August 2018 Report

The Australian Dollar fell from USD 0.7432 to USD 0.7201. The MSCI World Index rose 0.83% and the S&P 500 rose 3.26%. The ASX 200 rose 1.76%. All these are total returns including dividends. We gained 2.04% in Australian Dollar terms and -1.13% in US Dollar terms. So, we  outperformed the Australian market and underperformed international markets.

The best performing investment in dollar terms was CFS Geared Share Fund gaining AUD 9.2k  followed by Unisuper (CDM.AX) gaining AUD 6.9k and Bluesky Alternatives (BAF.AX), gaining AUD 4.5k. The best performing asset class was "real estate", gaining 2.45% followed by US stocks, gaining 2.28%. The worst performing asset class was hedge funds, gaining 0.26%.

The following is table of investment performance statistics computed over the last 60 months (extended from 36 months previously) of data:

The first two rows gives the annual rate of return and Sharpe ratio for our investment performance in US dollars and Australian dollars. The other statistics are in comparison to the two indices. Based on beta, compared to the MSCI World Index we seem to be slightly geared, while compared to the Australian index we are less sensitive to market movements. We have a slightly positive alpha compared to the Australian and a negative beta compared to world markets. Finally, we now capture more of the up movements and less of the down movements in the Australian market and the reverse in the international markets. The fall in the Australian Dollar over this period explains the poor performance compared to international benchmarks.

This month I only made a relatively small amount of money trading futures – USD 1.8k – though this is the second best performance so far in dollar terms. The table * compares my performance to the market and the model:

This month was the fifth month of the futures trading experiment. The first month was the model development phase, and since then I have been trying to get disciplined at trading and further incrementally improve the model. August was rather erratic. At one point I was up AUD 11k over the amount originally put into this trading account and then blew almost all of it in a mixture of bad model trades and bad trading in and out of positions. One of the bad model trades would now not happen, due to improvement of the model, so I have learned something from this experience. In the early part of the month I was trading two NQ contracts. After the bad trading I cut it back down to one contract again. So we are back in Stage 2 of the trading experiment, which is learning to consistently trade one contract. I am only doing long trades for the moment, due to the reduced volatility at the moment. Still, I unnecessarily sacrificed about USD 1,500 by closing a long position early. The model slightly underperformed the market this month. The same thing happened in May when the market had a very strong result. The model is bearish and under-performs when the market is strong and outperforms when the market is weak.

We reversed progress towards the new long-run asset allocation:

Total leverage includes borrowing inside leveraged (geared) mutual (managed) funds. The allocation is according to total assets including the true exposure in leveraged funds.

The deterioration in allocation, came mostly due to investment activity. We invest AUD 2k monthly in a set of managed funds, and there are also retirement contributions. Then there are distributions from funds and dividends. During the month, I also:

• I added another AUD 10k to the Winton Global Alpha fund, but withdrew AUD 50k from the trading account, for a net reduction in the allocation to commodities.
• I received the payment for the takeover of IPE but bought some more shares in OCP.AX, overall reducing the allocation to private equity.
• Added to positions in PMC.AX and CDM.AX, increasing the allocation to hedge funds. 
• I added a small position in Yellow Brick Road (YBR.AX), increasing the allocation to Australian small cap stocks.

* The statistics at the bottom of the table are based on only 5 months of data and so are not at all reliable yet.

Yellowbrickroad and Tribeca Natural Resources

Yellow Brick Road (YBR.AX) is an Australian mortgage broker and financial planning company. Mercantile Investment Company (MVT.AX), who took over IPE has made an offer to take over the company at 9 cents per share. However, the company has rejected the offer and the market is trading higher than 9 cents under the assumption that Mercantile will have to increase the offer. The company has net tangible assets of 13.4 cents per share, though much of that is future expected trail commissions. Regulators are clamping down on trail commissions and these might go away in the future, but I doubt that existing deals would be cancelled. The company just announced it made a small loss this year after a small profit last year. So, net tangible assets would seem to be the minimum reasonable price for the business.

I have started to make a small investment in the company. As it is risky to buy above the announced takeover price, this won't be a big position. The CEO and his brother own 19% of the company as does Nine Network. So, these big shareholders would have to get a price they are willing to accept for the takeover to actually proceed. MVT owns about 20% too, so smaller shareholders have 40% of the company.

Commsec announced the IPO of a listed investment company (closed end fund) managed by Tribeca. This will be a listed hedge fund. The managers have an extremely strong track record, though returns have fallen from the very high returns they made in 2015. I suspect that as money under management increased, returns fell. Still, they show the potential to perform very well going forward and I think this LIC should trade above net asset value. So, I plan to participate in the IPO. I also plan to redeem my units in the Colonial First State Janus Henderson Global Resources Fund, which has not performed that well in recent years.

Just Follow the Model

Yesterday, based on looking at the candlestick patterns and Oscar Carboni's caution about a possible "holiday reversal", I decided to close my long position. I missed a big rally today as a result. At least I didn't lose money. But I shouldn't doubt the model. Now the model is signalling short. However, due to low volatility, I won't take this short signal. There was a similar signal on 9 January that made small gains for a couple of days and then lost big. Another similar signal on 11 May also lost money. So, seems a good point now to just step out of the way, especially as on Tuesday I am traveling to Europe.

The model gained 3.19% on this long trade.

That Worked Pretty Well

The model passed the test. We are now back to a positive return from trading for the month.

Started Trading Again on the Long Side

Following up on this post, I took the next long signal from the model, which was yesterday. I got in at NQ=7418.75, which was almost the low for the day. The market ran up sharply at the beginning of the cash session and then corrected sharply, ending down, but still above my entry point. Today's forecast is long again using the latest version of the model, which smooths one of the signals but using the old version with an unsmoothed signal, it might be short, or maybe I'd invoke the "close to zero rule", which said to ignore a change in direction of the indicator if the indicator was close to zero. Now, we have a clear objective rule. Let's see what happens.

But until volatility shows some sign of increasing, I won't take the next short signal.

NetWorthShare

NetWorthIQ seems to have died. So, following up on EnoughWealth's blogpost, I have opened an account with NetWorthShare. It's surprising that NetWorthIQ didn't make more of their website. I would have thought they could have got a lot of advertising from the financial industry.

Does it Ever Pay to Go Short?

