Positional Option Trading by Sinclair

Introduction

Emphasis on Process.

"Many investors rely on methods that are either unproven or even proven to be ineffective. Few of these investors who keep records will see that they are failing, but rely on cognitive dissonance to continue to believe in their theory of how markets behave."

"Trading is fundamentally an exercise in managing ignorance. Our ability to judge whether a situation presents a good opportunity will always be based on a simplified view of the world, and it is impossible to know the effects of this simplification. It is impossible to understand the world. If you insist on thinking in absolute terms, everything has shades of gray. You won't learn much from this book if you aren't comfortable with this."

Process:

Identify the edge

Choose the appropriate option structure to monetize the edge

Size the position appropriately

Be aware of what you don't know

Chapter 1: A summary of options

BSM's main allure is its usefulness.

"Option pricing models don't really price options. The market prices options through the normal market forces of supply and demand. Pricing models convert the market's prices into parameters."

"The second reason to use a pricing model is to calculate a delta for hedging. Model-free volatility trading exists. Buying or selling the straddle gives a position that is primarily exposed to realized volatility. But it will also be exposed to the drift. The most compelling reason to trade volatility is that it is more predictable than returns and the only way to remove this exposure is to hedge."

If we pay the wrong implied volatility level for an option, we might still make money, but we would have been better off replicating the option in the underlying.

The choice of option structure and hedging scheme can change the shape of the P&L distribution but not the expectancy.

Chapter 2: The efficient market hypothesis and its limitations

(An edge is fundamental)

Many trading books propagate the myth that successful trading is based on discipline and persistence. This might be the worst possible advice. A trader without a real edge who persists in trading, executing a bad plan in a disciplined manner, will lose money faster and more consistently than someone who is lazy and inconsistent.

EMH is important as an organizing principle and is a very good approximation to reality, but it is important to note that no one has ever believed that any form of the EMH is strictly true.

EMH Paradox: Grossman and Stiglitz argue that perfectly efficient markets are impossible because if they were, traders wouldn't make the effort to gather information, so there would be nothing driving the markets towards efficiency.

Risk premiums versus inefficiencies

A risk premium is earned as compensation for taking a risk. If the premium is mispriced, it will be profitable even after accepting the risk. A risk premium can be expected to persist, as the counterparty is paying for insurance against the risk.

In contrast, inefficiency is a trading opportunity caused by the market not noticing something. An inefficiency will last only until other people notice it.

Differentiating a risk premium from an inefficiency can be challenging.

Alpha decay: The half-life of strategies that exploit inefficiencies is between 6 months and 5 years.

Sources of alpha decay:

An inefficiency being published
The cost to acquire or process information gets cheaper
Increasing liquidity

You need to be aggressive when you encounter inefficiencies because they won't last.

In the long term, values are related to macro variables such as inflation, monetary policy, commodity prices, interest rates, and earnings. These change on the order of months and years. Worse still, they are all codependent. A better way to think of market data might be that we are seeing a small number of data points that occur a lot of times. This makes quantitative analysis of historical data much less useful than is commonly thought. [Kris: “Thinking in N not T”]

The German philosopher George Hegel is famous for his triad of thesis, antithesis, and synthesis. A thesis is proposed, and an antithesis is the negation of that idea. Eventually, synthesis occurs when the best parts of the thesis and antithesis are combined to form a new paradigm. This is useful for describing the progress of theories. Behavioral finance seems to reconcile the EMH movement with the post-1980s alternative view that investors are not rational. A few examples:

Overconfidence
Availability
Self-attribution bias
Anchoring

Behavioral bias can be a useful lens, but it is hard to apply, prone to pop psychology, and misinterpretation. For example, behavioral finance studies individual decision-making, despite the fact that people do not make investing decisions independently of the rest of society. Everyone is influenced by outside factors. While progress has been made in the sociology of markets, the work has not yet been integrated into behavioral finance. We don't have any idea of how the individual biases aggregate in their net effect on market dynamics. Behavioral finance does not have a unifying theory that gives explanations of the current observations and makes testable predictions.

