About us

We are focused on developing practical AI applications in challenging environments. ASK your daunting questions as we have the ability to harness research, data, and AI methodologies so you can explore the possibilities. We believe:

  • Constant innovation and invention keeps you ahead of the curve
  • Advanced AI removes the drudgery of even complex tasks to drive efficiency
  • Research should also be meaningful or it gathers dust
  • Engines should be powerful even if they’re under the hood
  • Visuals should be interpretable to facilitate more creative work

What can we do for you?

For starters, Artificial Intelligence (AI) is generally defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Machine Learning is a type of AI in which computers use huge amounts of data to learn how to do tasks rather than being programmed to do them. Deep Learning is a type of machine learning in which neural networks discover non-linear, complex relationships from vast amounts of data.

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We caution that for suitability, beyond the buzzwords, effective implementation requires knowledge of complex processes for appropriate models and tuning to the pertinent task. Our view is to balance the expectations for the use case depending on the skill/experience level and potential task complexity.

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Leadership

Satyan Malhotra, Chief Executive Officer
Dr. Ali Hirsa, Chief Scientific Officer

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Platform

Reimagine Asset Management via a platform based entirely on AI methodologies applied across the investment lifecycle: data mining, regime/signal detection, security selection, asset allocation, portfolio management, risk management, asset planning, product development, other.

As illustrated below, if you want another independent/un-biased datapoint for evaluation across the investment lifecycle, what would the AI screens be?

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As such, the platform supplements the Asset Manager’s experience-based, traditional portfolio decision processes with Artificial Intelligence-powered visualizations for,

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Engagements

Implementing enhancements can benefit you financially, procedurally, strategically and/or behaviorally. We can help quantify impacts and embedding our systems/processes within your infrastructure.

Asset selection: How much RoT is in your portfolio?
With numerous pitches, professed insights, optimizations, superior modeling, biases, et al how do you know which asset is actually in line with your target? Our system uses your positions to conduct the comparative analysis.

Our research – Explainability Index (EI) and Risk of Target (RoT) are proprietary, mathematically sound and easily explainable measures that do the comparative analysis.

Let us tell you how much RoT is there!

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Publications

We believe questioning status-quo hones expertise and yields improvements via pushing the boundaries of AI applications; even in non-stationary markets. And, we don’t believe in black boxes so we publish all our research.

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Insights

16 Feb
General

Robust Rolling Regime Detection (R2-RD): A Data-Driven Perspective of Financial Markets

The nonstationary and high-dimensional nature of financial markets poses significant challenges for navigation. Temporally stable regime classification offers a perspective to manage these challenges. We propose the Robust Rolling Regime Detection (R2-RD) framework that adaptively retrains with streaming data and employs temporal ensemble, label assignment, and threshold policies to address temporal instability resulting from nonstationarity, model mismatches, etc.  Since a learning-based model is only as powerful as the data it trains on, the more stable results of the R2-RD make it a better candidate for usage across AI-based applications.

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16 Feb
General

Robust Rolling K-Means (R2K-Means): an Updateable Nonlinear K-Means Clustering Methodology for Financial Time Series

K-Means is a popular clustering algorithm designed to group data points into k clusters. In the financial industry, grouping funds or assets can isolate behaviors and define investment universes using any number of  performance measures, holdings, or alternative features. Standard K-Means clustering at each time increment creates extremely unstable results due to the effects of random initialization and cluster mislabeling. Robust Rolling K-Means (R2K-Means) is the extension of K-Means to time series allowing investors to dynamically track and group funds in a stable and updateable framework.  Since a learning-based model is only as powerful as...

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15 Sep
ML/DL Journey

Mutual Funds – Historic Analysis: US Small Cap

Small cap mirage - running 16,500 investment strategy combinations over 20 years highlights the delusion of leveraging basic historical analysis for selecting small cap funds that consistently beat the index! Note that 20% of the funds reclassified themselves (some more than 4 times), there is marginal prediction power in performance measures (less than 1 in 5 chances), performance has struggled to beat the Russell 2000, Covid literally flipped the signals and yet, over the last decade the US Small Cap mutual fund market grew 2.5x!  For the US Small Cap mutual fund market, assuming the Russell...

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