I attended the CSAF course from Jan 2022 to June 2022 and it gave a substantial boost to my career in Quantitative finance. The theory lectures a…
I attended the CSAF course from Jan 2022 to June 2022 and it gave a substantial boost to my career in Quantitative finance. The theory lectures and use case lectures were delivered by world-leading experts in this field covering a wide range of topics such as sentiment-enhanced equity trading strategies, portfolio allocation of institutions using Macro-sentiments, Crypto assets, and ESG Investments.
I particularly enjoyed the practical classes in which we could experiment with what we have learned. The practical class is quite interactive and the faculty helped me to implement a sentiment-enhanced portfolio allocation strategy.
A comprehensive and well-structured instructor-led course to understand the various aspects of trading and investment decisions using Sentiment A…
A comprehensive and well-structured instructor-led course to understand the various aspects of trading and investment decisions using Sentiment Analysis. The faculties come with impressive industry experience and the end of the session Q&A is very insightful. Would highly recommend the course to anyone who wants to explore the world of Sentiment Analysis in Finance.
I highly recommend the Certificate in Sentiment Analysis and Alternative Data for Finance (CSAF) course that I recently took. The course is taugh…
I highly recommend the Certificate in Sentiment Analysis and Alternative Data for Finance (CSAF) course that I recently took. The course is taught by Professor Gautam Mitra, who is a leading expert in the field and provides unparalleled insights into the world of algorithms and the latest thinking in financial technology.
The course is comprehensive and covers a wide range of topics, including trading, investment decisions, and the application of news analytics, sentiment analysis, and alternative data. As a finance professional, I found it to be an extremely valuable learning experience that has helped me to develop my career in modern methods in finance.
Additionally, the lectures by Dr. Cristiano Arbex Valle were particularly memorable and added a lot of value to the course. I also found that being called upon first in class alphabetically helped me to stay focused and set the direction for other students in the class.
Overall, I highly recommend this course to anyone looking to enhance their knowledge and skills in the field of finance.
EPAT has been a great experience for me. It is definitely the best programme out there to learn quantitative finance and algorithmic trading. The team was and still is very helpful and caring. The course itself is a combination of different disciplines including programming, finance, and statistics taught by very knowledgeable and experienced faculty.
I recently completed the EPAT programme from QuantInsti, and it was a rich experience. I learned more here than I did on my university curriculum. This is mainly since the EPAT course is very practical and I was able to learn a lot in such a short time. It provided me with a lot of theoretical and practical knowledge in the algorithmic trading domain. Besides their excellent curriculum, the support team is friendly, dedicated, and always there to support you during your EPAT journey. They also have a placement team that keeps you updated with career opportunities. However, keep in mind that your background will influence how well you fit into those career opportunities. They also have a self-paced learning portal named Quantra which I really enjoy. Overall, they are excellent at what they do.
QuantInsti is the best place to learn professional algorithmic and quantitative trading. The EPAT programme is a highly structured and hands-on learning experience and it's being updated frequently. The faculty and staff are extremely competent and available to address any concerns you may have. Upon completion of the EPAT programme you will have the necessary tools to begin a career in algorithmic/quantitative trading.
From basic knowledge of quantitative finance to practical hands-on python session of back testing trading strategies, EPAT course covers a large portion of knowledge needed to join algorithmic trading industry. Good introduction to dive in.
The Executive Programme in Algorithmic Trading (EPAT) is a well structured, intensive course which takes approx. 6 months to complete. The core focus areas of the course are stock market theories and quantitative principles, statistical analysis and programming. With the current trend of businesses moving towards implementing Artificial Intelligence (AI) or data-centric approaches to solving difficult problems, the skills gained from this course can be used to solve any AI-related problem (i.e. these skills can be used for any domain other than algorithmic trading). Having these skills in your repertoire will likely increase the probability of finding employment. Further, the Institute actively works towards the placement of the students enrolled (or alumni) in the course. The faculty are experts in their respective fields. In order to successfully complete this course, the student must be committed to completing the assignments and projects to cement their understanding of the course material. An added advantage is that there is lifetime access to the course materials, which will enable any alumni of the EPAT course to stay updated on the developments in this field. Overall, in my opinion, EPAT provides value for your money.