Behavioral explanations can still be used as part of a checklist for why an inefficiency might exist. Together with historical data and a theory of underreaction, one can have enough confidence that post-earnings drift is a real edge. The data suggests the trade, but the psychological reason gives a theoretical justification

There is a mention of how momentum is a robust phenomenon over various time scales and geographies. The specifics are almost a secondary concern ("do I use a 40-day window or a 45-day window?"). A hunter doesn't much care about the biochemistry of a duck, but they will know a lot about their actual behavior. In this regard, a trader is a hunter rather than a scientist.

Chapter 3: Volatility Forecasting

Model-driven approaches can suffer from being overly simplified. In the 1980s, collecting daily closing prices and calculating volatility was enough to gain an edge in the options markets. Now that this data is free and easy to automatically process, it seems like there is no edge left in the volatility arbitrage model. But there never was any edge in the model. The edge was in data collection and processing.

The GARCH family of models allows for mean reversion to a long-term variance. It is both an insightful improvement on naively assuming that future volatility will be like the past, but it is also a wildly overrated predictor. It does capture the essential characteristics of volatility — volatility will probably be whatever the historical long-term average is, but in the near-term, volatility clusters.

An ensemble prediction of volatility will usually outperform time series methods. The ensemble method should include implied measures.

Chapter 4: The Variance Premium

The equity index variance premium includes the following:

Skewness premium

Implied correlation premium

Volatility premium

Chapter 5: Finding Positive EV Trades

I appreciate how Sinclair attempts to categorize each source of edge as either a risk premium or inefficiency. He's also candid about the difficulty in categorizing some of them, but the thought process is useful to observe for understanding what kind of evidence he needs to sort the edge.

Confidence Level 3 edges (highest level)

Curve: roll up and roll down term structure

Options strategies based on stock fundamentals

In general, long vol on value stocks and short on growth stocks

Post-earnings announcement drift

Studies have shown that the drift occurs over a period of months

Within 9 months for small firms and 6 months for large firms
Between 1/7 and 1/5 of the effect happens in the first 5 days after earnings
Evidence suggests that the drift is a persistent inefficiency more than a risk premium

Confidence level 2 edges

The trade I call “renting the straddle”

Overnight effect and weekend effects (much of the index premium is from the overnight/weekends — I'd express this as the market assigns too much vol to the overnight). If you specify a variance calendar that assigns lower variance weights to a weekend or even zero, you can mechanically watch clean IVs increase as you head into the weekend, which will alert you to the fact that IV is getting expensive.

Excess premium in FOMC-event vol

Options with positive exposure to vol of vol are overpriced

Confidence level 1 edges

Earnings induced reversals (fading a runup ahead of earnings)

Pre-earnings announcement drift

Chapter 6: Volatility Positions

The tradeoff between win rate and skewness of returns is evident in return simulations.

Selling straddles has a payoff that is less sensitive to extreme moves or to making a poor forecast relative to strangles. It won't be as profitable as often, but it also won't go as badly wrong.

In terms of risk and reward, the butterfly is to the condor what the straddle is to the strangle: a lower winning percentage but higher upside and lower downside.

Calendar spreads have a similar payoff diagram to a butterfly at the expiration of the front month option. [Kris: This intuition can guide one’s thinking about how the skew in one month relates to the slope of the term structure]

A method for choosing the strike to sell: choose the strike that has the greatest dollar premium to a flat vol surface (as opposed to the highest vol, which will correspond to a low premium option)

There is no "best strategy”. The choice is a matter of personal risk preferences. When choosing a short strike, the trader needs to balance receiving the most edge with the amount of risk that this produces.

Chapter 7: Directional Options Trading

The main advantage of options is the ability to speculate on a more nuanced view than just "Up or Down."

