Trading Tips

Comparing Your Trading to the Alternatives

I wrote the original version of this for the Currensee blog, but it has a broad based focus, so I wanted to post it here as well.

A thread was begun by a member of the BabyPips community on the subject of measuring and comparing trading system performance. The author had earlier initiated a discussion as to whether active portfolio management (in this case specifically talking about forex trading systems, but the same ideas apply across markets and methods) was of any value given the Efficient Market and Random Walk premises. That latter subject matter predictably generated a rather intense debate. I won’t take that up here, but I do want to discuss the upshot of it. Forum members wanted to know on what basis a system could be judged as to whether it was better than a passive approach. The performance measurement thread took that up.

Here’s the premise.

Performance of any active approach to taking on the markets must be measured against performance of a passive approach. I did a hatchet job on the original poster’s primary recommended metric because it was mathematically flawed, but his overall idea is legitimate. If you’re going to actively play the markets, then it needs to make sense doing so.

Volatility of Performance
Now, this isn’t quite so simple as comparing your own trading returns to that of the S&P 500, or sets of system returns against each other. This is where “risk-adjusted” comes in. The various markets have different levels of volatility (see Looking at Volatility Across Markets), and the same can be said of trading and investing methods. Volatility is the standard measure of risk, so we need to incorporate that into our comparative analysis.

How you measure volatility varies. In academia it’s common to measure the variation of period returns over time. That tends to focus on the consistency of performance. You could, however, use a measure of the size and/or length of drawdowns, which more focuses on the impact of adverse periods. There are other metrics as well. The important thing is identifying the one that makes the most sense for your objectives.

With a metric in place, you can then assess the performance of different approaches to the market on a risk-adjusted return basis. That would let you know that System A, with a 15% annualized average return and a 7% average drawdown, is probably better than System B, with its 16% annualized return and a 10% average drawdown. And then you can look at where System A falls within the sweep of potential uses of your money which runs from low return/low risk (like T-Bills) to high return/high risk (like penny stocks).

The Cost of Time
Assessing a given approach to employing your money is more than just looking at risk-adjusted returns, though. You must also account for the amount of time and effort you put into the process. For something like sticking your money in CDs or investing in an index fund, the time element will be small. For an active day trading strategy the time element is going to be high.

For that reason, it’s worth having a separate metric for looking at this time element. A simple $/hr calculation will suffice. Once you’ve figured out the hourly return of your trading/investment activities, you’ve got another basis for comparison – and for looking at the best application of your time overall.

But beware that the time element of trading/investing cannot just be viewed in cost terms because things like entertainment and education value come in to play. For example, when I first started coaching volleyball I calculated what my hourly rate was when factoring in all the time I was putting in to it. The result was below $1/hr. It bothered me not one bit, however, because I enjoyed the work and was developing myself as a coach such that I could increase my effective hourly rate moving forward. This is a particularly important consideration for new traders – otherwise no one would ever even think about getting into trading!

To Go Active or Passive
The bottom line here is that you need to look at whether being an active trader or investor makes sense in terms of risk-adjusted returns and the amount of time you have to put in to it all. If it’s not, then you’re going to want to look in to a passive approach.

The Basics

Influences on Price/Earnings Ratios

The Price/Earnings ratio (P/E) is a metric commonly used in fundamental analysis of stocks – both individually and in terms of indices. It can be a useful gauge of relative over- or under-valuation both in terms of looking at a stock or index singularly, or in comparison with others. For example, one could evaluate where the current P/E of JPM is in terms of it’s historical levels and/or in terms of how it compares to BAC, C, WFC, and others in the banking sector.

It’s not recommended that P/E be used in isolation – meaning low P/E = cheap stock, or vice versa. There are reasons why a P/E can be low or high, including changing expectations for growth rates which have not necessarily started showing up in the earning data. For that reason, you should only use the P/E in conjunction with other forms of analysis.

Looking at Stock Valuation Math
In thinking about doing so, it’s worth noting the two mathematical influences on the P/E ratio when looking at the valuation of a stock. They are the earnings growth rate and the interest rate. Stock valuations are done by determining what future earnings are expected to be, then discounting them back to the present by doing a Present Value (PV) calculation.

Earnings growth rate assumptions obviously factor into the expectations for future annual earnings per share figures. The table below shows the impact of different levels of growth rate expectations for earnings on valuation, and thus P/E.

The above calculations only go out to 5 years. Valuations are often done with an additional perpetual growth rate for the years beyond #5. For the purposes here, however, five years is enough to make the point.

