Portfolio Review 2017

Saturday, Jan 27 2018 by

Last year was very much a year of transition as I moved from a hybrid high-yield/growth strategy to one focused on quality and momentum. This change was motivated by my increasing confidence in factor based investing (as seen in the Stockopedia StockRank system) and seeing investing friends take profitable advantage of high-quality shares with price/earnings momentum. It's not that I've made terrible returns in the past but I now know that I'm just not a value investor. My record here is patchy, as a result of buying shares that were cheap for a very good reason, and this led to my returns being somewhat volatile.

In addition to changing tack I also took on board the fantastic research done by Stockopedia around profit warnings and my approach since then has been to sell any companies that warn as quickly as possible. This is all about protecting capital and in a similar vein I now employ a 20% stop-loss in order to cut out shares where momentum turns negative; I've seen too many companies reach a peak before relentlessly falling and slashing my overall return. As a caveat this rule is implemented on a case by case basis since I don't necessarily want to drop a company if there really isn't any negative or recent news available.

What surprised me, in putting this review together, is just how many trades it took to achieve this rebalancing; on average more than one a week. For someone who is generally indolent, and believes that this is an excellent attribute for an investor, this level of activity feels like day trading! Anyway what follows are all of my trades and positions, warts and all, ordered by size of gain/loss along with a few comments on the companies that I own where nothing changed (for me) all year.

P.S. There's an obvious pattern to the type of share that I buy and this is because I employ a checklist (which I intend to write about soon). So if my reasons for making a trade start to become repetitive well I apologise in advance but that's the point of a checklist! Feel free to skip over some of my descriptions if this becomes a problem.

Losing trades

LXB Retail Sold at 20.3p after a series of profit warnings - Nov 17 - 45.7% loss


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As per our Terms of Use, Stockopedia is a financial news & data site, discussion forum and content aggregator. Our site should be used for educational & informational purposes only. We do not provide investment advice, recommendations or views as to whether an investment or strategy is suited to the investment needs of a specific individual. You should make your own decisions and seek independent professional advice before doing so. The author may own shares in any companies discussed, all opinions are his/her own & are general/impersonal. Remember: Shares can go down as well as up. Past performance is not a guide to future performance & investors may not get back the amount invested.

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9 Posts on this Thread show/hide all

rhomboid1 27th Jan '18 1 of 9

Great read Damian ..I’m always impressed by how many routes there are to investment success

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Damian Cannon 27th Jan '18 2 of 9

In reply to post #305548

Thanks rhomboid1. Curiously I was thinking much the same thing today while reading a thread on Twitter; there really are many ways to play markets and make money (and what is right for one person can be wrong to another).

However I think that there are certain heuristics which help you to be successful: one is that you need an investment strategy that is congruent with your personal psychology and ability to deal with stress; another is that you need to actually have a strategy and then to stick with it (while continually refining it based on new data).

If you don't have a strategy or you're constantly changing strategy or you're just copying someone else's strategy at a superficial level then I believe that you'll lose money in the long run.


Blog: Ambling Randomly
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gus 1065 28th Jan '18 3 of 9

Hi Damian.

Thanks for sharing such a detailed analysis of your investments. Interesting combination of mixing the Stocko algorithms with some monkey brain consideration of the underlying stock stories. Since subscribing to Stockopedia a few years ago I’ve kind of done something similar migrating from a portfolio of a small number of blue chip names to a much larger set of lower average holding sizes in high stock rank mid and small cap stocks.

One thing I’m currently doing is to trim back some of the more speculative stocks (particularly anything falling into Momentum trap territory) that have made decent money and putting the proceeds back into decent yielding blue chips. (For example £G4M was sold at the end of December). It’s something of a defensive measure in anticipation of a market correction at some point in the short to medium term. For the first time in 30 years of investing I even bought some GlaxoSmithKline (LON:GSK) last week ... must be getting old (or senile). My concern is that as and when markets turn the recent “frothy” successes may fall quickest and furthest and due to illiquidity be difficult to sell.