I did some tinkering with the model to avoid the kind of false buy signal that resulted in the stop out last week. I applied Hodrick-Prescott filtering to one of my indicators. This eliminates these kind of false turning points but also eliminates a fairly subjective rule in my decision tree. So, overall that improves the model. This is one step further to a fully objective system that can be automated.

You need to be careful with HP filtering as it uses all the data in computing the smoothed estimate. So in back testing you have to run the filter repeatedly using just the data that was known up to that point.

The model is currently short. But I don't have a trade on. I am thinking to put a trade on when it switches back to long.

In a recent post, I showed that a hedged portfolio levered 1.5 times would track the market when the market does well and track the model when the model does well. Instead of thinking of this as trading plus investment we can examine it as a pure trading strategy. That suggests that it doesn't pay to go short. Just stay out of the market when the model is short and only take the long trades and lever up the returns. In 2018 so far, going short would add to returns though. But in 2017 going short detracted from returns. The model only won 45% of trades in 2017. The average win (1.57%) was almost double the average loss (-0.9%) though so, the expected value of a trade was still 0.2%. When we split trades into long (22) and short trades (24) instead the average long trade made 0.83% and the average short trade lost 0.39%. So, avoiding short trades would have doubled returns, returning 20% instead of 10% for the year. Of course, just going long for the whole year would have returned 32%. But we don't know that will happen ex ante. Levering the 19% by 1.5 times or so reproduces the long-only result.

The question now is whether you can win by going long only in a year like 2008. My intuition is that trading would result in a positive return for the year but that this would be insufficient to hedge the losses in an investment portfolio. It would moderate the downside though.

Testing that hypothesis will have to wait a little while.

But for the moment, volatility is low and so going long only might pay off.

Stopped Out...

My long position was stopped out on Wednesday. Now back to more or less zero profit on this account - still have a positive overall result from the trading experiment. Also, I realised that I still can't really psychologically handle trading overnight futures positions at the moment even at the smallest trade size. I am losing sleep because of it. So, I am going to stop trading for the moment. I need to get a lot of academic work done in the next two weeks before going on another overseas trip. At some point after I am back I will do some further research on trading models. My thinking is I could design a model that would only make trades when the odds were most in favor of winning. The current model trades all the time regardless. The ultimate long-run goal is automation of trading or taking it out of my hands in some other way. To achieve these goals I don't need to be trading continuously at the moment if I'm not making good money at it and  it's having negative effects instead.

I am also at the moment on a trip for a job interview. As I am learning more about the position it seems more challenging and to need leadership skills beyond what I have. I would be shocked at this point, though, if they offered me the position.

Losing Model Trade and Trading Badly

The downside didn't last long... Model switches back to long this morning. The short trade lost the model 0.4%. Due to bad timing - closing my long at pretty much the low point of the down move and going short, I gave back almost all the profits I'd made for the month so far. As a result, I have gone back to trading just one contract until I can get my act together properly.  So, back to Stage 2 of the process, after attempting Stage 3...

The NQ equity curve so far this year (starts at net profits from previous years):

The problem with tactical trades is that I then need to follow the market to see whether to open or close a tactical trade. But they're not necessary for getting a good long term return from the model. It probably would be better to diversify to trade more than one market first instead, as that will result in fewer losing days if the correlation between the markets is low. So, I did recently start to build a model for oil again, though I was a bit stuck in coming up with good decision rules so far.

Turkey Turns the Outlook Bearish

The down day on Friday in response the Turkish crisis switched the model to short going forward. Based on the 3 hour stochastics, there could be a bounce on Sunday evening (US time) continuing the bounce into the close on Friday. This could be a good opportunity to go short.

Looking Bullish

The model state is almost identical with that on 10 January this year. That's presumably pretty bullish for the next week or so... Of course, anything could happen.

Backtesting and Hedged Portfolios

So, I backtested for all of 2017 using the latest model rules. The model makes money for the year, but there are several losing months, and the model underperforms the market. I could quite easily predict which months would be more profitable and which were more likely to be money losing by looking at their volatility. So, that idea works out of sample.

 

The graph shows the NASDAQ 100 index (close) for 2017 and the model return. The interesting thing is that a hedged portfolio of the market and the model, tracks the market quite closely. The hedging strategy would invest 75% of net worth in the QQQ ETF and use 25% of net worth to trade NQ futures with 3 times leverage according to the model. So it is 1.5 times leveraged with 50% of the total exposure long and 50% traded. Of course, you wouldn't really want all your portfolio into the the QQQ ETF. At least I wouldn't. But this is a step towards seeing what a realistic strategy with investment and trading would look like. Now if we look at 2018:

The hedged portfolio tracks the model, which vastly outperformed the market, closely instead now. It seems that you can get the best of both worlds with this strategy.

Soybeans

To try something different I tried a soybean daytrade. Made $107 in 17 minutes, so not bad :) But I only got the beginning of a much bigger move, so I need to be more patient in future trades i this commodity.

Volatility and Return

The graph shows the average true range (ATR) divided by the closing NQ futures price for all 14 day periods in 2018 so far and the average daily NDX model return over the same period. The correlation is very strong. The model tends to make lots of money when the market is volatile and potentially lose money when the markets are not volatile. This is why the model would have lost money in early 2017 for example and probably why the NASDAQ index produces better results than the S&P 500. Clearly, noise dominates signal when volatility is low. However, the correlation between recent volatility and future returns is quite weak. So, this isn't yet a useful tool for deciding when to trade and when not to trade on a daily or weekly basis. But if you were losing money for a while and volatility was low it would make sense to get out of that market and trade something else until volatility appeared to return.

I'm still holding the strategic long contract. It's up around $4k at the moment. I did a couple of tactical trades netting $215 and $980.

Trading on 2nd August

The market came with 4 points of stopping out the long position but then took off to the upside as soon as the New York market opened. Gain for the day was almost USD 3k. Today, I have added a second contract (@ 7391). Stop remains the same.

July 2018 Report

This month was the fourth month of the futures trading experiment. The first month was the model development phase, while May and June were about ironing out the glitches and training myself to trade the model properly (and not give in to gut instinct etc). In the first half of July I only traded one day and lost but model returns were good in the beginning of the month. Then in the second half of the month I got back into regular trading. Initially the model wasn't doing well but then things improved again as a short trade worked out.

The Australian Dollar fell from USD 0.7571 to USD 0.7432. The MSCI World Index rose 3.05% and the S&P 500 rose 3.72%. The ASX 200 rose 1.39%. All these are total returns including dividends. We gained 1.56% in Australian Dollar terms and 2.12% in US Dollar terms. So, we  outperformed the Australian market and underperformed international markets.

