Online Stock Trading

One of the few “regulars” on this blog asked me a question in regards to the recent stock market analysis I did. The essential question was:

“Could you say something about the process of collecting and analyzing the data? Where did you get it, what software did you use, how long did it take you to do this analysis etc.?”

To elaborate on the brief explanation outlined in this article about the Very Large Dataset Project, I made a fairly complex Visual Basic for Applications program in Microsoft Excel that would “scrape” information for each ticker symbol and place it into a spreadsheet. After having done this scrape periodically, I had a sizeable quantity of data from which to work with. To do the actual analysis, I spent several days arranging the data in different ways, sorting different fundamental stock statistics according to their yields and making pictures of the graphs that summarized the different things I thought were useful.

If there is enough interest in the Excel code, I may have the time to publish that on this blog for its readers, but it’s not an “out of the box” kind of application—it would require some tutorial to use. The actual gathering of the data takes many hours, and has to be run in smaller pieces (I run 8 separate files, each with about 1000 ticker symbols) because the program slows down considerably when trying to pull all the data all at once (in other words, as the spreadsheet grows in size, it also runs less quickly).

Why’s that you say? Primarily, because you never know what kind of admirers you might find along the way. Interesting people perhaps. People with an eye for suspicious matters willing to pursue minor investigations into the depths of other interesting people. When you see such things, as I sometimes do, you think that places like say, Brooklyn, suddenly might have an appeal all their own. Not that people from there could really be that different after all, not that their suspicions are unusual, or even unjust—to the contrary, their suspicions would be welcomed with open arms. One possible scenario might look just like this: Let’s say you were in possession of a scope much wider than most people would comprehend on first sight. Then, to allow a few particular special people to glimpse that wider scope you could make available to everyone access to that wider scope and put the access right under their noses. But, you could conceal it just enough, and in just the right ways so as to inadvertently prevent unwelcomed company. Anyone who made it through the net would have had to have done some leg work. I thank you.

In other news, it looks like Russell from Hawaii finally got his Starbucks card. Does first class to Hawaii go by boat? I mailed it on December 23rd for crying out loud. I’m certain I sent it first class. Enjoy Russell.

Also, I’ve finished reading the Bob Woodward book on Alan Greenspan and am writing a short review of the book; it’ll be up soon. Lastly, I’ve got some new analyses in the works as well, stay tuned…

This article is part III of a three-part series.

Here is Part I

Here is Part II

Now comes the really fun part. Now we’re going to take all the information we have and translate it into something useful: now we’re going to pick some stocks. I think it’s important to keep in mind what it is we’re doing here. We’re surveying a market where many of the normal rules don’t apply, and by examining it, we’re trying to answer this one question: which rules do apply? We’re divorcing our instincts and wearing our scientists’ uniforms right now. So far, we’ve been looking at what the market has actually been doing, not what we think it should be doing, but scientifically, what it’s really been up to.

After having looked at various graphs which show us what the market is doing, or more precisely, how fundamental stock metrics vary across the various yields the market has had, let’s summarize the data a slightly different way:

What I’ve done here is split up all the useable stock yields into “groups”, each representing 10% of the market. Group 1 is the 10% worst performing stocks, group 10 is the stocks in the top 10%. The various statistics (I’ve used 11 for sake of simplicity) are the average values for those stocks in each respective group. For instance, the average yield of the top 10% of stocks was 40.04% since the credit crunch, and they also have an average market cap of 8.9 billion dollars.

Now then, just because the numbers there are the averages doesn’t mean any stocks actually fit those criteria. For that we’re going to have to test them to find out. Below the groups I’ve set up initial ranges we can use to test with (initially I picked plus or minus 10%). We’re going to use this to do some rigorous back testing. Next, I’ve polled the July dataset to see if any stocks actually met the criteria. What we’re doing is, after having looked at the various groupings of yields, and seen which statistics can be associated with the highest yields, we’re reversing course and going about it the other way now. Just because the average statistics for the various buckets are what they are, doesn’t necessarily mean anything.

