March 2020 market storm has been an important test of the I-System model and the way it navigated the unforeseen events. The results have been very encouraging and the system has done well in all affected markets. Last week I summarized its performance on Brent crude oil, Silver and US 30-year Bond. Here we take a look at how it performed in Russell 2000, S&P500 and Palladium markets. Continue reading
In the near future, we are likely to experience severe consequences of three converging disruptions:
- Stock market crash
- Oil price shock
Since the last recession we’ve enjoyed the longest ever period of economic expansion with low interest rates, low inflation and subdued commodity prices. But this all could be coming to an end.
Bursting of the “everything bubble”
Throughout the west, unprecedented government and central bank stimulus programs helped inflate the current “everything bubble.” This is not a new phenomenon; monetary expansion always creates asset bubbles. The one thing we know is that without exception, asset bubbles ultimately burst. The examples are many and some of them made a mark in the collective conscious of entire generations, from the 1630s Tulip Mania to the 1990s dot-com bubble. Continue reading
“Yes, but how can your system know if XYZ happens and markets go haywire?” This is one of the two most frequently asked questions about systematic trading strategies I’ve used over the last 20 years. Most traders tend to rely on analyses of supply and demand fundamentals to form a judgment about future price changes.
My contention is that this simply does not work and I can make a strong case to back this up (see here, here or here). I can also offer evidence that my systematic approach does work (see here or here) even if I know nothing about the supply and demand economics of most markets I cover. This usually elicits the objection that my system can’t know if some XYZ event might happen tomorrow (recently, XYZ tended to refer to Trump tweets), upsetting the markets and rendering my strategies ineffective. Recent experience afforded me an (almost) perfect answer to this question (plus another important issue related to trend following). Continue reading
A few months ago, when reviewing our trades on US Treasury futures, I was so delighted, I drafted a bragging article titled “How we knew yields would collapse?” summarizing the results of our trading. That performance was entirely generated by my I-System model, first built in 1999. I still find myself awestruck that this works… We generated profitable trades through both the bear and the bull market in bonds, literally without needing to know a single thing about the market fundamentals. The trades were strictly based on the knowledge framework built into the system more than 20 years ago (by the way, our strategies are still generating excellent signals in those same markets). Continue reading
Last month I had the privilege of meeting with Jaran Rystad of Rystad Energy to discuss strategic cooperation between our companies. On the occasion, he gave me a rather detailed presentation of his firm’s energy intelligence database. I must say, in my 20+ years trading in commodities markets this is by far the most impressive product of its kind I’ve ever seen. Even from the software engineering point of view, I was very impressed. For full disclosure, nobody asked nor encouraged me to write this. Much as you’d recommend a restaurant where you ate well or a doctor you respect, I wholeheartedly recommend Rystad Energy as a provider of energy market intelligence as a matter of giving credit where credit is due.
However, even with top notch data on economic supply and demand fundamentals, divining the future remains difficult and unlikely. John von Neumann rightly said that forecasting was “the most complex, interactive, and highly nonlinear problem that had ever been conceived of.” Continue reading
- Trump Administration put their credibility on the line by taking a hard line on Iranian oil exports, pledging to collapse them to zero.
- Iranian officials matched the rhetoric by promising to close the Straits of Hormuz entirely to oil traffic. A third of world’s traded oil production transit through that choke-point.
- Assurances of ramped-up oil production from Saudi Arabia and Opec appear as firm as a wet noodle.
U.S. taking a hard line on Iran oil exports
Over the Easter weekend we’ve seen an escalation of Trump Administration’s rhetoric toward Iran. On Monday, 22 April, State Secretary Pompeo issued an official statement pledging that after their expiry on May 2, the U.S. would not renew any of the waivers enabling Iran to export crude oil. Iran’s oil exports have already dwindled from 2.5 million barrels per day last April to around 1 million barrels, and the official U.S. policy is to bring Iranian oil exports to zero.
In taking the hard line against Iran, the Trump administration has put its credibility on the line. Secretary Pompeo followed up the official announcement on twitter, stating that, “maximum pressure” means maximum pressure. Trump backed him up promising “full sanctions…”
- This week Sinopec disclosed the latest hedging mishap, losing $690 million amid last year’s oil price collapse.
- Unless price risk management is organized as an integral part of core business operations, it can devolve into eratic and risky game of speculation that can cause massive damage.
- The six simple but important guiding principles could help commodity firms create a world class risk management process and turn price risk into a source of value and competitive advantage.
This week Sinopec disclosed that it had incurred $690 million in losses in the fourth quarter of 2018. The losses were attributed to Unipec’s oil hedging bets. Unipec clearly took the wrong directional exposure to oil prices in the period when they staged a sharp, 40% collapse (October-December 2018). This much is understandable. However, such losses did not need to happen – I maintained heavy exposure to oil prices over the same period and not only avoided heavy losses but actually generated significant profits by simply adhering to a systematic trend-following model.
- In financial and commodity markets, large-scale price events are not predictable. Even so, most market professionals rely on forecasts most heavily in making forward-looking decisions.
- At times, this has disastrous consequences (see below)
- Large-scale price events are far and away the greatest source of external risk for commodity-related businesses. Their severity and frequency has been on the increase in recent years.
- An alternative approach to mastering uncertainty is to explore systematic trend-following strategies which, if used appropriately can turn price risk into a source of profit and hard to match competitive advantage
According to the latest Reuters survey, over one thousand energy market professionals expect the oil price to average between $65 and $70 a barrel in the years 2019 through 2023. Only 3% of respondents thought that Brent Crude Oil might increase above $90/bbl next year. So, market experts do not expect any surprises and largely agree that oil price will remain where it is. This groupthink reminds me of a similar situation some 15 years ago. Continue reading
Extreme price events are far and away the greatest source of external risk facing oil and gas producers and other energy-dependent companies. Frequency and severity of such events has been increasing dramatically since about 2005/2006 causing ocasionally severe pain for many industry participants.
Case in point was the 70% oil price collapse through 2014 and 2015, from over $100 to below $30 per barrel. In the aftermath of this decline, U.S. mining industry – which includes oil and gas producers – reported losses of $227 billion, wiping out eight previous years’ worth of profits as the following chart shows: Continue reading
The price of Copper has been trending significantly higher since the start of 2016. However, this trend has not been easy to trade using traditional trend following strategies.
This last event (D) was quite painful for most – if not all – trend followers, as the following chart illustrates: Continue reading