The best performing investment in dollar terms was Unisuper gaining AUD 4k closely followed by Cadence Capital (CDM.AX) gaining AUD 3.9k. The next best in dollar terms was Bluesky Alternatives (BAF.AX), gaining AUD 2.8k. The best performing asset class was "private equity", gaining 2.66%. The second best performer was US stocks, gaining 2.58%. The worst performing asset class was Australian large cap, gaining 0.41%.

The following is table of investment performance statistics computed over the last 36 months of data:

The first two rows gives the annual rate of return and Sharpe ratio for our investment performance in US dollars and Australian dollars. The other statistics are in comparison to the two indices. Based on beta, compared to the MSCI World Index we seem to be slightly geared, while compared to the Australian index we are less sensitive to market movements. We have a slightly positive alpha compared to the Australian and world markets. Finally, we now capture more of the up movements in the international and less in the Australian market and suffer less of the down movements in both the Australian and international markets.

This month I only made a small amount of money trading futures: USD 1.0k. The table compares my performance to the market and the model:

The US markets went up and then down. The model did outperform the market.* In the first week of July I didn't trade as I was in Japan and my phone wouldn't receive the text messages needed to log into the trading account. I actually received all these texts after returning to Australia! Then I traded long on a day when the futures price would suggest to be short and the index values suggest to be long and got stopped out. This made me do some more model research and revise the stops policy, though I found that index values provide better trading signals. After that I got back into regular trading trying to trade double the size but the model was losing at first. Then I started doing strategic and tactical trades, which helped psychologically.

We made more progress towards the new long-run asset allocation:

Total leverage includes borrowing inside leveraged (geared) mutual (managed) funds. The allocation is according to total assets including the true exposure in leveraged funds.

The improvement in allocation, came partly due to market movements and partly due to investment activity. We invest AUD 2k monthly in a set of managed funds, and there are also retirement contributions. Then there are distributions from funds and dividends. During the month, I also:

• I added another AUD 10k to the Winton Global Alpha fund, increasing the allocation to commodities.
• I added AUD 50k to the trading account and in the end was moderately successful at trading, increasing the allocation to commodities .
• I closed a small account with Colonial First State, which was invested in the CFS Geared Share Fund, reducing the allocation to large cap Australian stocks. 
• I rebalanced my CFS superannuation account, reducing the allocation to large cap Australian stocks and increasing the allocation to other asset classes.
  
• I bought 75,000 shares of BAF.AX increasing the allocation to private equity and real estate.
• I sold my position in PIXX.AX and bought a smaller amount of Platinum Capital (PMC.AX) and bought more as PMC fell further in price. This reduced the allocation to hedge funds.

* The statistics at the bottom of the table are based on only 4 months of data and so are not at all reliable yet.

Back to the Long Side

Closed short and opened long @ 7261.5. Stop is @ 7211.

The model switched to long today but the signal is close to remaining short and the intraday indicators are signalled short for the first part of the day. So, I kept the short position until now almost 7 hours into the trading day from 6pm New York time (8am Eastern Australian time). I put on a ten point trailing stop for two contracts, so it closed the short and opened a long simultaneously. Gained about 25 points on the short side compared to the market open.

Profit from the closed short was USD 3,000.90. The model gained 1.73% for the trade starting on 25 July and ending 1 August.

Monthly report coming soon.

Friday 27th July Trading

Finally we had some good downside on Friday with the NQ futures falling 126 points to 7299.75 – each point is $20 per contract. I had a strategic short from 7411.75 and a tactical short put on in the morning at 7429.5. My only regret is that I closed the tactical short at 7380 for only a $986 profit rather than setting a wider stop and letting it ride down for another $1600 in profit :) The strategic short is of course still in place as the model remains short. I am now up $885 for the month. Hopefully, I will stay up for the last couple of days of the month. I expect the market will go down further, both the model signals and looking at previous declines this year suggest that there is a lot further to go down before bottoming. All the previous declines went below the 34 day moving average and two went to the lower 34 day Bollinger Band:

If the latter happens, we would be at 6950 or so, $7,000 a contract from where we are now. Of course, given the strong trend it is more likely to be like the declines in April and June, which didn't reach the lower Bollinger Band.

Indications at the moment are that we don't want to do a tactical short during the US overnight on Sunday-Monday. A short near the US market open looks more likely to pay off.

Trading in 2018 is Objectively Better

I computed my average gain per NQ contract traded in 2006-2008 compared to in 2018. In 2006-08, on average I gained 0.46 points per contract traded or $9.31. Commissions on a roundtrip were $4.80 then. In 2018 so far, I made 3.69 points per contract or $73.74. Commissions are now $4.10 on a roundtrip. So, my trading now is almost an order of magnitude better. The average is brought down by lots of small daytrades I've done. As I plan to do fewer of those, the average should improve, I hope. On the other hand, the level of the index is now nearly 4 times higher than it was in 2006-8 and so a given percentage price move translates to more points.

This graph shows the equity curve on NQ trades - the actual number of trades is half this as there is one data point for each opening or closing of a position. Initially in 2006-7 I had a reasonably good increase in profits, peaking around $10k cumulative profit. Then there was a long slow decline into 2008 of a series of small wins, punctuated with larger losses. The big jump is the start of trading in 2018,  when I had a series of big wins. Since then, things have gone sideways, with losses equal to gains.

Tactical and Strategic Trading

After seeing a big profit disappear again a few times, I think I am going to adopt a combination of strategic and tactical trades now that I am trying to trade two contracts. One contract is always held in the direction of the model for as long as the model is long or short. This is the strategic trade. The other contract is in the same direction but can be closed out for the day when there is a big profit already. That is the tactical trade. Yes, day trading but the kind of daytrading where you put a trade on at the beginning of the overnight futures session and close it at the market open or vice versa. I had planned to do this but deferred it to stage 4 or 5 of the experiment. But I think I need the psychological boost now. I will make trading decisions using a chart with 2 to 3 hour candles. On a chart at that frequency most days break down into a rising and a falling period or a weak (when the market goes sideways) and strong period. I will close the tactical trade if it has made a profit and the next half of the day looks like being weak or going in the opposite direction to the model. Anyway, let's see if this works.

