The Forex (FX) market is shaped by liquidity fluctuations, which can cause return volatility and price increases. Identifying and predicting unusual Forex returns is important for risk management and trading strategies.
In this post, we explore two advanced approaches that allow investment experts to better understand and predict market changes. By integrating liquidity metrics with forecasting algorithms, investors can gain deeper insight into return behavior and improve risk-adjusted decisions.
The first approach focuses on outlier detection, where robust statistical methods separate periods with very large priced movements. These detected outliers are predicted using machine learning models informed by liquidity metrics, along with key macroeconomic indicators. The second approach directly targets the fluidity regime and employs a regime switching model to distinguish between hypofluid conditions and hyperfluidity. Subsequent return analysis within each regime reveals how risk is magnified in a low-liquid environment.
The patterns observed in the major currency pairs suggest that periods of liquidity decline are consistent with abnormal price behavior. Researchers like Mancini et al. and Karnaukh et al. We have demonstrated that liquidity risk, often measured through bid diffusion or market depth, is a factor in price. Rime et al. Others such as highlight how liquidity and information proxy can improve FX forecasting.
Based on these findings, there are two possible ways to tackle abnormal returns by leveraging machine learning methods and liquidity metrics.
An extraordinary return approach
Outlier
The first approach is to treat an unusual weekly return, i.e. outliers, as the main target. Practitioners collect weekly returns for various currency pairs and apply simple robust methods such as median absolute deviation (MAD) or more sophisticated clustering algorithms such as density-based non-parametric algorithms (DBSCAN) to detect outliers.
Once identified, these anomalous returns can be predicted by classification models such as logistic regression, random forests, or gradient boost machines using liquidity measurements (BID – ASK spreads, price impact, trading volumes) and macroeconomic factors (e.g. VIX, interest rate differentials, or investor investors). The performance of these models can be assessed using metrics such as accuracy, accuracy, recall, or areas under the ROC curve so that prediction power is tested from the sample.
Liquidity Regime
The second approach shifts emphasis on identifying the liquidity regime itself before linking to returns. Here, liquidity variables such as Bid-Ask spreads, trading volumes, or integrated liquidity proxy are sometimes fed into hidden Markov models to determine the state corresponding to high or low liquidity.
Once these regimes are established, weekly returns will be analyzed under the general regime, and shed light on how outliers and tail risks are possible during hypofluidity periods. This method also provides insight into the probability of transition between different fluidity states. This is essential to measure the likelihood of sudden change and to provide a deeper understanding of return dynamics. Natural extensions could combine both approaches by first identifying the fluidity regime and using a specific regime signal as an input function in a machine learning setup to predict or flag outliers.
In both scenarios, challenges include potential limitations on data availability, the complexity of calibration of high-frequency measurements for weekly forecasts, and the fact that administration boundaries are often blurred around macro events or central bank announcements. Analyzing emerging markets or currencies that normally trade lower amounts can cause different results.
Ultimately, the value of either approach depends on the amount and quality of liquidity data, the careful design of outlier or regime detection algorithms, and the ability to marry these with a powerful predictive model that can adapt to changing market conditions.
Important points
Navigating the volatility of the Forex market requires more than traditional analysis. Fluidity models and machine learning technologies can provide advantages in detecting and predicting anomalous returns. Through outlier detection or liquidity regime modelling, these approaches help investors identify hidden patterns that drive price movements. However, data quality, model calibration and macroeconomic events remain important issues. A well-designed adaptive framework that integrates liquidity dynamics with predictive analytics can enhance investment strategies and risk management in the evolving Forex market.
