Ep. 26: Deep Learning Promises to Bring Algorithmic Investing Smarts to the Rest of Us
Jun 14, 2017
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Gaurav Chakravorty, co-founder and head of trading at Qplum, shares insights on democratizing algorithmic investing. He discusses how machine learning can empower everyday investors, breaking down complex concepts into accessible strategies. The conversation highlights the shift from traditional methods to advanced algorithmic trading, addressing challenges and transformative potential. Gaurav emphasizes the importance of transparency in finance and critiques high fees, advocating for deep learning as a game-changer that levels the playing field in investing.
Gaurav Chakravorty underscores the transformative power of machine learning in investing, shifting from intuition-based to data-driven decision-making for greater accuracy.
The potential democratization of algorithmic trading through deep learning could level the investing playing field, enhancing access to advanced financial tools for all investors.
Deep dives
The Role of AI in Finance
Artificial intelligence is well-suited for the finance and investing industry due to the vast amounts of data available for analysis. This environment allows algorithms to make sense of complex data sets and make informed trading decisions. Gaurav Chakravorty highlights how his journey with machine learning transformed traditional investing, especially emphasizing the shift from intuition-based decisions to data-driven approaches. His success with algorithmic trading, generating over $1.4 billion, illustrates the potential of AI to revolutionize finance by enabling more precise and rapid responses to market changes.
High-Frequency Trading Mechanics
Chakravorty describes his approach to high-frequency trading, which involves analyzing real-time data from the market to make split-second decisions. He utilized fundamental machine learning techniques like linear regression to develop models capable of predicting short-term price movements, trading effectively within timeframes of 30 to 600 seconds. This method allowed for the identification of short-term market inefficiencies, enabling trades based on subtle price fluctuations between stocks. His ability to trade millions of dollars daily shows the scale at which these algorithms can operate, making human intuition less relevant in high-frequency contexts.
The Future of Investment through Deep Learning
The discussion points toward deep learning as the future edge in investing, with the potential to democratize access to efficient investment tools. Chakravorty argues that unlike traditional methods focused on individual knowledge and expertise, deep learning could level the playing field for all investors by making algorithmic trading more accessible. He believes that as these systems become widely adopted, they will improve decision-making processes and reduce the need for conventional portfolio management methods. This shift toward collaborative systems in finance could transform how we invest, moving beyond competitive strategies to a more utility-based approach.
In recent years hedge funds have taken the lead in algorithmic investing - or robo-trading as it’s sometimes called. But there’s no reason the hedge fund world should have all the good stuff. In this episode of the AI Podcast, we speak with Gaurav Chakravorty, co-founder of qplum, a startup that’s working to bring that same machine learning investing approach to the rest of us.
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