It probably was necessary to suffer through the pain of seeing a big profit on two contracts disappear a couple of times to be willing to have two contracts on overnight Australian time.... I tried adding one contract tactically before but was too nervous about it to set a wide enough stop.

Today the strong period was during the overnight (the market went down, in the model direction) and the weak period was during the US daytime when the market went up in the opposite direction to the model.

P.S.
I was just stopped out by the Google earnings report... Even more wishing I had closed one contract at the market open... This was a "tactical" rather than model stop. So, I got short again (tactically and strategically) at 7425.5 with the stop at 7441. This is very close, but was the second pivot resistance level when the model originally went short and so with the current model stop rules, that's where the stop stays.

Actually, the model is bit ambiguous today, but following the rules for these situations, we should still be short...

P.P.S.
I was just stopped out at the model stop. That means I'm out for today. Tomorrow morning I will re-evaluate the model direction. This is definitely looking like a losing month, similar to April, which was the initial model development month.

This model trade that was initiated on Friday lost 0.86%.

I researched the previous cases of similar ambiguous model signals so far this year.  There were only two previous cases, which were where the signal said to switch to short but was ignored because the turning point was from a value of the indicator that was close to zero. Both those times, staying long was the right thing to do. Maybe, in the absence of getting stopped out, staying short will turn out to be the right thing to do today. We will see. Either way, it is a very small sample to base any conclusions on.

The Kelly Criterion

There is a lot of incorrect information on the web about applying the Kelly criterion in the stockmarket. It is very different to applying it in a card game where you either win or lose a fixed amount. In that context the Kelly criterion tells you how much to bet on each gamble. But whether you are doing short-term trading or long-term investing that is not the case in the financial markets where there are continuous payoffs. In this paper, Ed Thorp lays out the Kelly criterion for investing in financial markets. It results in a rule of how much leverage to use when investing in a portfolio. That portfolio could be a buy and hold portfolio of stocks, or it could be a high turnover futures trading account. To determine how much of total net worth to allocate to a particular asset class or strategy is a different calculation. I think you should maximize the Sharpe ratio for your total portfolio. Where to set the stop loss in trading is a similar calculation - you want to use stop loss rules that maximize the Sharpe ratio for the strategy. I don't think Kelly tells you how much to risk on each trade in the way it can tell you how much to bet on each gamble.

The Kelly criterion isn't a practical rule in the real world as it requires you to continuously change the size of your position as you win or lose money. The suggested leverage for my trading model – this may be exaggerated because of too short a sample of returns and volatility – is greater than that allowed by the futures exchange. This amount of leverage would immediately blow up in the real world and result in huge amounts of commission and bid-ask spread payments...

Very Good Service from Interactive Brokers

We phoned Interactive Brokers about the login problem. They have a system issue. They set up the account so it can accept a temporary security code which they gave to us. We'll use this until they resolve the issue. The questions they asked to confirm our identity apart from a couple of the typical secret questions were what the net asset value in the account was, what position was in the account (short NASDAQ 100 futures), and what bank we use to transfer money to the account. If we had stolen a password we would have know two of those at least, because you can login into the account on a read-only basis with the password.

I managed to use the temporary security code to set up the mobile app which can produce codes even if it can't receive texts.

Can't Log Into Account

As of Saturday morning I am not receiving the test messages from Interactive Brokers that I need to log into the account. I sent a text to myself using Skype, so it is not the same problem that I had in Japan where I just can't receive texts. If this is still the case on Monday morning we will need to phone the broker to resolve this. They do have a mobile app that can generate the required login numbers even if you don't have phone service, but to set this up you need to get a text from the broker... At the moment this isn't a problem as the model is short for Monday still. In the worst case scenario, I can trade in the opposite direction using my own trading account until the stop is hit at NQ=7441. As my account is much older I have a physical security device - actually a bunch of codes on a card. But I will need to sell stocks/and or transfer money into my account to have enough margin to trade with. And this isn't ideal as profits are taxed higher in my account.

Switching to Short

The model has switched to short as at the open of today's Globex session (8am Eastern Australian Time, 6pm New York Time). I went short 2 contracts in an attempt to move to Stage 3 of the experiment.... The stop is at NQ=7441 and am short from 7383, so risk is relatively low (compared to what it might be), though the nearer the stop the greater the chance of hitting it...

Selling Everything

Well, in my mother's former account. Apparently the main (international) bank doesn't care that we the estate hasn't yet completed probate. Another local bank is, by contrast, very concerned about that. If we sell and go to cash, apparently we avoid paying this investment bank's very high fees. The account has returned practically nothing after fees in the last three years. August and September are historically bad months for equities (though only about 20% of the account is in equities). As we want to sell in the end anyway, it makes sense then to sell now. The plan is to hold everything in US Dollars in the interim.

Stopped Out Again

So, I put on my second trade of the month - long NQ - and was stopped out again, losing $1100 this time. The stop actually saved about $300 this time. But the model is still long for 17 July and so I put on a new long trade at 8:00am Australian time at 7320, which is up $190 at the moment. Stop is 7253 on this trade, currently at 7329.75. Down about $1700 for the month so far. NASDAQ 100 model is up 3.6% and NASDAQ 100 index 4.5% but I'm down 4.5% (due to two bad trades only and leverage). I'm determined to stick to the model now... let's see how I do.

Model Decisions for the Year So Far

The chart shows each short and long decision the model has made in the NASDAQ 100 index since the beginning of the year. The letter S or L is placed on the first day the model was long or short in each trade. So in theory you should get long or short at the previous close. Most trades were winners, though in late February, for example, the model got short on a big up day and then switched back to long the next day, which turned out to be the top. That long trade was also a loser. There were also stop outs along the way, which aren't marked here as new trades. Some times the model picks the exact top or bottom, at other times it misses it by a couple of days.

So, all I need to do is trade exactly like the model :) I put a new long trade on this morning.

New Investment: BlueSky Alternatives Fund

I had read back in April about BlueSky's battle with activist hedge fund Glaucus. As a result, the share price of the management company (BLA.AX) collapsed and they undertook a thorough review and independent valuation of all their investments. Listed investment company (closed-end fund) BAF.AX, is a fund of funds, investing in BLA managed investments in real estate, private equity, agriculture, and water rights. The price of this fund also fell, though not as dramatically. The valuation of all but one of its investments is now complete and the net asset value is AUD 1.13 per share. On Friday the stock was trading around AUD 0.80. The company is buying back a lot of stock which is supporting the price. I made an initial investment today and could add more if my thesis that it should rise, plays out.