I found the EPAT course to be exactly what I was looking for – the right mix of statistics, financial markets and coding. The faculties were excellent, and most importantly, the support team was exceptional with their efforts towards my learning.
Your one-stop solution for the niche and opaque domain of Algorithmic Trading. Be it faculty, student support service, training content & resources or communication, they match the standards of international repute!
I had a great experience through QuantInsti Learning. If you are passionate about Algorithmic/Quantitative Trading, or you want to start your journey in this amazing discipline, this is a great place to begin and grow your knowledge and interest. The administration and faculty were outstanding. Lectures are well-delivered and informative and there is always additional help should you require it. There is a wide array of learning material both through coursework and through the community as a whole.
Only great words to say about QuantInsti and my learning path during the EPAT programme. Always curious, always listening and improving. All the staff, starting from the CEO down to the support people were very nice 120% of the time (the 20% excess goes to all the help that they have given me after concluding the course, every time with a consistent will to help others).
Regarding the EPAT programme content, the key thing I would like to say is that is a wide covering approach. During six months, industry experts (i.e. real practitioners, not Gurus) dive into a variety of topics from scratch, so that, after that, you can choose in which field are you going to focus.
My final thoughts for new EPATians are: it is a must-do course if you are beginning in the field of algorithmic trading and quantitative finance. Although the real value is in the people that drive the institution. Be sure that you will have to take more courses after EPAT to succeed in this field, but you won't find the life-long learning support that they will give you anywhere else.
The course is very organized, both theoretical and practical, the staff is very competent and helpful, I found myself at ease during the whole course of study, I learned the basics to start a career in algorithmic trading and finance in general. What I appreciated the most were the lessons held with prominent personalities from the world of finance and trading, who shared their knowledge and experiences with the students. I would definitely recommend the course to anyone wishing to pursue a career in trading and finance.
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- Knowledge of basic trading procedures and basics of algorithmic trading: know and understand the terminology
- Understand statistical methods and statistical measurements including autocorrelation function, partial autocorrelation function, Maximum Likelihood Estimation (MLE), Akaike Information Criterion (AIC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
- Basic knowledge of time series analysis, stationarity of time series, and forecasting using ARIMA
- Fundamentals of Autoregressive and GARCH Models, and understanding volatility
- Logistic regression to predict the conditional probability of the market direction
- Different methodologies of evaluating portfolio and strategy performance (back-testing methodologies and statistical figures for evaluation including Sharpe ratio, Sortino ratio, Max drawdown)
- Basic knowledge of Asset Allocation Models
- Understand all the most practical indicators and oscillators (e.g., RSI, MA, EMA)
- Distinguish between Macroeconomic and Microeconomic news
- Basic knowledge of models for spot prices, futures prices
- General knowledge of types of multifactor models and updating a traditional factor model
- Knowledge on the basics of the financial market in general and the stock market in particular
- A clear understanding of the type of instruments and the stock markets.
- Understand the concept of the stock market index and its calculation
- Basic knowledge of machine learning, pattern recognition as well as Natural Language Processing (NLP)
2 Module 1: Sentiment: What and Whose
- Understanding investor sentiment and the pendulum of investors’ emotions
- The role of “Noise Traders” in driving the asset prices in the financial markets
- Media sentiment and how it affects asset prices
- Market sentiment and its measurement
- Determining crowd sentiment and its impact on financial markets
3 Module 2: Sentiment Data
- Classical newswires and macroeconomic announcements
- Various Sources of sentiment data such as news, social media, and search engines
- The impact of Micro-blogging platforms on stock markets
- Converting qualitative information to the sentiment score
- Using bag-of-words, natural language processing and lexicon-based methods in sentiment analysis
4 Module 3: Structure and Coverage
- News analytics (Meta) data structure
- The exact polarity of sentiment in the news
- News characteristics such as relevance, novelty, and sentiment scores
- Leading data providers for sentiment data analysis in finance
- Description of the data provided by major sentiment vendors
5 Module 4: Other Sources: Alternative Data (I)
- Scheduled (expected) and Unscheduled (unexpected) financial news
- Macroeconomic news and their usage in automated trading
- Relevance and use of alternative data in sentiment analysis
- Major types of alternative data
- Different categories of alternative data such as satellite data, geolocation data, etc.
