Banking Crisis and Its Impact On Market Liquidity

In this paper, we examine bank failures' impact on market liquidity.  We also introduce a systematic approach to quantify and measure liquidity shock induced by the failure of a liquidity provider. The magnitude of recent bank failures sparked financial contagion fear.  Total assets of Silicon Valley Bank and Signature Bank was US$319 billion or 85% of the total assets of the failed banks during the Global Financial Crisis of 2008. 

Please email contact@bestx.co.uk if you are a BestX client and would like to receive a copy of the paper, which is also hosted within our FAQ section of the UI.

Momentum & Range Regimes – Adding & Conserving Alpha

In our latest research article we expand the regime framework we have previously used to predict volatility and liquidity regimes, and apply it to price direction. Using a novel machine learning method, we categorise the market into Momentum (up or down) and Range regimes, and demonstrate applications of the regime predictions to both alpha generation, and also alpha conservation. The latter is done via the comparison of execution performance in the different regimes, with Range regimes generally demonstrating lower spread costs.

Please email contact@bestx.co.uk if you are a BestX client and would like to receive a copy of the paper.

Regime Change: An Update

Our latest research article is a follow up to the paper we published in April this year, where we introduced a framework for analysing and predicting regimes in the FX markets.

In this update we extend the research to predict regimes in terms of level, e.g. what is the probability over the next hour that USDJPY will be in a high volatility regime, whereas previously we were focused on changes in volatility. In addition, we publish out of sample performance results for a wider set of currency pairs, with the model having a predictive performance ranging between 70-90% for both volatility and liquidity regimes.

Please email contact@bestx.co.uk if you are a BestX client and would like to receive a copy of the paper.

Predicting Volatility & Liquidity Regimes using Machine Learning

There are many potential applications for trying to understand what particular state or regime a market is currently in, and more importantly, what regime is predicted.

For example, attempting to predict price momentum from an alpha or execution timing perspective, or predicting volatility and liquidity regimes to assist in execution decision making. At BestX, our regime research has initially focused on the latter and in order to provide a predictive component to the regime analysis we have employed the use of machine learning, a particularly hot topic in its own right with many different methods and approaches now available.

Rather than simply choosing the most complex sounding method for quantitative and intellectual satisfaction, we have conducted a rigorous study of 6 different methods to determine which is the most appropriate to help solve our particular problem of predicting regimes in volatility and liquidity. Interestingly, we found that the more complex deep learning/neural net methodologies were not as successful for regime prediction as a simpler classification method. This has reiterated the importance to us of ensuring you pick the right tools for the job.

If you are a BestX client and would like a copy of the research paper, please email us at contact@bestx.co.uk.