It turns out that no stocks met all 11 traits in the ranges we set up (+/- 10%) for group 10. In fact, with the tight ranges we picked, the most number of traits that any stock met was only four. In other words, no stock out there had more than 4 fundamental statistics which fell within plus or minus 10% of the averages of top 10% performing stocks. Six stocks made it:

On balance, these stocks have gained 9.23% since the end of July. Not too bad. Considering that the S & P 500 has lost over three percent:

Now what we can do is relax the tight ranges we set up initially, to see if we can get some stocks that have met more than 4 of the statistical averages from the 10% best performing stocks. If we relax the range to +/- 15% of the averages for the best performing stocks, we get 4 stocks that now meet 5 of the traits, and many more that meet only 4:

The average yield for those 4 stocks is still very high. Let’s relax again, this time to +/- 20%:

While we don’t get any stocks that have more than 5 traits within 20% of the average of the best “grouping” we have more stocks that have those 5, 12 now. What’s good about this is that the diversity gained reduces risk, and, even so, the average yield went up significantly. Now the average yield is almost 12% in the worst 5 months we’ve had in a long while. Outstanding. In theory, we could have placed 8% stop loss limits on all of them and not endured the losses of 3 of the 5 that have fallen. Now that we’ve done all this “past looking” let’s look to the future. It will be interesting to see if any of this will be validated going forward.

What I’ve done is taken historical data (July through December), took averages of that data, then picked stocks that met the averages of that data. Now let’s do some similar screening for the stock data as it was at the end of December, and see what happens. The good thing is that we can have some initial results immediately (there was a lag between my collecting and analyzing the data and doing all this writing). Let’s look at how our ranges are now:

When we take these ranges and apply them to the market data from December 22nd, we actually get 1 stock that meets 6 of the traits, and 13 stocks that meet 5:

Now, I’ve taken these stocks at their December 22nd prices and put them in a Yahoo portfolio so we can track them on the fly:

It’s definitely holding its ground. In the same timeframe the S & P 500 has lost 1.08%, whereas ours have lost 0.79%, so they’re outperforming the S & P 500 by 0.29% in just a week. You can come visit this portfolio picture as often as you’d like to see how it performs (I’ll update the picture every evening).

For those who want to download the graphs I used from Parts I & II you can get them in an Excel file here, or as the actual picture files here.

This article is part II of a three-part series.

Here is Part I

Now let’s move on to the flip side. For the next ten graphs, the x-axes will be the same, yields small to great. The graphs will show what the fundamental metrics are doing as yields are going up. In the upcoming and final part of this article, we’re going to try to capture what these next graphs are showing us about what the market is doing right now, so we can see if what’s happening can be captured by particular stock selections. Also, as stated in the beginning of Part I, these next graphs use a greater smoothing factor which gives us the advantage of actually being able to see trends where there are trends, but at the loss of some precision (the trends we will see will be subjects of much greater subsets).

Back to market cap. Previously when we looked at market cap, we looked at what the yields did over the various market caps; this time we’re going to look at what the market caps do over the various yields. Same thing, different view, different smooth:

It should be clear, bigger companies indubitably performed better these last five months. There is a very heavy bias toward larger market caps and where they lie among the yields. The extreme right (best performing stocks) had an average market cap of about thirteen billion.

If we look again at trailing P/Es, we can see some interesting things:

The trailing PEs (as they were at the end of July) seem to indicate that those with the highest PEs definitely fall along the worst yields—but only to a point. The lowest PEs did not perform the best, just not the worst; in fact PEs of around 26 were both the average best performers and also sit in the middle using this smooth. Picking the very lowest PEs won’t guarantee us the best possible performance. The very best performers had average PEs of about 25-26.

Next, let’s look at what the price to earnings growth ratio does over the various yields:

The lowest PEG ratios do both the worst and the best, though the very worst performers had lower PEG ratios than the very best performers. The very best performers had average PEG ratios of about 1.81, the very worst ranged between that and 1.70 (or even less).

Next let’s look at price to sales:

Here we can see a very clear tend. Surprisingly, the very best performers had the most expensive P/S ratios, the trend here is very clear. As yields go up, so do the average P/S ratios of those stocks with the higher yields. The aversion to risk is no doubt causing this effect. The very extreme best performing one thousand stocks had price to sales ratios of about 2.8.

Now take a look at how price per book values ranged over the various yields:

This one is even more tasty. What we saw with price to sales is completely confirmed. The stocks where you buy less book value for every dollar spent performed the best. The very best performing 1000 stocks had price to book ratios in excess of 4.50.

Now let’s look at how profit margins do over the yields:

Here, while we can’t say with certainty which profit margins are associated with the greatest yields, we can see which ones aren’t: we would want to average out in excess of 13.2%.

Cash per share looks rather peculiar:

These variations would seem to be caused by some extremely large companies who are very cash heavy. Surprisingly, the very best performing stocks had more than 30% less cash per share on average than the very worst performing stocks. The best performers had cash per share in the neighborhood of about six and a half dollars.