- Providers of alternative data
- Taxonomy of models
6 Module 5: Models to Exploit Sentiment Analysis (I)
- Taxonomy of models
- Descriptive, normative, prescriptive and decision models explained
- Modelling and information architecture
- Examples of modelling in the domain of finance
- The key role of time and uncertainty in decision making
7 Module 6: Models to Exploit Sentiment Analysis (II)
- Financial applications of sentiment data and their properties
- Risk management through risk quantification: risk computed for exposures of varying time spans, namely, weekly, monthly, or annualized
- Fund rebalancing on calendar dates: weekly, monthly, yearly
- Automated trading daily or intraday
- Retail application (creditworthiness, loan, and savings advice)
8 Module 7: Opinion and Biases
- Various challenges in the area of sentiment analysis
- Distinction between opinions and facts
- Role of behavioural finance in investor decision making
- Different types of biases that affect investor behaviour in financial markets
- Revisiting the pendulum of fear and greed
9 Module 8: AI, Machine Learning & Quantitative Models to predict market direction
- Quant models and AI & ML models- overview
- Interaction of Quant Models and AI & ML models to predict market direction
- Supervised and Unsupervised learning models
- Models for predicting market direction: K-Nearest Neighbor, Decision Tree Models, ANN, LSTM, SVM
- Trading Strategies using Quantitative Models and Machine Learning
10 Module 9: Role of Alternative Data in Financial Trading: Alternative Data (II)
- Rapid growth of Alternative Data in recent decades
- Improvement of technical ability to process data
- Categorization of Alternative Data and Application in Finance
- Use of Alternative Data to obtain insight into the Investment process
- Capture the predictive power of Alternative Data in Financial Trading
11 CSAF Exam
- CSAF requires you to successfully clear the Examination
- The exam is conducted in a proctored environment both at the Prometric centres in 80+ countries and remotely
1 Grasping Behavioural Finance by Anthony Luciani
- MarketPsych has built a sentiment analytics suite on investor-relevant media from thousands of online sources with hundreds of over-arching themes and topics covering all major asset classes.
- In these sessions, we explore how the stationary processes of psychology interact with the nonstationary processes of financial markets.
- Witness two major themes of investor over- and under-reaction to the news.
- See cycles of fear and their interactions with crude oil prices.
- Find solutions to common questions within the field of sentiment analysis for markets.
- Observe how new themes become more predictive over time.
2 Classifying Earnings Calls & News by Dan Joldzic
- Alexandria Technology develops natural language processing (NLP) software to convert text into data.
- Alexandria uses machine learning to identify key phrases in financial documents such as news reports, press releases, earnings calls, and filings.
- There are official sources of information such as newswires (Dow, Reuters, Bloomberg), company filings (10-Qs, 10-Ks), earnings calls, research reports.
- News classification and its impact on asset returns.
- Two types of news: company-specific news and economic news.
- News works better on short time horizons like 1 week or lesser. For greater time horizons, alpha decays.
- Unstructured news can be converted to structured data showing information on Ticker, topic and sentiment score.
- We look at the ratio of positive news reports to negative news reports of a company on a day to create a sentiment score. These companies belong to the US all cap.
3 NLP And ML Techniques in Finance: Some Examples by Dr. Matteo Campellone
- Datasets based on proprietary algorithms as Alternative Data.
- Introduction to the Brain Sentiment Indicator.
- Introduction to the Brain Language Metrics on Company Filings dataset.
- A workflow that uses ML and NLP for thematic selection.
4 Asset Allocation Enhanced by Sentiment Data by Dr. Zryan Sadik and Prof. Christina Erlwein-Sayer
- Introduction and Background.
- Market Data and News Data.
- Asset Allocation Strategy.
- Construction of Filters.
- Empirical Investigation.
- Discussions and Conclusion.
5 ESG in Factors by Dr. Katharina Schwaiger
- Environmental, social, and governance (ESG) signals are an important part of factor-based investing strategies as they can stem from the same economic rationales as general factor premiums.
