A PRACTICAL ALGORITHM TO DETECT SUPEREXPONENTIAL BEHAVIOR IN FINANCIAL ASSET PRICE RETURNS

To assist with the detection of bubbles and negative in financial markets, a criterion is introduced to indicate whether market likely be superexponential regime (where growth such would correspond an asset price bubble decline bubble) as opposed “normal” exponential behavior typified by constant rate or decline. The founded on Johansen–Ledoit–Sornette model dynamics derived from linear fit observed data nonlinear time transformation parameters distributed uniformly their permitted ranges. Making use expected values rather than underlying distribution, straightforward efficient compute can principle applied real intra-day markets well longer timescales. In some circumstances, shown have certain predictive qualities when portfolio stocks, could used input into algorithmic trading strategies. A simple strategy described which based reversion predictions stocks back-testing generates notable returns.