Market States: Regimes and Waves
There are many different types of market states–the longer term that are more aptly called “regimes”, and the short-term shifts in the market that I call short-term “waves.” Different types of waves can exist within the context of a regime. For example, we can have a brief wave of volatility bursts or a wave of “chop” within the context of a regime that is best described as an up trend with low volatility. Waves can be very brief–lasting from a few days to a week or more, while regimes often last a month and can last up to a full year. What I find interesting is that market states are always in the state of flux–one can even measure the likelihood of a regime or wave ending on the basis of historical precedent. This type of methodology is a part of what I like to call “state-based classification”, and the goal is to partition strategies according to their expected performance in both waves and regimes. Accurate state-based classification is extremely valuable, because if we know how a particular type of strategy behaves in a given market state we can “anticipate” how to adjust our portfolio allocation or position size. For example, it is well-known and logical to state that mean-reversion performs well during conditions of high volatility. In this case, if we can predict volatility, we can predict when to allocate more or less to a mean-reversion strategy.
Unlike conventional “swarm” or rotational strategies for adaptation–which reacts rather than anticipates—a clear sense of how to adjust for market states permits smoother transitions. You trade off one form of “error” for another: 1) “state adjustment error“: Swarm adaptation will be pushed towards what is working well with a fair degree of lag, and thus will adjust to “waves” incorrectly since they are transient. however, it should be able to adjust to regimes correctly as long as they persist for longer than one month. 2)”strategy classification error“: in contrast “state-based classification” faces the risk of incorrectly identifying the strategies most likely to perform well during a given wave or regime. I like to call this “strategy classification error” since you are making a judgement based on historical performance in advance and the actual strategy may not perform according to expectations during a given regime. This type of error is unlikely to be made by swarm/momentum type strategies since they will move towards what is actually working.
Again, it is logical to combine both procedures to enhance results. Using a swarm or relative-strength algorithm to confirm expected strategy performance within a given regime (or wave) is the penultimate form of “hypothesis testing.” You build a theory based on historical testing of a given strategy in a given state, and then once that state is confirmed to be present, you observe to see if the strategy is performing according to expectations in “reality.” Using anticipatory state-based strategies take this to the next dimension where you are predicting what states are most likely to occur and then looking to get earlier evidence from strategy performance prior to confirmation that you are in a given regime/wave. You can even bet a small amount in advance, and wait for confirmation so to speak.
Even if you are not creating an adaptive algorithm, these concepts are highly valuable for discretionary trading. After all, an algorithm is often designed to capture how a good trader with the objectivity and tools of a scientist would make a given decision. Thinking about these topics is a little less abstract for me because they relate directly to my experiences playing high level No-Limit Texas Holdem. Everything you do to be successful at a high level involves anticipation of how a given hand will play in a certain situation, a calculation of possible outcomes, betting in advance, and waiting for confirmation before going “all-in.” However, even for highly intelligent people—or perhaps most dangerously for intelligent people, they wish to trade or invest (and in poker too!) in some deterministic universe where the proper course of action is always clear-cut and logical. On the other hand the average person wants to always be right, and desperately seeks a formula that is reliable with an unreasonable degree of accuracy. They spend most of their time seeking new gurus or indicators that will solve their problems, and unfortunately tend to select what has worked best in a regime that is already past its prime. As a consequence, they are always behind the curve, and lose with an incredibly high frequency. Thinking about market states is a perilous but highly profitable path for the un-initiated. However, it is worthwhile considering how these concepts will inevitably affect your trading.