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Damian Cannon 28th Jan '18 4 of 9

In reply to post #305618

Hi Gus,

Thanks very much. I definitely identify with your shift away from blue chips and I've got Stockopedia to thank for helping me achieve this effectively.

On the monkey brain front I remember a post (possibly from Ed) which indicated that when you have a decent algorithm/checklist then adding in a human element only ever reduces the return below that which you would have got without any tinkering. I honestly don't know if this always holds true, over the long term, but I do wonder how much value I'm adding when you look at the NAPS portfolios.

Anyway I enjoy the process and to get an idea whether I am adding much value I've started writing notes on my buys/sells and on announcements that add new trading information. This review is really a way for me to draw a line in the sand for 2018 and force me to be a little more attentive to my holdings.

As for the markets turning and blue chips providing a safe haven well I don't know - pretty much all of my shares got marked down back in 2008/09 despite my mistaken belief in their solidity! I do agree on the liquidity front though; I'm reluctant to get involved below £100m these days due to the difficulties of trading that can occur and I do keep an eye on the nms to make sure that it doesn't equate to a stupidly low amount.


Blog: Ambling Randomly
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timarr 28th Jan '18 5 of 9

In reply to post #305688

When markets crash generally blue chips get hit first as they're the most liquid - in a full scale bear market institutions need to raise cash and that's the fastest way of doing so. Sometimes when this happens small cap stocks don't fall as fast, but spreads often widen significantly, as no one wants small, illiquid stocks on their books.

Basically, in a proper crash you get a liquidity crisis, which means that perfectly good companies get hit with the bad. At this point you really need to know how to tell the difference: those with good fundamentals will eventually recover, those without probably won't. As Buffett says, you only find out who's swimming naked when the tide goes out.

On the monkey brain thing there's limited evidence that a small number of people can actually outperform algorithms - it's the same principle that there are a few people who can improve the accuracy of weather forecasts. However, those that do improve only do so because they get frequent feedback on their performance.

In general most of us would probably do better if we followed an algorithmic approach. My concern about that is that there's no algorithm for all weathers - the processes that have worked very well over the past 8 years or so will probably fail catastrophically in the next downturn ...


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gus 1065 28th Jan '18 6 of 9

In reply to post #305698

Hi Tim.

On your first point, I suspect in a real crash you simply won’t get a bid on the small, illiquid stocks. Screen prices will be reference only. At least being in blue chips there should be an executable bid at a reasonable spread for those that have to trade. (Although personally I don’t use leverage on equity holdings).

On your second point, I agree the perfect algorithm will invariably out perform the monkey brained human. The problem is, as you say, the perfect algorithm is probably dynamic according to the prevailing market conditions and relies on the monkey brain to select and implement the optimal choice which becomes a bit of a Catch 22.


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Damian Cannon 28th Jan '18 7 of 9

In reply to post #305698

All fair points timarr. I think that getting and acting on feedback is a very good point. In the past I haven't explicitly gone looking for evidence; partly because I didn't have records on why I'd made a purchase and partly because when a share is sold it's almost as if it never existed (from a portfolio perspective). All that I really gained from this activity was a loose set of heuristics around which types of shares/situations didn't work for me.

Hence my decision to start recording this information and reflect on why I'm holding each and every one of my positions. Hopefully I can feed this data back into any new trades and so on.


Blog: Ambling Randomly
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JT3653 7th Feb '18 8 of 9

Hi Damian
Thanks for sharing details of your gains and losses and the reasons for your initial purchases. You give the % gain/loss but, in most cases, don't say how long you held the stock for. As I see it, the annualised % is the real determinant of how successful an investment/trade was.

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Damian Cannon 7th Feb '18 9 of 9

In reply to post #311608

Hi Jonathan,

That's a fair point. To be honest it took me so long to pull together all of the numbers as it was I didn't really feel like dealing with all of the trades that went into each position to work out the performance of each tranche! My other reason not wishing to extract this detail is that most of my sales were in high-yield shares, which is no longer my strategy, and as a result there's no great benefit for me in learning how good (or otherwise) they were as capital investments.

Still I hope that you liked the rest of the article!

Blog: Ambling Randomly
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