AI in Portfolio Management

We give credence to AI and are convinced it will somersault the entire asset management industry. Artificial intelligence will be integral to active Portfolio Management in the coming years. The reasons are simple: AI can use large data sets to identify non-linear trading opportunities. In addition to consulting with a good Portfolio Manager, artificial intelligence is unsurpassable.

Use of Alternative Data

Our data gremium has led to a novel conceptualization of previous fundamental data. This process has led to unique feature engineering, utilizing over 2,000 statistically significant variables in our advanced deep learning models. This approach ensures that our strategies remain innovative and ahead of the competition.

Leading Inhouse Research

Qnology AI Ltd is known for its innovative research approaches. It replaced its trading algorithms' usual Recurrent Neural Networks architecture with an internally newly developed Convolutional NN architecture for time series analysis. This resulted in substantial performance gains while maintaining stable forecasting accuracy.

Frequently Asked Questions

The following questions and answers will give you a brief overview of Qnology AI.

1. What is the virtue of utilizing artificial intelligence in active asset management?

Utilizing artificial intelligence (AI) in active asset management offers a transformative approach to the financial sector and Qnology’s goal is to utilize the technology to its full extent. AI's capability to process vast, diverse data sources enables comprehensive asset evaluations beyond traditional methods, revealing hidden, non-linear financial relationships. This leads to superior portfolio management, with research showing AI-driven portfolios outperforming their human counterparts. AI's speed and pattern recognition in trading allow for optimized high-frequency trades, capitalizing on minor price discrepancies. Additionally, AI enhances risk management by precisely forecasting market events and credit risks by analyzing unconventional data. Robo-advisors, backed by AI, democratize financial advice, offering cost-effective solutions tailored to individual investor preferences. Importantly, AI ensures objective, emotion-free decision-making, continuously refining strategies as new data emerges. AI's integration in asset management elevates efficiency, precision, and adaptability.

2. How does Qnology employ artificial intelligence in its research?

Qnology harnesses the power of artificial intelligence in its research to bring innovation and cutting-edge solutions to the asset management sector. The company believes AI will revolutionize portfolio management, particularly by identifying non-linear trading opportunities within vast datasets. To achieve this, Qnology's data team has reimagined fundamental data, creating unique feature engineering. This process involves utilizing over 2,000 statistically significant variables in advanced deep learning models, ensuring the firm remains at the forefront of the industry. Furthermore, Qnology's innovative research approach is exemplified in its shift from the traditional Recurrent Neural Networks to an internally developed Convolutional Neural Network architecture specifically for time series analysis. This shift has resulted in significant performance gains while maintaining a stable forecasting accuracy. Through these methods and its commitment to AI, Qnology provides its clientele with unparalleled insights and analytics, particularly within the hedge fund industry and alternative investment strategies.

3. How is alternative data deployed and how does it differentiate from “traditional data” from Bloomberg?

Qnology places a strong emphasis on the use of alternative data in its AI-powered research and algorithm development. This data gremium offers a fresh perspective by reinterpreting prior fundamental data, resulting in unique feature engineering with over 2,000 statistically significant variables. Unlike the "traditional data" from platforms like Bloomberg, which predominantly focuses on standardized financial metrics, historical stock prices, and conventional financial reports, alternative data dives deeper, by encompassing a broad array of information sources, such as text sources from news articles, balance sheets, social media posts, market-driving factors and real-time sentiment analysis using machine learning. By harnessing this wider spectrum of data, Qnology AI Ltd ensures its strategies are not only innovative but also remain ahead of the competition, enabling the identification of non-linear trading opportunities and novel insights that might be overlooked by traditional data sources.

4. Who are the typical clients of Qnology and how do the offered services reinforce their investment strategies?

Qnology primarily serves professional investors, hedge funds, asset managers, and financial institutions aiming to embrace the power of artificial intelligence in asset management and gain in-depth insights into the broader hedge fund landscape, including its regulatory framework. These clients benefit from Qnology's comprehensive services which include up-to-date data, in-depth reports, and professional articles. Collectively, Qnology’s offerings fortify their clients' investment strategies by blending traditional financial expertise with cutting-edge AI-driven insights.

5. What is the associated fund solution mentioned on the website and how is it affiliated with Qnology?

The associated fund solution refers to a unique collaboration between Qnology AI and an Alternative Investment Fund Manager. This collaboration integrates Qnology AI's strengths in algorithm development and consulting directly into a regulated hedge fund structure. This fund harnesses advanced AI algorithms to refresh and optimize conventional investment strategies. Its main objective is consistent growth across varying market conditions, underpinned by a total return approach that covers a spectrum of publicly traded securities.

6. Who is Alan Koska, CEO at Qnology, and how does his prior experience with artificial intelligence in asset management help him in this role?

Alan Koska is the CEO of Qnology as well as portfolio manager and executive director for The Great People Fund (TGPF). His professional background is deeply rooted in the asset management industry, with a specific emphasis on developing quantitative investment strategies using sophisticated deep learning algorithms. Before leading Qnology, Mr. Koska was a part of the asset management division of a prominent global insurance company, focusing on data analytics. He also lent his talents to a leading hedge fund, specializing in quantitative investment strategies. This blend of classic investment strategy expertise and proficiency in the evolving domain of AI positions him uniquely, enabling him to spearhead Qnology's innovative AI-driven initiatives in asset management.

AI-Driven Total Returns

AI Driven Total Returns

AI in Portfolio Management

Use of Alternative Data

Leading Inhouse Research

Frequently Asked Questions