Notice in the yellow Value row how the valuation of the stock in question (based on adding the PVs of the earnings forecasts for Years 1 through 5) rises as the assumed annual growth rate (left column) goes from 0% up to 20%. Using the Year 0 earnings (current year achieved result) as the E in the P/E, and the valuation as the P, we get the P/E listed in the right-most column. Notice how it rises in line with rising growth rates.

Now, this probably won’t come as a big surprise. It’s commonly understood that higher earnings growth rates translate to higher P/Es. That’s why the P/E of a perceived growth stock will generally be higher than the P/E of a more mature stock, like a utility. It also should be noted, however, that P/Es also vary because of interest rates. The discounting of future earning’s done in the valuation process employs an interest rate to calculate the PVs. Thus, interest rates impact P/Es.

The chart below provides an example.

The chart above shows the P/E value of a stock with a 5% annualized earnings growth rate with valuations determined using discount rates from 1% to 10th. Notice the steady decline as interest rates rise. It’s not a big change, of course. Changes in earnings growth rates are more impactful. This may be something very important for the stock market moving forward, however. If we think interest rates are going to be rising in the years ahead, then we have to factor in slightly lower P/E ratios.

The Basics

Positive Points, Negative Profits

A subject of conversation in the blogosphere in recent weeks has been the idea that you can have a positive point or pip (I’ll use points for simplicity from now on) balance from your trading, but end up with a negative net P&L. This may sound like an impossibility, but I assure you, it isn’t. Let me provide an example.

Let’s say you do 10 trades. You have a 60% win rate and make 50 points on average for those winners while suffering 30 point losses on the other 40% of trades. That tallies up to a total gain of 120 points (6 x 40 – 4 x 30). This looks like a pretty good result, right?

The problem with the above is that it assumes all of the trades are the same size, the same value per point gained or lost. As soon as you start including trades of different sizes you cause problems with using point accounting to gauge performance. All I have to do is change the size of one of those losers to make something that looks quite positive into something with a negative bottom line.

Let’s say for 90% of your trades each point is worth $10, all of the winners and three of the losers. That translates into a net profit of $1500 (6 x 40 x $10 – 3 x 30 x $10). If, however, you had a really good feeling about the last trade and put on a position ten times the size you normally traded ($100/pt), then the 30 point loss for that final position would wipe out all the net gains from the other 9 trades and end up $1500 down ($1500 – $100 x 30pts). Suddenly the total point gain figure doesn’t seem so good anymore, does it?

You may be thinking you’d never trade 10 times your normal size, but that’s not really the point. You can create any number of scenarios in which the point tally is positive and the actually profits are negative or break-even or decidedly unimpressive because of variation in position size between trades (or just as easily the other way around where it results in much more impressive performance than the point tally suggests). That means unless you always trade the exact same point value, counting points doesn’t tell the real story of your trading.

This is exactly the reason why I’ve been a proponent of using other measure to gauge performance. Obviously, % return is a good one, though that doesn’t factor in risk. Using R is a good way to incorporate risk into comparative performance measurement. Whatever you use, though, just make sure to be aware of both its benefits and short-comings.

Trading News

Retail Forex Trader Profitability and the Death of US Forex Trading

I initially wrote this for the Currensee blog on October 18th. I’m cross posting it here because I think it will be of considerable interest.

This is the first week under the new CFTC rules restricting leverage for holders of US retail forex trading accounts to 50:1 for the major trading pairs and 20:1 for minor ones (see Asaf’s post and an earlier one of mine on the subject). Obviously, there are implications for certain traders because of the change (probably not the vast majority, though), but one of the more interesting aspects of it all is the reporting the brokers must now do regarding the performance of their brokerage customers. They now have to disclose to new account holders the % of customers who have made and lost money. Forex Magnates has gotten hold of these reports for most of the brokers servicing the US customer base and presented the information from them here.

The common mantra in retail forex trading is that 95% of all traders fail. Of course we don’t really know what ‘fail’ means or over what time frame this is meant to cover. The figures from the brokers are equally subject to some “Yeah, but” type questioning. According to the Q3 figures, about 25% of brokerage customers are profitable, if you don’t include Oanda.

The problem we have here is that we really don’t know what these numbers mean in terms of long-run trader profitability. The % profitable figure is very likely to demonstrate a survivor bias whereby traders who crash and burn will eventually fall out of the study, as the reporting only includes accounts where trades have actually been made. Obviously, if you’ve lost all your money or become so disheartened by poor performance that you stop trading all together, you’re not going to be counted.

