Machine Learning, AI, & FinTech in the Capital Markets by Sol Steinberg
The capital markets have changed forever, the machines are replacing decision making, order flow, risk management, valuation, and much more. Explore these changes and be prepared to add value in the transformation of the capital markets.
The AI impact on capital markets has never been profound as it is in the present times. AI has certainly taken the finance world, especially banking and investment services by storm. Artificial Intelligence is a suite that comprises of a set of tools- like machine learning, natural language processing, deep neural networks etc. that are impacting almost every industry, in the most efficient way. At its core, AI is essentially a set of technologies that are meant to augment or perform human tasks, without their intervention. Over a few decades, these technologies have evolved to sense, learn, comprehend, and act. Such a progression now enable systems and software to acquire, identify, recognize, and an analytical database (both structured and unstructured), derive insights, envision the process, and then put them into the real-time use cases. In context to capital market, AI is enabling the machines to do algorithmic trading, qualitative analysis, automate trade execution, and manage risk. What turns AI set a disruption in almost every industry is its decision making ability, which is based on cognitive learning. In contrast to perpetuating up on the programmed responses, AI overcome the limitations, complexities, and challenges by teaching the system to learn through past experiences.
Despite being the hot buzzword on Wall Street these days, machine learning is still fairly misunderstood. It is not artificial intelligence itself, but rather a form of it in which computers fed extremely large data sets are able to learn as changes in that data occur without being explicitly programmed to do so. The data is just one part of the approach, what can be more challenging is making machine learning and data science a core capability among companies so that they instinctively take internal and external data sets and interpret it for patterns, risks, and opportunities. Machine learning is shifting your trading counterparty to engineers and quants, it is critical you understand this evolution.
Downtown Conference Center
157 William Street
New York, NY 10038
Tel: +1 212 618 6990
Founding Principal, OTC Partners
Sol Steinberg: Founding Principal, OTC Partners
Sol Steinberg is a OTC Markets Subject Matter Expert and specializes in Risk Management, OTC derivatives, Market structure, Collateral, Trade Lifecycle, Valuation, Financial Technology Systems, Strategic development, and Monetization.
Sol is the founding principle of his firm, OTC partners. OTC partners is a boutique value add firm that specializes in research, content, development. Before starting OTC Partners Sol was a senior executive at the world’s leading clearing house LCH.Clearnet. Sol also spent nine years on the buy side and Citi, performing product development, risk management, and valuation for the OTC markets.
Sol has a wide-ranging network of asset managers, analytic providers, execution venues, regulatory, and government contacts. He used his eco system to successfully commercialize analytics, data, and other non commercialized intellectual property and had significant monetization success. He brought to market several initiatives, including institutional and commercial risk engines such as SMART tool, Risk Explorer, Global Market Risk System for Citi: the largest VaR engine in the world from 2004 to 2006, as well as developing CCP2 – a derivative education & certification program for leading consultancies. Sol also contributed to OTC industry’s clearing and default management policies for the cleared OTC swap markets as well as contributed to industry standard risk analytics in times of low market rates.
Waters Magazine’s award “Best risk analytics initiative 2012” & “Best risk analytics initiative (Sell Side) 2013”
FTF’s award for “Most cutting edge risk contribution 2013” for developing the SMART risk analytics tool.
Global nominee in 2012 for “Best Practices in Global Financial Risk Management” from PRMIA, Professional Risk Managers International Association.
Modern Market structure looking beyond 2020: The rise of alternative technology, marketplaces, and products such as exchange traded derivatives, and crypto currencies.
- Exchanges, Clearing houses, and Collateral
- Exchange traded & OTC derivative landscape
- Big Data, AI, and machine learning in trading, finance, and operations
HFT, Connectivity, & AI in Trading- Have we hit a wall? How competitors have reached critical mass
- Combating HFT? IEX launches HFT proof exchange, reviewing the offering and why it works and why it doesn’t matter anymore.
- Case Study: No more traders? How market leader JPM is automating almost their entire worldwide trading business – eventually
- Case Study: Hedge Fund Renaissance & Artificial Intelligence greatest success story in the Markets- How Renaissance’s Medallion Fund Became Finance’s Blackest Box
Big Data in the financial eco-system: Financial modelling, data governance, integration, NoSQL, batch and real-time computing and storage
- Infrastructure and technology
- New data sources
- Modern data analysis: Structured / Unstructured data and new models
Machine Learning Models: what is your best fit use in your business?
- Supervised learning, Unsupervised learning models, Reinforcement learning, Deep learning
- Machine learning in analysis: Momentum and Mean Reversion, Sentiment Analysis, Asymmetric Trading Strategies
- Machines at war (trading): Non Linear Multi-Factor Models, High Frequency Trading, Advanced Machine Learning
Machine learning in finance – Opportunities and challenges
- Algo-Grading 101, Interpretation
- Data mining biases: overfitting, survivorship and data-snooping
- Robust trading strategies: The future of machine learning in finance
1. Gain a profound understanding of what lies ahead of you in the rapidly changing Capital Markets driven by Machine learning.
2. Understand other new technology that is impacting the markets and what FinTech offering is next to impact the market.
3. Explore relevant risk factors of Machine learning that keep market participants up at night. Understand how to diagnose these risk factors in a volatile market.
4. Participants will be better equipped to understand the unique dynamics of the markets. How various factors push and pull, effecting other areas of the market. How automation, big data, machine learning, regulation, collateral management, and high-frequency trading have an impact on market conditions.
5. Participants will address Big Data, Machine Learning, Automation, FinTech solutions, Trading workflow, and Market structure.
Challenge #1: “I don’t understand what is driving Machine Learning in the market?”
Participants will come away from this class understanding not only market structure but the factors that are affecting technology in the market and what to expect in the future in terms of Machine Learning, Big Data, and A.I. affecting our ecosystem.
Challenge #2: “I don’t understand the new technology such as Big Data, blockchain, HFT, or crypto currency.”
After attending our course participants will have a deep and profound understanding of the technology behind the crypto currency’s blockchain, applications, as well as the current dynamics of the crypto currency trading environment. Participants will also learn what to expect in the future with this revolutionary technology.
Challenge #3: “I don’t have a clue on what is happening with the future of regulation, how has the market changed? Has it effected the technology we use?
Participants will not only understand market structure conditions but they will also understand market risk conditions that will affect the market going forward. Participants will have a profound understanding of the future of regulation, what it means to them in their business, and what to expect in terms of market conditions because of these changes.