Tata Mutual Fund Launches New AI/ML-Powered Quant Fund
We live in a technological era where machines and software are becoming essential tools empowering businesses, Tata Mutual Fund has launched an artificial intelligence (AI) and machine learning (ML)-powered fund using a proprietary quant framework — Tata Quant Fund. Tata Quant Fund is an AI-powered and ML-enabled open-ended mutual fund scheme. Once subscribed to the scheme – the subscription lasts for a stipulated period of time; upon expiry – the scheme requires repurchasing or resubscribing. On the 3rd January, Tata Quant Fund was offered for subscription, the New Fund Offer (NFO) closes on the 17th January and will reopen on the 27th January again. Rs 5,000 is the least amount of money required for anyone who wishes to subscribe and an initial invest of Rs 1,000 is mandatory. Subscribers can compete to acquire additional shares and increase their initial investment amount by multiple of Re 1. With the launch of the Tata Quant Fund, the company becomes the fourth one to enter this space. Similar to Tata Quant Fund, DSP Quant Fund and Nippon Quant Fund follow the S&P BSE 200 TRI (Total Return Intex) while Reliance Quant follows Nifty 100 ESG TRI. Tata Mutual Fund has built a reputation for being morally upright where investor’s interest takes precedence over the company’s own. Tata Quant Fund Outperform The Market The scheme looks to invest in equity or equity-related securities anticipating medium to high capital appreciation in the long run. The performance of the schemes is predicted by the AI/ML-powered engines. The quant fund engines are designed to analyse more than 20 years of market data and records, including the scheme’s behaviour, influence and relation with other schemes, and also its inabilities. The findings are then compared with the prevailing market and macro-economic conditions. The final outcome is free of human bias, emotions or other inefficacies and is logically the most viable option for investors and subscribers to buy into. Tata Quant Fund utilises several Rule Engines which can be understood as if/then system where ‘if’ the required conditions are not met ‘then’ the system terminates the operation or keep it on hold. The AI/ML-powered engines take into consideration the procurement of maximum profit during a boom, or it focuses on minimising losses during a recession while formulating schemes and predicting the scheme’s performance. Each Rule Engine is capable of analysing market data of 30 companies individually and grading them accordingly. The AI/ML engines are designed to recalibrate itself at predetermined intervals to incorporate new and relevant information. This enables the AI/ML engines to predict future trends and developments in the mutual fund market. The algorithm, behind the AI/ML engines’ analysing and predicting capabilities, is developed by data science specialist from within the Tata Group. “Machines have massive computational power needed to process very large data sets, spot patterns and correlations, make decisions faster, objectively and without human biases. In the current world, computers are powerful enough to solve problems, a lot of data is available and we strive to use this data in combination with algorithms to its best,” comments Prathit Bhobe, Managing Director and CEO of Tata  Mutual Fund.
We live in a technological era where machines and software are becoming essential tools empowering businesses, Tata Mutual Fund has launched an artificial intelligence (AI) and machine learning (ML)-powered fund using a proprietary quant framework — Tata Quant Fund.  Tata Quant Fund is an AI-powered and ML-enabled open-ended mutual fund scheme. Once subscribed to the […]
Tata Mutual Fund has launched its artificial intelligence (AI) and machine learning (ML)-powered quant fund — Tata Quant Fund.
["AI/ML"]
["data science", "Go", "API", "machine learning", "artificial intelligence", "programming_languages:R", "AI", "ML", "programming_languages:Go", "AI/ML", "R"]
["AI", "artificial intelligence", "machine learning", "ML", "data science", "R", "Go", "API", "programming_languages:R", "programming_languages:Go"]
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