Then we have the question of Oanda, which shows WAY better customer profitability than the others. Are they using a different calculation methodology? Does the fact that they pay interest on your margin balance influence the reporting? I ask because an account that does no trades but still has a balance will end the quarter profitable because of the interest earned. I don’t know if those daily interest payments are transactions which make an account “active” or not. I’d love to hear from someone at Oanda whether that’s the case. If not, then we have a very significant question as to what makes Oanda customers more profitable. Is it somehow a function of the 50:1 leverage they’ve always had? If so, it starts to make the CFTC decision look a lot better.

The demise of US retail forex trading
The other thing we can look at in these reports is the actual number of active customer accounts each broker has. Folks have been howling about the pending destruction of the US forex business every since the NFA came through with its FIFO and no-hedging rules last year. The broker reports don’t go back that far, so we can’t see what impact was had where folks shipping their accounts overseas might have had, but since many of those accounts are now coming back, and will thus be included in the broker Q4 numbers we may get some idea.

We can perhaps get an idea what the CFTC leverage restrictions may have done to US broker accounts, though. The initial proposal of a 10:1 leverage limit hit the markets at the start of this year, with the announcement of the final 50:1 cap coming in August. The table below outlines the impact.

Notice that in the first quarter of the year there was a 5% reduction in active broker accounts. Thereafter, though, the decline has only been 1% in each of the last two quarters. I’m reluctant to call that any kind of major problem, and it will be very interesting to see if the forced-repatriation of accounts from foreign lands that is happening will actually result in a positive impact on the numbers for this quarter, especially for those brokers who have had the biggest drop in US accounts.

Again, we see Oanda as a major outlier. Rather than being about flat to lower in terms of customer accounts, it has seen a 20% rise in the last year. Considering Oanda does not do any marketing and has only every allowed a maximum 50:1 leverage, these are quite interesting figures. It leaves one to wonder if that reflects the fact that Oanda has no fixed lots, and thus allows very low capitalization customers to take part in the market without having to trade with very high leverage ratios. That’s just speculation at this point, though. We may never really know.

The point is that we probably haven’t seen the end for the US forex business, despite the doomsayers. We’ll want to wait to see the Q4 2010 figures for a better reading on customer accounts, though, because of those who would have moved accounts offshore away from CFTC oversight and those brought back from broker foreign affiliates.

Trading News

Trader Performance May Not Be as Bad as They Say

Here’s an interesting forex broker table posted at Forex Magnates. It lays out the % of profitable traders among its customers.

There’s considerable talk among retail traders that 95% of those who enter the forex market crash and burn. The figures above, which show the % of customers who have made money vs. lost money in a given quarter, would seem to suggest the 95% is an exaggeration. I’ve always felt that was likely the case, but keep a couple things in mind when looking at the information above.

First, % unprofitable, I believe, includes break-even accounts. That means folks who didn’t do any trading at all during the quarter would be counted as unprofitable. (Clarification: The broker has indicated that inactive accounts during the quarter were not included.)

Second, we have no indication of how profitable or unprofitable these traders are based on these figures. I bring this up because it could be the case that the profitable folks are only just and the unprofitable ones are very, or vice versa. We have no way of knowing.

Third, I don’t know how closed or blown-up accounts are accounted for here. One of the issues with performance metrics is the survivor bias. Is the % profitable simply a reflection of the fact that the unprofitable traders are churned out of the count?

Here’s something else worth considering. According to numbers I’ve seen from among live trading performance of members of one social trading network, 60% of all trades were profitable. They’re % of winning traders was comparable to what’s show in the table above. What’s the conclusion? Something I’ve said many times. Win % is not what determines trader performance.

Reader Questions Answered

Correlation analysis for trading position diversification

I got the following inquiry from the ever-curious Trader Rod.

A few questions concerning correlation between two instruments for the purpose of diversification:

1. When calculating the correlation coefficient, given my time frame is daily, should I focus on daily closing prices or daily returns?

2. If the answer to previous question is to focus on returns, and the average holding period for a position is one week, should I calculate the coefficient using series of weekly returns?

3. What is a good threshold for diversification, i.e. at which point do I decide the two instruments are too highly correlated, e.g. anything below 0.7 is acceptable ?

Correlations have been gaining in focus of late, no doubt in large part due to the very obvious and public linkages between the dollar and stocks. It’s hardly a new thing, though. It’s been used in the type of work Rod is describing here for decades. Modern Portfolio Theory, for example, is heavily involved with correlation analysis. In other words, this isn’t a new field of analysis, so if it’s something you have a mind to explore there’s plenty of reading you can find on the subject. Make sure you do take a half approach, though. Using correlations without understanding what they are, what they mean, and how they can change can be extremely dangerous. Just ask the blown up banks and hedge funds who didn’t make it through the credit crisis.

