Research

As practitioners, we are aware of the gap between available research and off-the-shelf solutions for financial professionals. With our research products, we like to narrow the gap, at least in some domains.

With access to our Dashboard, you can expect the most valuable insights we could find for discretionary investors who want to incorporate Quant tools into their investment decisions.

Try out a free test period.

Sentiment Analysis

At dcorr, we understand that the conversations, sentiments, and decisions of market participants are of critical importance in gathering meaningful insights. To this end, we have established an efficient process for collecting and filtering vast amounts of data related to these topics. Our advanced AI models then summarizes the topics, before a specifically trained AI model is used to assign a sentiment score to each subject, providing insight into the relevance of the topic to market participants.

Macro Regime

In our opinion, a vital insight for discretionary managers is understanding the current state of the overall market and whether it has shifted. To determine the market‘s regime, we apply the most sophisticated machine learning techniques and examine cross–asset correlations to identify the typical market behavior for each regime.

Machine Learning Nowcasting

Using machine learning, we try to determine the most probable path an asset will take during different market regimes. Additionally, we can estimate the duration of each asset in the regime, and continually update our nowcasting predictions based on this data. We believe this to be an invaluable tool for investors, as it provides insight into the behavior of assets compared to their history.

Global Asset Relationships

Using statistical tools and network analysis, we are able to gain a more accurate understanding of correlation behaviors across different market regimes. This helps investors to identify which assets are leading and lagging others, thereby providing them with a valuable insight into which assets they should focus on. Our approach to analyzing correlations is far superior to the naive method of simply looping back over a set period of time.

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