Study of instability factors that are endogenous to the financial market as a complex non-linear system is another form of fundamental analysis, not unlike the analysis of global macro imbalances or company balance sheet risk.
When investors speak about fundamentals they normally do not look at the information contained in the financial time series itself. This is because 'efficient-market hypothesis' (EMH) assumes that market prices are essentially random. There is no useful information in the financial time-series apart from a few linear statistics variables such as mean and (co)variance of asset returns. What really drives portfolio returns according to EMH is the new information that market participants receive unexpectedly, such as changes in company financials or new macroeconomic data. Professional investors hope to out-perform their peers by getting the news earlier than the rest of the market.
Unfortunately, by focusing on company news and macro analysis as the only driver of excess returns investors are left with precious little to actively manage their portfolios during times of 'market dislocation' (an euphemism for a 'fat tail' event, a larger-than-statistically-expected market price move). Traditional theory proclaims that such changes are caused by factors that lay outside of the model, so their prediction is not possible in principle. For example, nobody could have predicted the sharp drop in market prices last year. (Because nobody could have predicted the fall of Lehman Bothers, etc.)
Modern science has moved beyond Gaussian model of the market. Today we know that the market is better described as a complex non-linear system. It is essentially deterministic, not random, even though the system's complexity makes it impossible to predict the future state most of the time (and during this time a Gaussian model provides a passable approximation).
There are moments when market returns are influenced by inner dynamics of the system itself, not by exogenous shocks. You can imagine a situation when a small move in price triggers massive execution of hidden limit orders, a spike in option hedging activity and a 'bandwagon' behaviour on part of a trend-following crowd. The emerging trend will be amplified and will end up as a larger-than-statistically-expected directional move. Traditional investment theory ignores these 'fat tail' events as something outside the model. But modern complexity theory (on which ChaosMonitor algorithm is built) sees its mission in identifying conditions leading to such extreme events.
It has been established that power-law systems (to which financial markets belong) display certain observed log-periodic scaling patterns. When these patterns are broken one may reasonably expect the system to settle back in its normal behaviour soon. ChaosMonitor tracks two kinds of such anomalies.
The first one is called 'extension', and it may appear when the financial time-series displays a strong directional trend that is becoming unsustainable. The second one is called 'compression' and it appears when the market displays the extremes of lack of direction (lack of any positive feedback in price), when a small price move is likely to result in an emergence of a new trend.
To summarize, majority of extreme events that affect portfolio returns are emergent properties of the market itself, they are not a result of some external shock. Since we can calculate invariant properties of the market (and ChaosMonitor signals are a product of calculation, not an opinion of some analyst etc.) they belong to the realm of fundamentals. This may go against current industry and academic tradition, but recent financial crisis has shown what a consensus could be worth.
You can see academic references relating to studies of system self-organized criticality and log-periodic scaling on ChaosMonitor front page. There is also questions and answers secton that deals, among others, with where technical analysis fits in the picture.
Most importantly, qualified investors who use ChaosMonitor web service (it covers all major market instruments on time-scales from 15 minutes to daily and weekly) can conduct hundreds of real-time experiments and see how ChaosMonitor algorithm detects potential 'fat tail' events before they happen.
Let us illustrate how non-linear fundamental properties of market time-series can be useful for investor portfolio management. On the chart below we see two weekly signals that ChaosMonitor algorithm generated in 2008. The blue compression signal would normally precede a new trend. Assuming that a prepared investor was aware of downside danger for risk-bearing securities, the signal would provide an essential timing guidance. Later in the year an extension signal pointed at the end of the dramatic market fall.
When investors speak about fundamentals they normally do not look at the information contained in the financial time series itself. This is because 'efficient-market hypothesis' (EMH) assumes that market prices are essentially random. There is no useful information in the financial time-series apart from a few linear statistics variables such as mean and (co)variance of asset returns. What really drives portfolio returns according to EMH is the new information that market participants receive unexpectedly, such as changes in company financials or new macroeconomic data. Professional investors hope to out-perform their peers by getting the news earlier than the rest of the market.
Unfortunately, by focusing on company news and macro analysis as the only driver of excess returns investors are left with precious little to actively manage their portfolios during times of 'market dislocation' (an euphemism for a 'fat tail' event, a larger-than-statistically-expected market price move). Traditional theory proclaims that such changes are caused by factors that lay outside of the model, so their prediction is not possible in principle. For example, nobody could have predicted the sharp drop in market prices last year. (Because nobody could have predicted the fall of Lehman Bothers, etc.)
Modern science has moved beyond Gaussian model of the market. Today we know that the market is better described as a complex non-linear system. It is essentially deterministic, not random, even though the system's complexity makes it impossible to predict the future state most of the time (and during this time a Gaussian model provides a passable approximation).
There are moments when market returns are influenced by inner dynamics of the system itself, not by exogenous shocks. You can imagine a situation when a small move in price triggers massive execution of hidden limit orders, a spike in option hedging activity and a 'bandwagon' behaviour on part of a trend-following crowd. The emerging trend will be amplified and will end up as a larger-than-statistically-expected directional move. Traditional investment theory ignores these 'fat tail' events as something outside the model. But modern complexity theory (on which ChaosMonitor algorithm is built) sees its mission in identifying conditions leading to such extreme events.
It has been established that power-law systems (to which financial markets belong) display certain observed log-periodic scaling patterns. When these patterns are broken one may reasonably expect the system to settle back in its normal behaviour soon. ChaosMonitor tracks two kinds of such anomalies.
The first one is called 'extension', and it may appear when the financial time-series displays a strong directional trend that is becoming unsustainable. The second one is called 'compression' and it appears when the market displays the extremes of lack of direction (lack of any positive feedback in price), when a small price move is likely to result in an emergence of a new trend.
To summarize, majority of extreme events that affect portfolio returns are emergent properties of the market itself, they are not a result of some external shock. Since we can calculate invariant properties of the market (and ChaosMonitor signals are a product of calculation, not an opinion of some analyst etc.) they belong to the realm of fundamentals. This may go against current industry and academic tradition, but recent financial crisis has shown what a consensus could be worth.
You can see academic references relating to studies of system self-organized criticality and log-periodic scaling on ChaosMonitor front page. There is also questions and answers secton that deals, among others, with where technical analysis fits in the picture.
Most importantly, qualified investors who use ChaosMonitor web service (it covers all major market instruments on time-scales from 15 minutes to daily and weekly) can conduct hundreds of real-time experiments and see how ChaosMonitor algorithm detects potential 'fat tail' events before they happen.
Let us illustrate how non-linear fundamental properties of market time-series can be useful for investor portfolio management. On the chart below we see two weekly signals that ChaosMonitor algorithm generated in 2008. The blue compression signal would normally precede a new trend. Assuming that a prepared investor was aware of downside danger for risk-bearing securities, the signal would provide an essential timing guidance. Later in the year an extension signal pointed at the end of the dramatic market fall.
= large price move next
or 
= this price move ends