- Because factors are broad and diversified, building portfolios by jointly optimizing factor exposures with ESG and carbon outcomes can result in similar historical performance as benchmark factor portfolios that do not include those considerations.
- We show how sustainable signals, which often involve alternative data, can be integrated into the definitions of factors themselves.
- We offer two examples of green intangible value and corporate culture quality which enhance traditional financial value and quality factors, respectively.
6 Using traditional structured data for long-term analysis of publicly-traded equity and using alternative, unstructured data for short-term analysis of public and private equity by Dr Keith Black
- Quantitative investors have long used traditional data sources, such as income statements and balance sheets of public firms, to drive stock selection models.
- Factors such as value, growth, earnings quality, and earnings surprise can be effective at predicting the long-term performance of publicly-traded stocks.
- With the explosion in the amount and diversity of data in the last five years, alternative data sources are quickly revolutionizing quantitative investing.
- Alternative data sources can include natural language processing of news and social media content, review of credit card transactions and consumer emails, and geolocation data using cell phone signals and satellite images.
- Alternative data is more complex to process, moves in a different time frame than traditional data and may provide a new window into information availability for private companies.
7 News Sentiment Analysis in Em Sovereign Debt research, Investment and Risk Management by Jacob Gelfand and Kamilla Kasymova
- We present the internally developed framework for ex-ante analysis of the foreign exchange and sovereign bond markets based on the news sentiment in global and local media, the Global Economy and Markets Sentiment (GEMS) model.
- The predictive analytics from the GEMS model are used to enhance the fundamental analysis to better assess risks and opportunities in the Emerging Markets sovereign debt market.
- We introduce the long-term and short-term trading strategies based on the produced GEMS analytics and spearhead the discussion about the predictive qualities of the produced analytics.
8 The Art of (Alternative) Data Science by Ganesh Mani
- The dynamic world produces data that is constantly changing. Financial markets can be particularly mercurial, triggered by geopolitical events, regulation changes, industry news and the earnings outlook of companies.
- Exploiting data science to explain or predict the ebb and flow of security prices can be a bit of an art. Knowing which data – from the plethora of traditional and alternative datasets – to focus on, what techniques to use (e.g., traditional statistical, historical-data-intensive deep learning, reinforcement learning, forward-looking simulations or a combination); and, what aspects to the model are nuanced decisions that will significantly affect portfolio risk and return.
- Human-machine teaming is also a focus area and I hope to address some of the above themes in my brief presentation. A subsequent panel will elicit multiple opinions in this milieu.
9 Using alternative data: from research to production by David Jessop
- The use of alternative data is a necessary part of many investment processes running today.
- There is, however, a difference between running a one-off analysis and having a regular process running in production.
- In this lecture, we will provide some advice and suggestions for all stages of this process.
10 ESG Data and Investment Returns by Richard Peterson
- Learn How Esg Perceptions And Controversies Are Detected In News And Social Media Using AI.
- Identify Which Esg Factors Have Been Leading Shares Higher, And Which Are Irrelevant (Or Even Damaging) To Shareholder Value.
- See How Specific ESGH Controversies Affect Corporate Share Valuations Over Time.
11 A Quantitative Metric for Corporate Sustainability by Dan DiBartolomeo
- In recent years the concept of “sustainability” companies has been at the forefront of concerns for many investors. As most consideration of sustainability has focused on the broad ideas of ESG concerns (Environmental, Social, Governance) the field has lacked a straightforward metric by which investors can assess “How many years into the future is a given company likely to survive without bankruptcy?”.
- While somewhat similar to a credit rating, such a measure must also consider the situation of firms with no current debt, and those that are pathologically conservative so as to survive until eventually becoming obsolete.
- Such a metric was introduced by Dan diBartolomeo (Journal of Investing, 2010) based on an extension of the Merton contingent-claims model (Journal of Finance, 1974). In this study, we illustrate refinements of the methodology and present empirical analysis of the relationship between the sustainability metric and investor returns from 1992 through 2021 for all equities traded on US exchanges (inclusive of non-US firms traded in ADR form).
- The results show statistically significant relationships that may be exploited for superior returns in both equity and corporate bond markets.
12 Creating a strategy using Machine Learning by Dr. Ernest Chan
- Most investors found it hard to generate sustainable alpha by machine learning.