As for Rod’s specific questions:

1) You should always use returns. They are how you can compare two instruments in a standardized fashion. Otherwise, differences in the prices of the securities can create comparison problems.

2) A holding period of only a week makes correlation application challenging. They tend not to be that stable in the short time frames. At best I’d say look at a rolling 5-week correlation and use it as a rough guideline. But realize that it’s only rough. There’s likely to be considerable deviation.

3) To achieve true diversification between two securities you need to have a near zero or negative correlation.

Trading Tips

Taking the Piss Out of Goldman

My British and Aussie/New Zealand readers will understand the above headline, as will those who have spent any time (as I have) with friends or colleagues from there. The rest of you will figure it out quickly enough as you read on. No, it isn’t about urine! 🙂

Actually, I’m not talking about the whole of Goldman Sachs here. It’s just one analyst based in London (I believe), but does demonstrate that not everyone who works for the company is brilliant.

A colleague of mine sent along this story

Goldman’s Currie Says Oil Drives Dollar Down, Not Vice Versa

2009-11-04 11:19:12.989 GMT
 By Juan Pablo Spinetto and Alexander Kwiatkowski

     Nov. 4 (Bloomberg) — Crude oil, which has risen 80 percent this year, is causing the U.S. dollar to weaken, driving metals and other commodities higher, according to Jeffrey Currie, head of commodity research at Goldman Sachs Group Inc.
     While oil has risen, the U.S. currency has weakened, leading to speculation that the dollar’s depreciation is driving investors to buy oil as an inflation hedge, thereby pushing up the price of crude.
     “I would argue the other way,” Currie said in an interview yesterday in London. “I would argue that higher oil prices drive the dollar down and then the weaker dollar drives the metals and soft commodities up.”
     The U.S. currency dropped to the lowest in more than a year against the euro on Oct. 26, while the dollar index, an indication of the international value of the currency, has lost 6.4 percent this year. Gold for immediate delivery has climbed 24 percent to a record this year while sugar is up 70 percent.
     “Oil represents 40 to 50 percent of the U.S. current account deficit, so a higher oil price represents an outflow of dollars that pushes the currency lower,” Currie said in the interview, after attending a Chatham House conference on food security.
     Goldman Sachs estimates that oil will reach $85 a barrel by the end of the year on Chinese demand for diesel, and $95 within 12 months time. Crude oil for December delivery traded at $80.35 a barrel, up 0.9 percent, on the New York Mercantile Exchange at 10:48 a.m. London time.

The immediate thing that jumped out at me was the “Oil represents 40 to 50 percent of the U.S. current account deficit…” bit. This is a really poor statistical analysis. I hesitate to even given it that much credit. You can say oil is X% of the total outflows. That would be fine. You cannot, however, state that one thing or another is X% of a deficit. A deficit comes about because outflows in total exceed inflows. It doesn’t mean some components are placed in the part of the equation that balances while others are placed in the deficit portion.

Be careful what kind of stats you accept – even from the so-called experts (and especially from politicians!) – as sometimes folks just don’t look at things the right way and in other cases they only present the information which supports their position (intentionally or otherwise). And watch out for making the same errors in your own analysis. They are insidious little buggers that can do real damage.

The said thing is this guy probably makes a lot more money than me. Maybe I should put in for a job at Goldman. 🙂

OK. Got that rant out of my system.

Here’s the real issue with what this Currie bloke is claiming in regards to the Dollar/Oil relationship. Oil prices can change without there being any change in currency exchange rates. It just becomes more or less expensive in all currency terms. This is what you would expect if prices are being driven mainly by supply/demand considerations. In other words, a rise in oil prices does not have to result in a decline in the exchange rate of the dollar.

On the flip side, though, a broad change in the value of the dollar against the other world currencies MUST result in a change in the dollar price of oil, holding the previously noted oil market supply/demand element equal.

Take a look of this chart of the correlation between EUR/USD and Oil prices.

Rolling 1-Month Oil vs EUR/USD Correlation

The chart above shows the rolling 1-month correlation between the EUR/USD exchange rate and Oil prices. That means each point on the chart shows the correlation between the two markets for the prior roughly 22 trading days. It shows that while most of the time the two have moved in the same direction (oil up, dollar down), there have been times when there’s been either little or no link, and at one point it the relationship was quite inverted. If oil was the main driving force in the dollar’s moves we would expect to see a more sustained positive correlation (though not necessarily always near 100%), not one that’s all over the place.