Trade Using Market Regimes?
Interest in trading multiple strategies via predictive market regimes, henceforth referred to as regime trading, is exploding since the teaser Quantivity post on a Market Regime Dashboard.
A quick poll of readers to help tailor future Quantivity posts: are you interested in regime trading, including any of the following: analysis, algorithms, full systems, and/or daily trading lists—along the lines introduced in the following posts and papers? If so, please comment or reply privately (all replies will be kept strictly confidential).
Neutralizing Equity Exposure
An equity trader recently posed the following question:
Given an equity position, how can I “borrow” some part of the capital tied up in that position to undertake another short-term trade without liquidating the equity position.
This question boils down to hedging, and thus is interesting as its solution is relevant to numerous other trading and investment challenges—and one which many retail investors may underappreciate its usefulness and broad applicability:
- Derivatives: use of leverage without being directional speculation
- Portfolio management: constructing an alpha overlay (i.e. any short-term trading strategy) on a standard beta index strategy (e.g. S&P 500 via SPY)
- Employee stock options: hedging for unvested or non-exercised employee stock options (ISO or NQO)
- Tax avoidance: satisfy holding period to qualify for long-term capital gains
To answer this question, start by considering a position composed of a single stock: say, long 100 shares of GE.
Coming of Retail Exotics
An increasing number of quantivity trading strategies are incorporating exotics, either synthetically or via direct instruments. Alpha is easier to tickle out on exotics due to less transparency to the retail masses. Further, exotics tend to confound many classic trading strategies.
History of retail exotics is a comparatively short one, with two recent hits. CBOE VIX was a massive well-known success, introducing vol trading and volarb to retail (props to VixAndMore for pioneering the blogosphere on this topic). VIX was quickly followed by VIX futures, VIX options, VXX, VXN, VXZ, and their friends across the indices and vol surface. More recently, leveraged ETFs and their derivatives have been a similar success story (as discussed previously here in Leveraged ETFs and Market Close).
Reality Intrudes
One of the great fantasies of financial economics is the Efficient Market Hypothesis, originated posited by Bachelier and popularized by Fama. Although this may be true in some very limited asymptotic statistical sense (which arguably is what Bachelier, but not Fama, intended), it certainly is not true from the perspective of any individual trader—or even, the bulge bracket, it now appears. What is particularly interesting for traders is understanding why this hypothesis is not correct, as the underlying factors evolve over time (with commensurate opportunity for profit, as proved nicely by Swenson and Birnbaum with ABX).
Market Regime Dashboard
The previous post, and excellent attendant reader comments, posited that effective quantitative trading needs to dynamically adapt to context. Yet, one of the most difficult systemic quant problems is identifying relevant “context”, how to measure it, and how to visualize it.
All too often, people focus exclusively on analyzing raw price charts for context (technical analysis is particular guilty). This is unfortunate, as basic time-series analysis offers to tickle out and visualize myriad richness otherwise invisible.
Naïve Backtesting is Bogus
The most frequently cited conventional wisdom of quant trading is backtesting, often summarized as:
Wise traders do as much backtesting as possible before starting to trade a system with real money.
Unfortunately, this wisdom is bogus. More accurately, this wisdom is bogus when practiced according to the standard backtesting formula:
- Indicator: choose indicator (whether fundamental, technical, or statistical)
- Data: choose long panel of data for some instrument (usually as much data as possible)
- Backtest: build strategy by optimizing entry and exit, given indicator, over data panel
- Profit!
Yes, undoubtedly some traders find short-term success with this formula. This is actually inevitable, due to the infinite monkey theorem: enough traders doing enough data snooping on powerful computers will inevitably result in a small number of them discovering what appears to be successful strategy due to pure randomness.
Regime Change Tests for Mean Reversion
An institutional algo trader recently posed the following question:
Q: So I have data that is mean reverting, but then the mean changes…What are the best tests for me to see when that is happening?
As always, there is no single “correct answer”. Answering this question depends upon broadly generalizing the previous post on Quantile Stability.
Applicable detection methods depend upon the interpretation of “mean changes” in this context. Factors to potentially consider in this context include:
- Signal origin (type and construction)
- Model linearity
- Discontinuity form (linear or non-linear)
- Desired detection frequency
Quantivity Feed / RSS
An increasing number of readers have inquired about feed / RSS support for Quantivity. Based upon popular demand, Quantivity is now available via feeds:
- Full: https://quantivity.wordpress.com/feed/
- Comments: https://quantivity.wordpress.com/comments/feed/
Feed links have also been added to the right rail on every page, as well as the About page.
If anyone has problems with these feeds, please comment.
Lever Options: Gamma Decay & Smiles
Levered ETFs are fascinating financial instruments. Derivatives on levered ETFs, termed lever options here (or, levers for shorthand), are even more interesting. As lever options are likely to be the first exotic to gain substantial exchange-traded volume, volatility traders should be salivating!
One of the most interesting aspects of levers is gamma decay (liberally inventing terminology, borrowing from the classic greek gamma):
Gamma decay is the decay in option value due to the leverage decay of the underlying levered instrument.
Stability by Quantile
Stability is one of the most important factors influencing consistency of quantitative trading algorithms. What works in a bull market does not work in a bear market; what works in trending market does not work in range-bound market.
Examples illustrating the importance of stability abound, with the most recent infamous example being discredited CDS pricing via copula methods (originated by Li in his classic article “On Default Correlation: A Copula Function Approach”, Journal of Fixed Income 9: 43-54). Turns out correlation simply is not a stable measure of coassocation. Other examples from quantitative trading include:
- Volatility: relationship and switching between market regimes
- Dispersion: relationship between index volatility and constituent volatilities
- Sector rotation: relationship among market sector, value vs. growth, and related factors
Stability is often evaluated using techniques from robust statistics (applied dynamical systems contributes additional techniques, often more familiar to mathematicians and physicists).
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