To avoid the difficulties in finding relevance for cash per share outright (because we can’t know if the value is caused by the cash or the number of shares) here’s that quirky statistic I made, price per cash per share:

While we can’t tell much with the cash per share by itself, we can by using this, we can know how expensive that cash per share is, we can then make the cash per share relative to all other stocks. In this case, we can see that the relation of a stock’s price to its quantity of cash per share is very interesting. The stocks where we are paying less for their cash per share, were the clearly best performing stocks, these were ones where we paid (gulp) about 220 times their cash, for their stock.

Debt to equity next is another very clear trend:

The very best performing stocks had definitively lower debt in relation to their equity. The graph almost has an inflection it’s so clear. Highly leveraged companies have not been treated well in this leverage-averse market.

The last one here is the current ratio again:

To be honest with you, I can’t quite get my head around it. The companies who would seem to be able to pay their immediate bills for less time were the best performers. All I can think of is that larger companies just don’t keep that much cash around, they keep what they need to. Any thoughts on this?

This article is part II of a three-part series.

Here is Part III

I should warn you in advance that this article is both long and tedious. However, through that length and tedium you will find that it also offers a rigorous education for those willing to step into the deep. The article includes a total of twenty-one graphs which illustrate various stock metrics. At the end of the article there is a link to a “.zip” file from where you can download all of them if you want to examine them on your own. Also take note, all the pictures are designed with code so that they will open in a new tab when clicked (so you can see larger, clearer images), there’s no need to right click on them to keep the page you’re on.

After sifting through data from every single stock listed on the NYSE, NASDAQ & AMEX exchanges from the end of July until the end of December, I can decidedly endorse what we already know, namely that the stock market has performed poorly. Even so, there are, upon an exhaustive examination, some clear trends which may be extremely valuable when looking for the best possible investments under less than best conditions.

The primary merit of this examination is its timeframe: July 20th through December 22nd. Why? Because in the long run the stock market is “on balance” normal, meaning that good stocks with strong fundamentals which are undervalued will perform better than the market as a whole. But in the short run they can get annihilated. Short runs like credit crunches. This comprehensive analysis will examine not how the market does over longer periods, with “normal” conditions, but rather, these short and awful last few months.

Don’t let the fact that between the end of July and the end of December the S & P 500 has only lost 3.24% of its value fool you into thinking that things aren’t so bad. Those are some of our best 500 companies. Between all the exchanges, all the stocks, they’ve lost on average 9.85%. That’s pretty bad. So let’s get started.

If we look at the yield distribution linearly, we can see the spread immediately:

There is a clear steepness for the positive territory in contrast to the large flat slope in the negative territory. Indeed, over 70% have fallen in value. The bias is clear. If we look at the yields of all stocks as a frequency distribution, we see the same information in a different way:

We have very shallow frequencies of stocks which have gained followed by much higher frequencies of stocks in negative territory. In a perfectly balanced, neutral market, this would resemble a bell curve distribution. This shows the same heavy bias towards losing yields. The next nine graphs will show relationships of fundamental metrics as primary catalysts, with the resulting yields over the ranges of those metrics. These will also be represented using a short smooth (200-stock smooth) to extract greater precision at the loss of functionality. The ten after those will be many of the same metrics, only inverted (primary catalyst will be yield, with the fundamental metrics being measured on the y-axes instead) and will use a much larger smooth (1000-stock smooth) which will lose some precision but will achieve clearer trends where there are trends, which is ultimately what we’re after. Also, keep in mind that the fundamentals here are based off July 20th data. In essence, we’re going back in time to see what metrics then performed in the ways they have since.

Looking at market cap, we can see a clear trend:

This is the S & P 500 issue we talked about a moment ago. There is a very clear bias for larger market caps. The biggest companies have fared the best. However, “on balance” no subset would seem to have guaranteed gains, except the very extreme right, the very, very largest companies have, on average, squeaked out mild gains. On the other end, the smallest market caps have been annihilated.

Ok, so this is a credit crunch, correct? Let’s look at current ratios. We would expect that the companies with the largest quick ratios (those who can pay their immediate bills for longer) would have fared better. But this isn’t true:

As the ratios go up, the average yields do definitively tend down (we’ll return to current ratio later during the second set—when we do, this will be confirmed).

Still on the credit issue, let’s look at cash per share:

Here there is a clear bias towards greater cash per share: those with cash per share of 3-5 dollars fared best, but on balance still had negative gains. This metric is difficult because it is hard to know whether more cash per share is a result of simply a company having more cash generally, or just fewer shares. In the next section I introduce a rather quirky statistic (price per cash per share) which intends to test for the “expensiveness” of the amount of relative cash per share a company has.