Position Size

One of the main ideas in traditional momentum trading wisdom is that as volatility increases your position size should decrease. This is one of the key ideas in the Turtle Trading System, for example. If you have no idea what will happen, then higher volatility likely will result in higher losses as well as higher gains.

But if you do have some ability to predict the future, that trading signal might be stronger when volatility is higher and weaker when volatility is lower. Then you will have more losing trades when volatility is low and a higher proportion of winning trades when volatility is high. This seems to be the case with my system. Higher volatility means higher risk but also a higher probability of being right. In this case, position size maybe should be constant regardless of volatility.

P.S. 22 July

I calculated the Sharpe ratio for constant position size and for strategies that reduce position size as volatility increases and and increase position size as volatility increases. The constant position size strategy has the highest Sharpe ratio confirming my intuition. The strategy with a negative correlation between position size and volatility has the lowest Sharpe ratio. The strategy with a positive correlation is in between. So, for the moment I will stick with constant position sizing.

Turtle Trading

I have been reading the Complete Turtle Trader, trying to get some inspiration. Back in the early 1980s, futures trader Richard Dennis hired a bunch of relative novices (some actually had trading experience) and taught them a trend-following method of trading. He then got them to trade some of his assets using the methods. The idea was to see if trading could be taught. During the next few years, many of them generated extraordinary returns, as documented in the book. Then the experiment ended after Dennis suffered major losses and shut his fund.

Some of the "turtles" went on to run their own investment firms. The star pupil seems to be Jerry Parker who founded Chesapeake Capital. However, subsequent performance has not really been that good.* The fund has underperformed the S&P 500 and has had about twice as much volatility. Taxes would be much higher on Chesapeake's strategy than on buying and holding the index. Why does voltatility matter? Because I could have used leverage to invest in the S&P 500, increasing volatility to the level of the Chesapeake Capital fund, but increasing returns far beyond its returns.

This doesn't encourage me to adopt a long-term trend following strategy. The assumption of this kind of model is that the future is entirely unpredictable... Eckhardt is cited in the book as saying that random entry into a trade is just as good as long as you follow exit rules. That's true about most momentum trading strategies I think.

It's notable that none of these turtle related firms are very big in terms of assets under management.

* The "LV" fund performed better but still underperformed the S&P 500 on a risk adjusted basis. Salem Abraham's – described as a "second-generation turtle" in the book – fund has gone nowhere in the last ten years.

The Index Gives Better Trading Signals than Futures Prices Do

It turns out that the NASDAQ 100 Index gives better trading signals than the NQ futures prices themselves do. I think the reason for this is that most trading takes place when the stock market is open and that is usually when big moves happen. The "out of hours" trading is mostly noise then reflecting what is happening in other stock markets and after hours earnings reports etc. The futures prices still provide signals that "beat the market" but not as well.

I did find again, that stops mostly detract from performance and I am introducing a new stops policy. When we change direction we set the stop loss at the the second pivot support for a long or the second pivot resistance for a short. We then keep that stop until either the direction of trade changes or we are stopped out. This results in far fewer stop outs.

It's likely that in commodity markets such as oil or gold the futures prices do provide good trading signals. Well, there isn't anything else to use anyway.

Futures Prices vs. Index Values

I didn't trade while I was in Japan because my mobile phone wasn't receiving the text messages I needed to log in to my trading account. When I got back to Australia I dithered about getting back in for a couple of days, missing a nice rally. Then this morning I decided to make the plunge (on the long side) and 2 hours later I was stopped out. Apparently there is negative news on US tariffs on trade with China.

After the cash market closes at 4pm New York time, the stock index futures trade for another hour before closing for one hour. The futures closing price can, therefore, be quite different to the index closing price. This was the case today where the futures plunged around 30 points in the last ten minutes of the futures trading session. Using the index data for the 4pm close, my model said to stay long. However, if we had knocked 30 NASDAQ points off to reflect the futures closing price, it would have switched to short. So, I think I need to get historical futures data and re-estimate my model with these. I should be able to get these from Quandl. An additional advantage of using futures prices is that I can do the analysis one hour later -  currently from 7am Australian Eastern time rather than 6am Australian Eastern time. The futures market then shuts for an hour and reopens at 6pm New York time or 8am Australian time.

However, on Saturday morning the futures market closes at Friday 5pm New York time and then doesn't reopen till Monday morning at 8am in Australia. So, I will need to do the analysis with index closing data before 7am on Saturdays unless I want to get stuck in possibly the wrong direction over the weekend.

June 2018 Report

This month was the third month of the futures trading experiment. The first month was the model development phase, while last month was about ironing out the glitches and training myself to trade the model properly (and not give in to gut instinct etc). It turned out that this month was more of the same and I am still on the second stage of the experiment, which is learning to consistently trade the model and iron out the glitches. The third stage is to reach a level of profits equal to my salary, while the fourth stage would be to maximize returns beyond that. I had planned to move to trading two contracts this month, but mostly traded one contract still.

June is the month when our Australian managed funds pay out their main distributions at the end of the Australian financial year. These usually have large tax credits associated with them. In this report, I have estimated the likely tax credits, which won't be known till later in July.

The Australian Dollar fell from USD 0.7571 to USD 0.7391. The MSCI World Index fell 0.50% and the S&P 500 rose 0.62%. The ASX 200 rose 3.63%. All these are total returns including dividends. We gained 3.16% in Australian Dollar terms and 0.71% in US Dollar terms. So, we  underperformed the Australian market and outperformed international markets.

The best performing investment in dollar terms  was CFS Geared Share Fund gaining AUD 23k. The next best in dollar terms was IPE, gaining AUD 19k. The best performing asset class was "private equity", gaining 7.79%. The second best performer was Australian large cap stocks, gaining 3.21%. The worst performing asset class was hedge funds, losing 0.46%, the only asset class that lost money.

The following is table of investment performance statistics computed over the last 36 months of data:

The first two rows gives the annual rate of return and Sharpe ratio for our investment performance in US dollars and Australian dollars. The other statistics are in comparison to the two indices. Beta expresses the change in investment returns for a 1% change in the market. Compared to the MSCI World Index we seem to be slightly geared, while compared to the Australian index we are less sensitive to market movements. Alpha shows the risk adjusted excess annual return. This is how much we are beating the market (or not) adjusted for risk expressed as beta. We have a slightly positive alpha compared to the Australian and world markets. Finally, up capture and down capture breaks beta into the response to positive and negative months in the stockmarket. A greater up capture than down capture ratio is desirable. We now capture more of the up movements in the international and less in the Australian market and suffer less of the down movements in both the Australian and international markets. A hedge fund like return would show this positive skew and a positive alpha. We show some hedge fund like properties across the markets.