- Machine learning algorithms are the ultimate "Black Boxes", offering no hint on why a trade is made.
- Those characteristics have impeded the adoption of machine learning in asset management.
- However, Corrective AI and Conditional Portfolio Optimization are two machine learning methods that have the potential to bring practical benefits to asset management.
- Corrective AI is the use of machine learning to compute a probability of profit of an existing trading strategy, while Conditional Portfolio Optimization is the use of machine learning for portfolio optimization.
- This lecture will discuss why these methods both work better and provide more transparency than traditional applications of ML to asset management.
13 Discussion on Project Results
- Objectives: To introduce to the participants a guideline for preparing technical reports of empirical investigations; how to develop an experimental project; and simultaneously prepare for report writing.
- Learning Outcomes: Develop a generic approach to preparing Technical Reports and develop reports collaboratively as a team.
1 Introduction to News Data
- In this session, we get acquainted with the sample of news analytics data provided as part of the course. The data is provided for a selection of American stocks. We process both market data and news data and produce some data visualisations as well as a suggested rolling sentiment score for the S&P500 index.
2 Producing your own News Sentiment
- The main goal of this session is to produce news analytics from news headlines. We collect publicly available headlines for specific companies from a website via a simple web scraper. We then use a natural language processing package in Python to produce news sentiment based on the collected data.
3 News Impact Calculation (I)
- In these sessions, we first give a short lecture on the concept of news impact scores. We then proceed to develop a notebook which calculates news impact for the sample data provided. We start by transforming the multiple scores into a single News Sentiment Value (NSV).
4 News Impact Calculation (II)
- We implement the calculation of news impact based on NSV. Finally, we produce some data visualisations based on the news impact series.
5 Momentum-based Strategy
- In this session, we first give a short lecture on the calculation of news impact-based momentum. We then implement the momentum calculation in a notebook and produce an investment strategy based on the momentum series. The strategy may invest in any of the stocks provided, as well as a risk-free asset. We also implement a simple backtesting tool in order to test different configurations of the strategy.
6 Portfolio Optimisation (I)
- In these sessions, we implement the Markowitz model for portfolio optimisation. Instead of using Python packages that solve this model, we implement it from scratch. This allows us to easily customise the optimisation model in order to generate more desirable portfolios. We then use news impact in order to produce different configurations of the Markowitz model. We produce backtesting results for a few configurations.
7 Portfolio Optimisation (II)
- We use news impact in order to produce different configurations of the Markowitz model. We produce back-testing results for a few configurations.
8 Discussion on Project Results
- Objective: To introduce to the participants a guideline for preparing technical reports of empirical investigations; how to develop an experimental project; and simultaneously prepare for report writing.
- Learning Outcome: Develop a generic approach to preparing Technical Reports and develop reports collaboratively as a team.
- Note: Students attending the Hands-on Project session will get access to Sentiment data from multiple data providers.
Anthony Luciani is a Senior Quantitative Analyst at MarketPsych. He is working on simplified sentiment and “Superforecasters” models. He developed sentiment-based financial models, previously for OptiRisk. He has a Master’s Degree in Financial Mathematics from the University of Leicester.
Prof. Christina Erlwein-Sayer
Prof. Christina is Professor of Statistics and Financial Mathematics at Hochschule für Technik und Wirtschaft (HTW) Berlin and has worked at OptiRisk Systems as a quantitative analyst and senior researcher. She completed her PhD in Mathematics at Brunel University, London in 2008. She then worked as a researcher and consultant in the Financial Mathematics Department at Fraunhofer ITWM, Kaiserslautern, Germany.
Dr. Cristiano Arbex Valle
Dr. Valle has a bachelor’s degree in Computer Science and an MSc in Operations Research from Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil. In 2011 he joined OptiRisk as a software engineer and a researcher and obtained his PhD in the Department of Mathematical Sciences from Brunel University (UK) in 2014.
Dan Joldzic, CFA, FRM is CEO of Alexandria Technology, Inc, which develops artificial intelligence to analyse financial news. Prior to joining Alexandria, Dan served dual roles as an equity portfolio manager and quantitative research analyst at Alliance Bernstein where he performed factor research to enhance the performance of equity portfolios.
