Sep 23 2018. Sentiment rank: -0.998314

Top Joyful Comment
“Thank you, Garth. Such wonderful stories. Happy New Year to you and your family.” – Chris, Dec 30 2018.? Joy Rank: 0.991983.

Top Angry Comment
“Enjoy life don’t, worry about the small shit. Gamble bitches.” – Smoking Man, Apr 4 2019. Anger rank: 0.935145

We knew SM would appear here somewhere. Frankly, this does not strike me as all that angry, I like rank #10 as the most angry:

“If the lazy parasites communist CONservatives want t No B-20 that is fine but they should DEMAND the shut down of CMHC. They always hate government interference but since it helps those useless communists they dont say boo about CMHC. Proving CONservatives are nothing but CON artists and SHYSTERS who hate the free market but love communism for the rich.” – Communist CONservatives, May 14, 2019. Anger rank: 0.853132

Top Disgust Comment
“Progressives and their “ideals” sure are revolting and disgusting.” – Classical Liberal Millennial, May 5, 2019. Disgust rank: 0.91043

Top Fear Comment
“oh boy I fear for the comments section today. I’m out.” – yorkville renter, Feb 28 2019. Fear rank: 0.947706

Top Sad Comment
“My Dad did the same as Nance:( He won’t tell me but at 88 I’m sure he’s broke now. Just CPP and OAS to live on. Sad AF!” – Honey Dripper, Sep 13 2019. Sadness rank: 0.965764

How we did it:

IBM Watson is “IBM’s suite of enterprise-ready AI services, applications, and tooling.” The area of AI we are using is known as Natural Language Processing?and Sentiment Analysis. Basically you give a computer system some phrases in a human language like English, and it will return its interpretation of some aspects of it, such as:

  • Overall sentiment – is it positive or negative?
  • Emotions – what emotions are involved, and how strong are they?
  • Entities – what are the topics or categories being discussed?

Organizations use this to process large volumes of data to look for specific trends.? For example, a call centre at a bank could transcribe call logs and monitor realtime if customers are becoming emotional in one direction or another, or?companies can analyze huge volumes of their customer survey results for meaningful insights into how their customers or market is feeling.

To get all this data we built a web scraping tool using Python to retrieve publicly available content from the Greater Fool blog. From there we passed it to Watson’s API and recorded the results in a database for further analysis. From there we did further analysis with SQL, Python and Excel.

We ran our approximately 56,000 records against Watson without tweaking or tailoring Watson to understand the culture, sarcasm or irony prevalent on the Greater Fool blog.?? For example, it deemed this post as overwhemingly happy and positive!

“Happy Housing Crash Everyone! The days of SHYSTER lies are over. I can’t wait for a uber type realtor. Could we see $1000-$5000 to sell a home?? Happy Housing Crash Everyone!:-)”

It reads like a biblical pestilence is being lifted… how could that not be happy?? To be fair Happy Housingcrash Everyone’s average sentiment across 21 comments was negative.

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