This month I only made a small amount of money trading futures: USD 1.2k. The table compares my performance to the markets and the models:

The US markets went up and then down, ending quite flat. The models did outperform the market.* Through a series of missteps I performed worse than the models given that I was using leverage. This is mostly because I picked the wrong contract to trade with for some of the time. I think one way to trade in strongly trending markets is to act more tactically, trading in the direction of the model when other short term indicators (using a chart with 3 hour candles) show it is advantageous and then closing the position when the odds move the other way. More than once I was up USD 2k and then gave it all back... On 21 June I did exactly the wrong thing, throwing in the towel for the day and closing my short just as the market was about to reverse and go down... Seeing that happen did increase my faith in the model a little bit more. Gut instinct is not as good as the model. But then the same thing, kind of, happened on the last day of the month. The models were short, the market went up, but I capitulated at almost the worst point, because at the market close the indices were way down from the highs.

The best I can say is that I didn't lose money for the month as a whole. So it looks like more of the same for next month. I'll try to trade one contract exactly according to the model and one tactically.

We made a little more progress towards the new long-run asset allocation:

Total leverage includes borrowing inside leveraged (geared) mutual (managed) funds. The allocation is according to total assets including the true exposure in leveraged mutual funds.

The improvement in allocation, came partly due to market movements and partly due to investment activity. We invest AUD 2k monthly in a set of managed funds, and there are also retirement contributions. Then there are distributions from funds and dividends. During the month, I also:

• I shifted money out of trading when I shifted the account I am trading with. This decreased the allocation to commodities.
• Added another AUD 10k to the Winton Global Alpha fund, increasing the allocation to commodities.
• I sold 500,000 shares in IPE and bought a small amount of OCP.AX, reducing the allocation to private equity.
• I sold some Platinum Capital (PMC.AX) and bought a lot of PIXX.AX, which is the equivalent ETF, because PMC was particularly overvalued. This increased the allocation to hedge funds.

* The statistics at the bottom of the table are based on only 3 months of data and so are not at all reliable yet.

Trading Update: Tokyo Edition

So far it looks like the "old" more systematic model won today. US stocks are down. I'm not trading as I was flying to Japan and now my phone's connectivity is dodgy and I need it as a security device. Anyway, Wednesday is US Independence Day and Tuesday is already a short trading day so, I'll wait till Thursday or when I am back in Australia next week,

My hotel is the blue tower in the background.

P.S.
The stockmarket turned and went up. So, the "new" model was vindicated in the end. Still, I don't like trading rules that don't make logical sense.

Changing the Model Back and Researching the Model

I'm not happy with this model rule that I added on 27th April. It's not very systematic. So, I dropped it, while keeping rules added more recently. The stock market switches between two states - trending and cycling - and the model uses different indicators in each state. Deciding what state we are in is a little problematic. So, I am going to have another look at the rules for that and also possibly smoothing out very small moves, which are noise. The rule I just dropped was ad hoc - it switched to the indicator for the cycling state when we are in a trending state based on one period lagged positive performance.

Monday is a good test of the two algorithms - the simpler model is short and the model with the "ad hoc" rule is long. Let's see which wins.

I also thought of using the volatility indices, VIX and VXN, as indicators. The basic idea is that volatility is highest at bottoms in the market. But I couldn't see a way to do this. This graph shows why:

VXN (and VIX) rose throughout January while the stock market rose too. This was a warning sign that a correction was coming. But a model that shorts the market when VXN is expected to increase would have lost money all January. Also, my existing forecasting model is no good here...

Gold 2048: The Future of Gold

This report seems bullish for the price of gold over the next 30 years. Continued growth in India and limited gold discoveries recently seem bullish. On the other hand, some regions haven't been explored much and technology could enhance extraction, though the latter likely balanced by increased environmental restrictions.

Mercantile Makes Offer to Take Over IPE

Mercantile Capital (MVT.AX) has made an offer to take over IPE.AX at AUD 0.0775 per share. I've discussed Mercantile's interest in IPE before and bought shares as a result. I had 1.5 million shares. My only regret is I didn't buy more. People selling even below 6 cents discouraged me from buying more.

I had 500,000 on offer for sale and they sold just now at 0.075 up from 0.063 yesterday. I will wait and see with the remaining million. I should at least hold it into the next tax year, next month. If I sell now, it will wipe out my existing tax losses and more. So better to defer tax for another year.

Tax Optimization for Trading

I still have some capital losses left over from the financial crisis. I will probably use them up for this tax year ending 30 June. After that, trading profits would be taxed at my marginal rate of 47% (and even higher if Labor get back into power and implement their tax policy). So, I am opening an account at Interactive Brokers for Moominmama (formerly Snork Maiden). Trading in her account will only attract a marginal rate of 34.5% initially and then higher if we make lots of profits. This will reduce our overall tax bill and is totally legitimate in Australia.

Actually, given that franking credits are fully refundable, even if they exceed your tax bill (but Labor wants to change that too), it also would make sense to have other investments in Moominmama's name. The reason we don't, is that up to a couple of years ago, when my income went over AUD 180k per year and she went part-time we were in the same marginal tax bracket. But perhaps I should direct new investments to her account?

In somewhat related news, the minimum wage in Australia has just been raised, so that someone working full time at the minimum wage earns just over AUD 37k a year (about USD 14.25 per hour). This means that the marginal rate for such workers is now also 34.5%! That really seems crazy to me.

May 2018 Report

Another very active month financially. The Australian stock market rebounded quite strongly but then turned over as other markets did. This month was the second month of the futures trading experiment. The first month was the model development phase, while this month was about ironing out the glitches and training myself to trade the model properly (and not give in to gut instinct etc).

The Australian Dollar rose from USD 0.7540 to USD 0.7571. The MSCI World Index rose 0.21%, and the S&P 500 2.41%. The ASX 200 rose 1.09%. All these are total returns including dividends. We gained 1.84% in Australian Dollar terms and 2.26% in US Dollar terms. So, we outperformed both the Australian market and the international markets and slightly underperformed the US market.