Next, let’s look at debt to equity:

This one’s interesting. At this level of smoothing, the yields are sporadic through all ranges with the exception of the companies with the worst (greatest) debt to equity ratios. The very worst debt to equity ratios fared the very worst with the rest faring approximately equally.

Let’s next look at some valuations. The following is book value:

This metric seems to confirm the market cap analysis but has the same dilemma as the cash per share we looked at. Later, we’ll look at price per book to dissect the arbitrary dimension of book value outright versus number of shares outright.

This next one is really interesting. This is the 52-week changes of all stocks related to yields:

Look at the left extreme (x-axis). Those are stocks that, as of the end of July, already lost 30%. Since then though, they’ve since lost another 30% or so. Ouch. I guess this confirms what Danny and Bob already know, that stocks doing well will continue to do well, and those doing poorly, well, can get slaughtered.

To see another example of why valuation outright matters nil in this market, let’s look at price to earnings ratio:

Under normal circumstances, we want to find good companies with relatively low PE ratios, meaning we’re buying more earnings with our money and less expectation of future earnings. In this market though, you can pitch that. There is a clear bias toward higher PEs, but not too high—the very highest PEs get slaughtered. We can assume that the extreme highest PEs lost all their speculative punch through risk aversion.

Earlier we looked at book value but didn’t see what we would have expected (namely because book outright may not be relative enough to be useful). Now let’s look at price-per-book to see if this as a valuation is more meaningful:

This would seem to confirm what we found with price-to-earnings: throw valuation out right now. The more expensive stocks in terms of how much book value you are buying per share actually clearly did better, in fact, the “best” valued ones (ones where you’re buying much more book for every dollar you spend) did terribly.

This article is part I of a three-part series.

Here is Part II

Here is Part III

After burning up one computer, transferring data to another, then having to upgrade that new one to handle the workload of the Very Large Dataset Project, I’ll be finally posting some results again. In addition to the computer upgrades, I had to create new code that would correct share price values from the past to automatically account for the effects of stock splits. Now it makes two passes, one Internet scrape for the fundamental data, another to correct pricing. The good news is that now it’s really, really cool.

The first analysis I’ve done since the upgrades is on dividend stocks (and funds). Because the market has been waffling and otherwise performing badly, many prudent people no doubt consider dividends to be means of offsetting some of those ugly falling and volatile stock prices. But, no one wants to buy a dividend stock, watch it sink 15% in 3 weeks and gleefully exclaim, “it has a 3% dividend!” So which dividend stocks have done well? In poor economic times, when companies’ profits are somewhat uncertain, many companies lower their dividend to reserve cash. Which companies (or funds) have held their dividend the same, or even better, who’s raised their dividend? Because risk is relatively high right now, and stocks could fall more, which would make a 2% dividend seem almost silly, who’s got a more meaningful yield (say, better than 5%)? Also, which stocks have done well, recently, during the time just since the credit crunch over the summer? The Dow peaked on July 19th, softened substantially, eventually peaked again on October 9th (but by then the “credit crunch” was front-page news) and has since been pretty lousy. The data I’m using is from the time period from July 20th to December 21st.

Good luck finding all that with a stock screener .

So here we have it. The following is a list of 91 stocks (or funds)—sorted by share price gain—who:

1. Have share prices that have gone up,
2. Have dividends that have either stayed the same (annual trailing) or have even gone up,
3. Have current dividend yields of greater than 5%, and
4. Have done all this since July 20th.

A quick note: several of the stocks/funds whose dividends have gone up have what appear to be one-time, non-regular dividends (in other words, their trailing yoy went up because of this reason—so keep your eyes open for these). If you would like to download this table in Excel, click here.

When mixing the old with the new, one better be vary careful. Careful why? Because whatever hope one might have in preserving the best qualities of the old can be ruined with the introduction of the new. Care would seem to be something that was taken, when Kashmirwas covered by Bond.So much care in fact, that it (in my ignorant opinion) actually improved significantly on Led Zeppelin’s terrific version. Meanwhile, this utterly fantastic piece of music was born.

In life, according to my view of things, great things only come in two ways, either by accident or with the intent of a human will, and the latter are inherently always at least a bit better than the prior. So goes my human bias. But those greatest things, or those best of the great things, then, must, never be accidents. But because the greatest tragedies also have that very same origin, we have to keep our eyes open for all the ways where we can forget the last and always manage to do the first.