This chapter assumes a trader has a view on the underlying and shows how to use options to monetize the view. Having a view is the hardest part, but it's still possible to fail even if you're correct.

One approach is to reprice the options with a user-defined drift and just use the interest rate as the discount factor for the option prices.

Intuitively, the highest delta options are going to be the most mispriced and the best choice if you want to buy a fixed number of options, but the lower delta strikes will give you the most bang for your buck if you want to invest a fixed dollar amount.
"Best" or "optimal" is only with respect to a given criterion, and trading decisions need to be based on more than just one criterion.

Chapter 8: Directional option strategy selection

On risk reversals and skew trades:

Despite views to the contrary, skew trades are not particularly useful for speculating on the movement of the implied skew itself. The fluctuations in implied skew are dwarfed by the effects of stock movement and the level of implied volatility. [Kris: I've said this before and strongly agree. Sinclair offers a math justification based on the order of magnitude comparing Greeks]

Skew trades might make more sense with longer-dated options that have more Vega and less gamma, but the skews are also more stable. It's possible to make money with this trade, but the edge is likely overwhelmed by noise.

Ratio trades have all the same problems mentioned with skew trades and are an even worse vehicle for trading. An idea that isn't very good to start with. [Kris: In my opinion, risk reversals and ratio spreads are “path trades” and should be framed as bets on spot/vol correlation. In fact, a great demonstration of this point is the challenge of calibrating your delta in the presence of strong spot/vol correlation).

Actually, buying the ratio to sell the single skewed option, which will have a higher skew premium in dollar, not vol, terms could be a better expression of the skew trade!

Chapter 9: Trade Sizing

The reason why everyone doesn't use the Kelly criterion for sizing includes different goals than maximizing the long-term growth rate of the bankroll — a different utility than a log function, the best bets might be uncomfortably large, and portfolio volatility and drawdowns may be significant.

The chapter delves into Kelly when a distribution is non-normal or skewed. The key insight is that the optimal betting fraction might lead to a negative growth rate, so we need to consider the volatility of the optimal betting fraction as well to estimate the chance that the prescribed betting fraction leads to positive returns.

It can be wise to use Kelly plus a stop. However, stops are complicated:

Many trades that would have been winners will have been stopped out, so it is not as simple as assuming that you are just cutting off the left tail of the distribution. You would need to know how many trades would cluster at the stop threshold [This is a question of path].
Simple simulations show that the expected value of a strategy will fall if you use a stop, although you shed the large losers.
Trailing stops cost even more than a fixed stop because they are always in play, as opposed to a fixed stop which gets further away.
Stops don't just stop losses. They drastically change the shape of the return distribution and can lower the average return. Adding stops won't transform a losing strategy into a winning strategy. The only reason that we would add a stop is that we prefer the shape of the stopped distribution.

Stops make more sense if we are trading momentum and less sense if we are trading mean reversion.
A position should be exited when we are wrong. Sometimes this will coincide with losing money. In this case, a stop is harmless. But sometimes losing money corresponds to situations for which we have more edge. Here, a stop is actively damaging and contrary to the idea behind the strategy. [Fully agree and why I believe in risk rules that are independent of P/L for option trading and more thoughtful of ex-ante risk shocks].

Chapter 10: Meta risks

These are the biggest risks, the non-market risks. [Kris: I totally agree…this is what keeps traders up at night.]

Amateur traders lose money due to practically every decision they make. They should be able to manage their market risk so that no single loss will be catastrophic. The risks that professionals should be most concerned about are those created by political instability, contract spec changes, the stability of financial institutions, and fraud. These can never be totally avoided.

Examples:

Currency debasement

Theft and fraud

Unexpected changes to an index (that could result in changes in cost of carry assumptions or future volatility, which would affect outstanding options)

Counterparty risk

Appendices:

Adjustments to the BSM

Statistical rules of thumb

On execution