Examples of Akai Kaeru at Work in the Finance Industry
Akai Kaeru’s Explainable AI for Fintech Analysis
Our software can quickly identify the conditions under which features become important and then uncover the causal relations that connect them. First, our pattern mining engine identifies groups of data items with shared characteristics. These patterns are then passed on to our easy-to-use interactive Visual Pattern Explorer where analysts can inspect and act upon them. Use cases in Fintech include:
Furthermore, our correlation miner will help you determine:
Identify Key Drivers of Return in S&P 500 Data
We took a data set including stocks in the S&P 500 to analyze monthly returns from January to February 2016. The goal was to identify the key drivers of return during this period. The data included over 25 features describing different aspects of the stocks (e.g., sector, volatility, dividend yield, etc.). The sector categories include Financials, Healthcare, Materials, Industrials, Consumer Discretionary, Consumer Staples, and Utilities.
We used AK Miner to identify groups of stocks for which returns were unusually high or low. These groups were statistically robust and defined by simple criteria (e.g., low Volatility and high Dividend). We then switched to AK Visual Pattern Explorer to view and further explore this information, as seen in the figures below.
This is the same dashboard, now showing the Group Detail in the top right panel (selected by clicking the respective tab on the top). The three cells for each feature are colored by the number of stocks in the low, mid, and high value interval. It indicates the variable’s most dominant (if any) value interval in the chosen group of stocks. We can see that the values for Volatility are on the lower end, while the values for Dividend are on the higher end.
The green bar in the last column shows for each feature the strength of its effect on Return, which is positive for all features. If the effect was negative, it would be colored red.