Over short intervals of chronological time, prices are not the Gaussian random walks so revered by the Efficient Market Hypothesis but rather predictable artifacts of market microstructure. High frequency trading (HFT) is intimately related to market microstructural factors. And within that context, HFTs exploit inefficiencies derived from traders’ idiosyncratic behavior, enabled by their own asymmetric information. As a result, a small predictive power on a large number of independent bets yields a high Information Ratio and thus a profit.
Notably, HFT relies on machines that “think” in volume-time, not chronological time. In other words, HFTs monetize superior knowledge and accurate forecasts in transaction volume-milliseconds (“event-based time”). Algos buy low and sell high and neither hold onto losing trades nor hold winning trades until they move adversely. This nextSignals indicator was devised to identify informational asymmetry by modeling high liquidity in event-based time.
The predictive power of asymmetric variance premium is information-driven and typically reflects the activity of informed traders who place more transactions on out-of-the money options.
Among informed investors, as the predictive value of their forecast increases, the distribution of their long positions shifts to deeper out-of-the money (OTM) strikes. It is difficult, however, to accrue extraordinary profits based upon publicly available information unless traders closely follow the footprints of algorithmically-executed orders and possess superior trading skills. This nextSignals indicator uses OTM options market activity instead of price to predict short-term direction.
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