Power of Sentiment Analysis to Improve Risk Management in Market Trading

Sentiment analysis helps market traders and analysts to spot potential risks by monitoring social media, news and industry releases for negative sentiment.

Effective risk management is a major source of competitive advantage for financial institutions and asset managers who seek to stay ahead of the competition.

As such, sentiment analysis has become a critical part of modern risk management, here’s why.

Overview of sentiment analysis

Sentiment analysis takes unstructured data, such as news headlines, earnings calls and social media discussions, and turns it into actionable quantitative insights. 

By doing so, it provides financial firms with the ability to anticipate, quantify, analyse and mitigate potential risks. 

Thanks to recent advances and innovations in the world of machine learning and natural language processing, sentiment analysis can now be scaled in real-time to offer market-wide insights, helping firms to detect market signals before they materialize in the asset price. 

This sentiment can be categorized as bullish or bearish.

Why sentiment analysis matters in risk management

Traditionally, firms have used indicators such as volatility indices and credit spreads to monitor risk exposure. 

However, these indicators often lag behind real-time events, only providing slow reactive analysis. In contrast, custom sentiment analysis solutions can provide real-time insights into how markets are interpreting developments. 

This not only provides the capability for faster reaction times, but empowers firms to make more informed risk responses. An example of this fast moving advantage was the 2023 banking turmoil that affected several US banks, namely Silicon Valley Bank, Signature Bank and First Republic Bank. 

Sentiment analysis data showed significant deterioration days before the equity markets reacted to the news. The sharp sentiment decline began with the relatively obscure regulatory filings from Silvergate Bank, something that traditional financial risk management models may have overlooked.

It isn’t just real-time information that sentiment analysis proves useful. It is also particularly valuable in identifying gradual, underlying risks that build up over time. 

In the run up to the Covid 19 pandemic, the global sentiment indicators showed a consistent decline weeks before markets reacted. Similarly, before the 2007-09 financial crisis, news-based sentiment analysis were showing steady deterioration in terms of financial sentiment, long before legacy methods of risk management such as volatility indexes such as the VIX.

How firms are using sentiment analysis

The early phases of financial sentiment analysis adoption was concentrated to the quantitative trading teams. Due to the rise in custom solutions built with user-friendliness in mind, it is no longer just the quant teams that can utilize sentiment data. 

Instead, fundamental analysts, risk managers, compliance, operation teams and hedge fund portfolio strategists can incorporate sentiment signals into their workflows. 

During earnings season, fundamental analysts can track the sentiment shifts tied to management commentary or the reactions from specific analysts. Risk managers can monitor for systemic sentiment indicators, which can in turn guide contagion strategies. 

This global, real-time capability is crucial for how interconnected today’s global economy is. Political developments in one region can have ripple effects on commodity prices, FX markets and cross border equity flows. 

It is through the adoption of real-time global sentiment analysis tools that firms can manage their risk and exposure to these seismic shifts before the wider market responds. 

Smaller firms and even individual analysts are able to access a high-quality calibre of sentiment tool that once were reserved for large hedge funds and investment banks using market sentiment platforms like StockGeist. This democratization of the access to these innovative tools is helping to level the playing fields and to widen access to the financial markets.

Integrating sentiment analysis into risk models

The true value of sentiment analysis lies in the ability to embed quantified sentiment data into wider risk models. The sentiment scores derived from news articles, filings and social posts can be used as inputs into portfolio optimization, stress testing and asset allocation models. 

Should the sentiment index identify a downward trend, this could be interpreted as a predictive signal for an upcoming market correction. As such, trading strategies could reduce exposure, rebalance portfolios and increase hedging as the sentiment shifts. 

Depending on the strategic decisions of the financial firm, these sentiment analysis signals could work independently or in tandem with other macroeconomic and technical indicators, helping to create a more holistic view of the market risk.

Closing remarks

The use of sentiment analysis within risk management is no longer optional, it is an essential tool for firms and analysts at all levels of market exposure. 

Whether providing a quantitative base to predict long-term shifts, or providing the real-time insights needed to react to breaking news, sentiment data provides a critical lens into the wider market psychology and behaviour. 

As the trading environment continues to evolve into a greater utilization of complex data, it is the firms who harness the full potential of sentiment analysis that will not only protect themselves from downside risk, but will also be able to position themselves to capture potential outside upside.

StockGeist.ai provides an innovative, interactive platform that monitors the current popularity of 2200+ publicly traded companies through textual information from social media. We offer real-time insights and guidance into stocks and cryptocurrencies through various dashboard features.