Russell Tang@russelltangwl1
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Events attended
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United Kingdom
1 year of experience
About me
Recent BSc Artificial Intelligence graduate from the University of Manchester, now embarking on a new journey as an Analyst in GMIT (Markets) at Nomura. My academic background in AI, coupled with a keen interest in financial markets, has positioned me at the exciting intersection of technology and finance. At Manchester, I honed my skills in deep learning, natural language processing, and ensemble learning techniques, culminating in my final year dissertation on financial text sentiment analysis which has won the Hilary Kahn award for the best undergraduate project in my cohort. In my role at Nomura, I'm applying my technical expertise to support and innovate within the dynamic world of global markets. I'm passionate about leveraging AI and machine learning technologies to enhance financial processes, improve decision-making, and drive efficiency in market operations. I'm always eager to learn and collaborate on projects that push the boundaries of what's possible in financial technology. Whether it's discussing the latest advancements in AI, exploring new applications in fintech, or exchanging ideas on market trends, I'm open to connecting with like-minded professionals and innovators in the field.
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ProfitPilot
The project aims to develop an advanced AI-powered tool using the Gemma 2 language model to assist financial advisors in comprehensive portfolio management, market analysis, and client communication. The solution will leverage machine learning techniques to provide nuanced insights into portfolio performance, market trends, and personalized investment strategies. Key Problem Statement: Financial advisors face significant challenges in: 1. Efficiently analyzing complex market information 2. Correlating diverse financial data sources 3. Providing personalized portfolio insights 4. Navigating complex market news and its potential impacts 5. Optimizing portfolio performance across multiple dimensions Technical Approach: The project will utilize a multi-step approach: - Fine-tune the Gemma 2 large language model - Create a comprehensive training dataset - Develop custom data processing pipelines - Implement advanced financial metric calculations - Build an intelligent query-response system Core Capabilities: 1. Portfolio Composition Analysis - Detect direct and indirect stock holdings - Analyze asset allocation across stocks, ETFs, and mutual funds - Identify potential investment opportunities and risks 2. Performance Metric Computation - Calculate advanced financial metrics: * Sharpe Ratio * Beta * Price-to-Earnings (P/E) Ratio * Price-to-Book (P/B) Ratio * Momentum Indicators (RSI, MACD) 3. Market News Correlation - Interpret market news in context of specific portfolios - Predict potential impacts on individual client investments - Provide personalized market insights Data Sources and Integration: - Financial APIs (Alpha Vantage, potentially Bloomberg, Quandl) - Custom-generated training datasets - Historical market data - Fundamental company financial information
1 Dec 2024