Such accuracy, so it also seems to me, requires, in the very least, proper tools and proper weapons. What such weapons could these be, these things from which great things construct the face of the earth, and walk the streets of her cities? Surely we can answer that, sitting here on our perch on history—we know they once, long ago, never quite got things perfectly right, and even more obvious are the many modern ills, so what of tools composed from both? How about both old and new?

…hopefully a hell of a lot warmer than I am right now . In fact, I recommend that this individual buy a couple Frappuccinoes instead of a hot mug. Russell from Hawaii is the winner. Congratulations Russell. Russell may contact me using the “contact me” link on the left so I can verify his identity and get an address. Also, Russell, once you receive the card, I would be grateful if you would leave a comment to this post so others will know that you’re human and that you did in fact get it.

For anyone willing to toss in their two cents to someone willing to receive them, I would love to get some feedback or other ideas on how to do more promotions like this one. I wonder if using coffee is too constrictive, or if the amount is too petty, or possibly other things as well. If anyone has some suggestions about how I could make these more successful (in terms of marketing potential), my ears are open.

So stocks suck right now. Down and down they go, when they will stop no one knows. How does one react to such forces? I don’t know about you, but clearly, as you can see quite plainly, updated for you daily during the week and at least once a weekend, my portfolio has long since passed the bloody nose phase—now it’s lost arms and legs. But much like the knight from Monty Python, it still has…um…teeth? Ok, so I’m not so sure about that but I do know one thing for certain, and that’s what I have. Cash. Having patiently squirreled more and more away, I plan to enter a mother load buying behavior phase. Soon, I will be buying, yes, all U.S. assets. All securitized equity. Maybe even Canada’s.

Assuming the market will recover next year (and tentatively I’m leaning toward that—meaning that with all available information so far, that’s what I’m thinking, but what’s available changes every day), and assuming I can play this pig all the way down, It should be some fun (to say the least) to ride it back up.

Also, I’m definitely developing a bias in favor of stocks with dividends. I’m writing up a lengthy analysis of dividend stocks and their performance over the last few months, but my Internet scrape had a couple failures this weekend and consequently took too long (and the way the programming is set up right now, I can’t run it during the week), so come back often, especially this weekend for that post.

So now where did those arms go…?

I’d like to think that somewhere in Mountain View, California, somewhere near Google, some weary eyed and exhausted computer technician is just finally putting his tools away after having been called in for a special emergency: multiple hard drive failures in one of their Blogger servers. Of course this is a bit exaggerated, but my mere imaginings are the small price I’d like to pay myself after the (relatively) spectacular traffic gush spanning Thursday night and Friday morning. It wasn’t a thumping quite like I’ve heard Digg can be, but it was about a 1,000 new visitors on a blog that up until then had just over 6,000 ever. Here’s an updated graphical look at the magnitude of traffic versus the days prior to the StumbleUpon foray:

Of course those magnanimous results are over with, but hopefully I befriended some new readers.

For those new readers, I’d like to identify a recap of some of my aspirations for this blog, to introduce new readers to what I consider to be some of its many merits:

The question is often asked in the world whether smart people are more successful than more mainstream people. Studies often prove that they are not necessarily “better off” in wealth, happiness, etc. While this is good news for the most mainstream people, it can be a source of soreness for all the smarties. If they don’t fit in perfectly well with the larger crowd, where can they go cozy up? Here, for one.

Why here? I hope because here I often try to take a different approach to old problems. I try to do that by looking at the picture in front of me through various lenses. In other words, I take a much longer time thinking about the things I choose to write about than you’ll likely find in many other resources, like television for instance.

That doesn’t mean that what you might find on television is incorrect. For example, pretend you were playing chess and there were 2 reasonable moves you could make. With no thought involved whatsoever you have a pretty good chance of picking the best one. But, if you really exercised your intellect you could more frequently pick that right one. The stock market is similar to this. You could, with no effort whatsoever, pick winning stocks or make wise investments, but to make those selections consistently you’d have to have some serious skill.

Another merit of this blog is also quite possibly its greatest detractor—its utter transparency and honesty. Find me another blog where a stock investor openly flaunts all successes and all failures. I’ve found several but sometimes you really have to go digging. By prominently displaying my performance (every single day) I hope to offer two things: 1) a place where you can come back to see how you’re doing in comparison to me and 2) to show, eventually, that even given serious downturns in the market, many of my strategies will significantly outperform the market overall. Of course, these last few months have been somewhat painful, but I’m committed to a long-term focus. Just wait, that damned chart will go back up .