The best performing investment in dollar terms was NASDAQ futures gaining AUD 9.5k (this is going to be a theme :)). The second best was CFS Geared Share Fund gaining AUD 8.9k. The worst performer in dollar terms was IPE, losing AUD 1.5k. The best performing asset class was "commodities", which includes futures trading, gaining 6.24%. Hopefully, this will become a near permanent feature. The second best performer was Australian small cap stocks, gaining 2.92%. The worst performing asset class was private equity, losing 0.78%, the only asset class that lost money.

A new feature starting this month is the following table of investment performance statistics. The statistics are computed with the last 36 months of data:

The first row gives the Sharpe ratio for our investment performance in US dollars and Australian dollars. The other statistics are in comparison to the two indices. Beta expresses the change in investment returns for a 1% change in the market. Compared to the MSCI World Index we seem to be slightly geared, while compared to the Australian index we are less sensitive to market movements. Alpha shows the risk adjusted excess annual return. This is how much we are beating the market (or not) adjusted for risk expressed as beta. We have a slightly positive alpha compared to the Australian market and a close to zero alpha compared to the world market. Finally, up capture and down capture breaks beta into the response to positive and negative months in the stockmarket. A greater up capture than down capture ratio is desirable. We do capture more of the up movements in the Australian market and suffer less of the down movements. A hedge fund like return would show this positive skew and a positive alpha. Compared to the Australian market we show some hedge fund like properties.

This month I made money trading futures: USD 7.2k. The table compares my performance to the markets and the models:

I also bought and sold investments in this account and added AUD 25k in cash towards the end of May, so don't expect the starting cash to change with just income earned. My rate of return in May far exceeds the models or markets because of leverage. I mostly traded one contract at a time and so was using a bit over 3 times leverage. I could also select the market where I thought the model signal was most reliable. In the early part of the month I mostly traded NQ (NASDAQ) and in the later part of the month ES (S&P 500). I also traded CL (oil). Most of the gains were made early in the month when the market rose. After that the market mostly went sideways.

I more or less successfully followed the plan for the month, which was to consistently trade one futures contract according to the trades that the model provides, while learning about entering trades more optimally and setting stops. There were some hiccups, particularly on 14 May when I lost much more than the model due to bad trading. I can say that the second of my goals in the experiment - to consistently trade the model - was a qualified success. I was much more disciplined than when I previously traded futures, but still not disciplined enough. The third goal - to earn as much as my salary from trading fell short though I was in the ballpark. I would need to make USD 12.5k to reach the goal. For the next month I plan to work on the same goals and maybe increase the position size when I am particularly certain about the market direction. This is why I added more cash to the account. After adding the money and doing some bad daytrading, which I shouldn't be doing, I had second thoughts about taking a larger position. But I maybe should be trading both stocks and oil simultaneously. Horizontal rather than vertical expansion.

We made more progress towards the new long-run asset allocation:

Total leverage includes borrowing inside leveraged (geared) mutual (managed) funds. The allocation is according to total assets including the true exposure in leveraged mutual funds.

The improvement in allocation, came partly due to market movements and partly due to investment activity. We invest AUD 2k monthly in a set of managed funds, and there are also retirement contributions. Then there are distributions from funds and dividends. During the month, I also:

• Traded futures successfully, increasing the allocation to "commodities" as a result. As mentioned above, I also added cash to the trading account. Just over 4% of net worth is now allocated to trading.
• Added another AUD 10k to the Winton Global Alpha fund, also increasing the allocation to commodities.
• Closed my investment in GMOM, due to poor performance over many years.
• Increased investments in the China Fund (CHN), Boulder Income Fund (BIF), and 3i (III.L).
• Sold my trade in Woolworths (WOW.AX) and made a quick trade in Platinum Capital (PMC.AX).
• Switched from Colonial First State Geared Share Fund to CFS Conservative Fund in a small account I have, which I am planning to close soon (after the end of June distribution). Then I switched back again. Originally, I had this account as a trading account!

Flipped Back to Short

The model was long NQ for one day and lost a little (it remained short ES, surprisingly). Now it has flipped back to short. Given yesterday's post, I'm still thinking this is a limited correction. Here is a possible interpretation based on Elliott Wave Theory:

We are now in wave C of 4. Based on Elliott Wave Theory that wave should stop before price falls below the maximum point of Wave 1, as shown on the graph. I find Elliott Wave very useful in understanding the different things that might happen, but I don't think it is an exact fit to what the market does, especially on very long and very short time scales. Over the time scale shown on this chart, it is particularly useful. On the other hand, Eliott Wave is notorious for continually morphing and following what the market does, rather than predicting it.

Of course, my model has nothing to do with Elliott Wave Theory it is just nice to have some other approach that does not conflict with the model or confirms it.

If you look closely you'll see I'm short from 6911.5 and up quite nicely, but I was up $500 when long yesterday evening too and that reversed...

P.S.
The downside didn't last long! Market turned around in the morning US time and went up, eventually reaching above the top of the triangle in the chart above. At one point I was up USD 1500, but unfortunately I didn't take profits as I was sticking to what the model said to do. Now I am considering doubling my position during the Australian daytime - the US overnight and then closing half in the US morning. If I had done that yesterday I would have ended up on the day. I am going to backtest the strategy of course. 10 years back when I previously was trading futures, I did look at "overnight trading" as a strategy, and now it has come up again.

Model has now flipped back to long. S&P model was short till today, and now has also gone long.

Getting Bullish

The model is switching back to long today. The last seven business days it was short but the market just went sideways more or less and it netted USD 1,200 a contract or 0.85% for the effort. The previous 10 days of being long, by contrast yielded USD 5k (3.67%). That's an indication of the bullishness. Australian and European markets have been more bullish throughout this period - the US market has been lagging perhaps due to relative over-valuation and to all this trade war and other nonsense.

So far for the month, the model is up USD 7k per contract and I am about matching that.

