Some models are wrong in a trivial way. They clearly just don’t agree with real financial markets. For example, an option valuation model that includes the return of the underlying as a pricing input is trivially wrong. This can be deduced from put-call parity. Imagine a stock that has a positive return. Naively, this will raise the value of calls and lower the value of puts. But put-call parity means that if calls increase, so do the value of the puts. Including drift leads to a contradiction. That idea is trivially wrong.
Learning is hard. Sometimes it is hard in a trivial way. It can be hard because the subject matter is hard. Quantum field theory is a difficult subject and it is understandable that learning it won't be easy. However, learning is also hard for a more concerning reason. Humans don't change their opinions when confronted with facts, even when they directly contradict the opinion.
Effective learning from experience requires two things: practice and feedback. In trading we can’t do a great deal about the frequency of trading (practice) but we can make sure the feedback we get from our trades is relevant and valuable.
This is part II in our series of how to understand and manage risk in trading.
“Risk depends on the skills of a specific trader and is all of the things outside our control. So traders with different sources of edge will have different remaining risk factors. If I have an edge in volatility prediction and you don't, volatility is my edge and your risk. This is true in most of life. For a heart surgeon, doing a bypass is a low risk operation. For me, it would be murder.”
The difference between mean and median is known by anyone with a high school education. But a lot of traders, particularly option traders, seem to misunderstand what it means in practice. Any trade with edge needs to have a positive expected value, which is the mean return. Unfortunately, if your trade has highly skewed returns you are much more likely to observe a median value than a mean value.
This is part I in our series of how to understand and manage risk in trading.
“It is the focus on risk that separates good investors from poor ones. No matter how good your edge is, if you go bankrupt through poor risk control you won't be able to capture the money. Successful trading is a long term game. “
I've gone from thinking that categorizing strategies was useful to thinking it is essential. Dividing trades into inefficiencies or risk premia helps when deciding how aggressive to be in sizing and also how suspicious to be about its decay in effectiveness. I've found the model based or situational dichotomy helpful with sizing decisions.
There are two ways for a trading idea to be big enough to make it worthwhile. It can have depth or breadth. Index options have depth but there aren't many indices to trade. Equity options have more breadth. There are currently over 3000 companies with listed options and, although liquidity drops off fast after the top few hundred, equity options are worth trading.
It is well known that investors have limited attention. Every day they have to consider an enormous number of potential investments and it is just too much to take in. But there hasn't been much research showing how traders and investors react when they are reminded of particular firms (reactions to earnings announcements have been very well studied but that is a situation where people are watching the news on their own and don't need an external reminder). But now a recent study has shown that even a very quick attention grabbing prod can produce a surprisingly large effect.
Recently John Cotter and Niall McGeever posted an interesting paper to ssrn.com. They studied the persistence of nine anomalies in the U.K. equity market. They found a general decline in the profitability of these factors from 1990 to 2013. On average, the annual excess return dropped about 50% between 1990 to 2001 to the 2002 to 2013 period. Eight of the nine factors decayed. Momentum disappeared completely.
My new book has a section on specific trading edges. Vol of vol gives one such edge. I’ll summarize the effect, give references and a suggested strategy to monetize the edge. The “confidence factor” is 1 to 3. 3 is something that I have high confidence in. Confidence is based on the amount of empirical evidence and the plausibility of a reason for the effects existence.
The traders’ concept of the Efficient Market Hypothesis (EMH) is, “making money is hard”. This isn’t wrong, but it is worth looking at the theory in more detail. Traders are trying to make money from the exceptions to the EMH, and the different types of inefficiencies should be understood, and hence traded, differently.
2018 hasn't been a good year for volatility funds.
In February there was a feedback driven catastrophe in the implied volatility space. The details are less important than the result. A lot of volatility funds lost more than 30% in the month and one of the largest funds lost over 80% in a day.
Mid-term elections are not usually very memorable affairs. The voter turnout is much lower than for presidential elections (since 1960, voter turnout has averaged around 40% versus 60%). It seems likely that voter interest is atypically high this year, but that isn't the point of this post.
In my last post I discussed some of the hazards of assigning numerical measures to hedge fund performance and suggested that keeping things simple might be the most robust approach. Here I’m going to write about evaluating the business side of a fund.
A siren was a mythological being who lured sailors with their enchanting music to shipwreck on the rocky coasts of their island. Their songs were almost impossible to resist.
But more generally a “siren” is a bad thing that we are attracted to, either physically or psychologically.
For investors, an example of a siren’s song is simplicity. Many investors are prone to looking for just a few metrics to evaluate a fund or a strategy. This is a problem. It is a problem for me because my answers to reasonable sounding questions will be incomplete. “What is your Sharpe ratio?” sounds reasonable enough. But the answer on its own is close to meaningless without a much more detailed elaboration. What is the sampling error around the point estimate? How constant has it been across sub-periods? How distorted is it due to the shape of the full return distribution?
On April the 24th, the Wall Street Journal ran a story detailing the huge move of the VIX index in the opening 30 minutes of trading on the previous Wednesday. The VIX spiked up 12% in 30 minutes despite the underlying S&P 500 index not moving much at all. To put this 12% move into perspective, the current expected move in a 30-minute period is less than 2%. In addition to being a large move, the timing was suspicious. It was during this period that the settlement price for the April VIX futures was calculated. Anyone holding a long futures position would have benefitted greatly, at the expense of the shorts.
The equity market hasn’t really decided what it wants to do. However, we are now very clearly in a new volatility regime. From the start of 2017 through to the end of this January, realized volatility was 6.9%. In February and March, it was 23.6%.
Below I show the S&P 500 from the start of 2017 until the end of this March.
Following on from the last post we will now look at how the concept of adversity can lead to the variance premium.
Let’s think about a $100 stock and the $100 strike call and put. Assuming no interest rates or dividends, and a volatility of 30%, both the one-year call and put will each be worth $9.92. One trader sells the put, and another buys the call. Now let’s look at two price paths for the stock.
Recently I’ve come to believe that we don’t know nearly as much as we think we do.
Specifically the history of markets is nowhere near as big as we often assume. For example, equity options have only been traded in liquid, transparent markets sine the CBOE opened in 1973. S&P 500 futures and options have only been traded since 1982. The VIX didn’t exist until 1990 and wasn’t tradable until 2004. And the average lifetime of a S&P 500 company is only about 20 years.