Backtesting 1987

You would want to make sure that your trading model put you in the right direction in the 1987 crash (which I am old enough to remember very well), wouldn't you? So, I backtested the model for 1986-87. The main model would be short going into the crash. But a more primitive model I am using in conjunction with the main model would switch to long on the Friday before the crash. That day the market went down 5%, so it would have already been a bad idea on the Friday. Recently, this secondary model has been doing well and I have combined its signals with my main model. So, we need some new rules about how and why to combine them. In this chart you can see that the buy signal would have come with the price already outside the +/- 2 standard deviations envelope (S&P 500 index):

These are "Bollinger Bands", though I use a 34 day moving average instead of Bollinger's 20 day MA. So, the new rule is not to take that signal when the price is outside the Bollinger Bands and the width of the Bollinger Bands is increasing. That wouldn't change much recently (NASDAQ 100 index):

The secondary model gave some very good buy signals just as price hit the Bollinger Bands in early February and late March. In these cases the price was not outside the Bollinger Bands or they weren't expanding.

The model is short for Monday.

How Big Should the Trading Program Be?

At the moment I am still in the experimental phase of the trading program. A 1 contract S&P or NASDAQ position either adds or subtracts about 0.1 beta to the portfolio. So if the beta of our portfolio to the market was 1.0, trading modifies this to 0.9 when short to 1.1 when long. My goal is to be able to hedge our portfolio against a market crash. That means we need to subtract up to 1 full beta from the portfolio. On the long side we then would double exposure. This means that the trading program needs eventually to be 10 times the size it is now. Using 3 times leverage on the cash in the trading account that implies allocating 25% of assets to trading. My existing allocation has 25% of assets allocated to managed futures. This total could be allocated between my own trading and "outside managers" such as Winton and meet this goal.

Why 3 times leverage? Simulation shows that about a 12% drawdown is possible. Remember that we use stops and or hedging to limit possible daily losses. So this drawdown means a string of large losses. With 3 times leverage that would wipe out 1/3 of the trading account. More than that and it will reduce the earning potential of the account too much going forward, I think. And be way too scary.

Formal Rules for Stops

I have decided on formal rules for setting daily stop losses. It is based on the pivot-point method. The pivot point is the average of the high, low, and close for the previous day. When short the stop loss is set at the second resistance level - the pivot point plus the previous day's high-low range - and when long it is set at the second support level - the pivot point minus the previous day's high-low range. If this results in a stop that is less than 1% from the opening price, I instead set a 1% stop. These stops increase the Sharpe ratio of the model though they slightly decrease returns. The chart below shows the last month of daily pivot points and first and second resistance levels:

The model got stopped out on 26 April when short - losing 1.42% that day. The market closed up 2.08%. So that saved 0.66% of losses The model also got stopped out on 3 May when long losing 1%. The market closed only down 0.02%. So that increased loss by 0.98%. That shows you why this reduces returns...

These numbers don't quite match what you can see on the chart as the chart shows the 24/5 futures market and the model is based on the NASDAQ 100 index. I am thinking of switching the model to use actual futures prices. Will need to pay for the data, I think.

Trying to Learn the Lesson about Narrow Stops Again

Yesterday the model said to go short NQ (NASDAQ) and long ES (S&P500). I started off the day, doing exactly that, though I entered the trade badly and ended up down on the NQ part of the trade relative to the ES part of the trade. Then, I closed the ES long for a small profit and based on "pivot points", I set a stop loss at 7010 - 50 points above my entry point. As you can see from the chart, the market briefly went through the stop but then turned and ended the day near where it started. So, I lost a lot more money than the model did. If I had set the stop at 1% (7030) or kept the hedge without stops, I would have ended the day with only a small loss. Really, it was fear of missing out on making a profit on the ES trade that screwed me up.

I also did a long CL (crude oil) trade yesterday and was stopped out too. Then the market also turned around and actually ended the day higher. The lesson I am learning from that one is to stick to the stock market for the moment so I can set wider stops and not worry about the potential loss from multiple positions going wrong.

The model is again short NQ and long ES for today. I was going to sit out for today, but writing this post has inspired me to put on that hedged trade. For the record, I sold NQ at 6969.75 and bought ES at 2729.50.

Weekly Update

Another successful week. Now up USD 8k for the month and USD 6k since the trading experiment started. On Friday I was long NQ when I should have been short. I only lost USD 150 luckily (though double that relative to what I would have got if I had done the correct trade). This was because of an error in a link in a spreadsheet. That link is now fixed. The model says short NQ (NASDAQ), long ES (S&P 500), long CL (Crude) for Monday. I think the short NQ is only a short term correction in NQ and probably it will switch back to long by Tuesday. It is a bit of an unusual feeling to see myself keep winning trades. I said to Moominmama that it felt like I was cheating or something. She said: "Please don't feel like that, please make lots of money :)". So far, this month I just have had to stay long, which isn't so easy for me as I tend to be bearish. And apart from Monday it looks like that staying long will continue to be the challenge for a little while till the model actually shifts to the short side. So, for the meantime we are still in phase 2 of this experiment, which is to see if I can stick to what the model says to do. Only, when we've been through both a long and a short phase successfully, will we be able to say that I think.

These 13F Tracking ETF's Have Horrible Performance

13F is a form lodged quarterly by US based investment funds. A 13F following strategy takes the stock picks from top hedge funds as revealed by their 13F forms. Two ETF's that follow this strategy are ALFA and GURU. But both have horrible performance with negative alpha of of -5% and -7%, which is rather ironic. Does this strategy no longer work?

Cracking Horse-Racing, the Lottery, and the Stockmarket?

Articles about Bill Benter who "cracked" gambling on horse-racing by using a model to predict which horses would win and Eddie Tipton who cracked the state lottery, illegally. I'm testing whether I've cracked the stock market :) So far, so good this month, but it is early days.  

P.S.
More on quant betting on horse-racing. Model remains long stocks (NDX and SPX) and switches to long oil for Monday. Yes, I added a model for predicting oil, so far I only did very quick trades in oil.

P.P.S.
More on Zeljko Ranogajec.

Fear of Missing Out versus Loss Aversion

The key to sleeping better in Australia while trading in the US markets seems paradoxically to be using wider stop losses rather than tighter stop losses. With a tighter stop, I am concerned that the market will hit the stop and then bounce back up strongly, which is what would have happened last night except I stayed up and adjusted the stop. This is the fear of missing out - crystallizing a loss and then missing the upside. I need to be more accepting of the possibility of large losses to allow the possibility of gains. I actually seem to have less aversion to losses if they aren't tied to then missing out on gains. FOMO seems to beat loss aversion. This is because my trading model has a high win rate. Traders with techniques that have a small edge or no edge have to make sure that wins are bigger than losses - letting winners run and cutting losses. They need the asymmetry to make money. I don't.