Prof. Enza Messina
Prof. Enza Messina is a Professor in Operations Research at the Department of Informatics Systems and Communications, University of Milano-Bicocca, Italy, and holds a PhD in Computational Mathematics and Operations Research from the University of Milano. She is a co-founder of Sharper Analytics, a spin-off from the University of Milano Bicocca.
Prof. Gautam Mitra
Prof. Mitra is an internationally renowned research scientist in the field of Operational Research in general and computational optimization and modelling in particular. He is the founder and chairman of OptiRisk Systems and UNICOM seminars. He has published five books and over a hundred and fifty research articles and was awarded the title of ‘distinguished professor’ by Brunel University in 2004.
Dr. Matteo Campellone
Dr. Matteo is the co-founder and Executive Chairman of Brain. He holds a PhD in Physics and an MBA. Dr. Matteo’s past activities included Financial Modelling for financial institutions and Corporate Risk and Value-Based Management for industrial companies. As a Theoretical Physicist, he worked in the field of statistical mechanics of complex systems and of non-linear stochastic equations.
Dr. Richard Peterson
Dr. Peterson is CEO of MarketPsych Data which produces psychological and macroeconomic data derived from text analytics of news and social media. He is an award-winning financial writer, an associate editor of the Journal of Behavioral Finance, has published widely in academia, and performed postdoctoral neuroeconomics research at Stanford University.
Dr. Arkaja Chakraverty
Dr. Arkaja received her PhD from the Indian School of Business in Financial Economics in 2017; and is affiliated with Higher School of Economics, Moscow. Currently, she is based out of Melbourne, Australia and is working on a series of research papers. As of April 2021, Arkaja has joined the OptiRisk team as a Senior Research Associate (Consultant).
Dr. Zryan Sadik
Dr. Sadik holds a Bachelors in Mathematics from Salahaddin University, Iraq, a Masters in Computational Mathematics with Modelling and a PhD in Applied Mathematics from Brunel University, London (2018). His research interests include news sentiment analysis, filtering in linear and nonlinear time series applying Kalman filters, volatility forecasting, optimization, risk assessment, and the role of news sentiment in financial markets.
Dr. Ernest Chan
Dr. Ernest Chan is the CEO of the financial machine learning firm Predictnow.ai, and an award-winning quantitative hedge fund manager and acclaimed quant finance author. He was previously a machine learning researcher at IBM T.J. Watson Research Center and Morgan Stanley, and a proprietary trader at Credit Suisse.
Mr. diBartolomeo is President and founder of Northfield Information Services, Inc. Based in Boston since 1986, Northfield develops quantitative models of financial markets. The firm’s clients include more than one hundred financial institutions in a dozen countries. Mr. diBartolomeo is a Director of the American Computer Foundation, a former member of the Board of Directors of The Boston Computer Society, and formerly served on the industry liaison committee of the Department of Statistics and Actuarial Sciences at New Jersey Institute of Technology.
Keith Black is the managing director and program director of the FDP Institute. Previously, he served as the managing director of content strategy at the CAIA Association, where he was a co-author of the CAIA curriculum. During a prior role, Keith advised foundations, endowments and pension funds on their asset allocation and manager selection strategies in hedge funds, commodities, and managed futures. He has also traded commodity and equity derivatives and built quantitative stock selection models.
Dr. Black earned a BA from Whittier College, an MBA from Carnegie Mellon University, and a PhD from the Illinois Institute of Technology. He is a CFA, CAIA, and FDP charterholder.
David Jessop has responsibility for overseeing the independent investment risk management process for all portfolios managed in the EMEA region.
Before joining Columbia Threadneedle Investments, David was the Global Head of Quantitative Research at UBS. Over his 17 years at UBS his research covered many topics but in particular he concentrated on risk analysis, portfolio construction and more recently cross asset factor investing / the application of machine learning and Bayesian techniques in investment management. Prior to this he was Head of Quantitative Marketing at Citigroup.
Mr. Jacob Gelfand, CFA, Director of Quantitative Strategy and Research, Investment Risk Management, is responsible for macro research and analysis for the fixed income, currencies, equity and cross-asset class portfolios.
His focus areas include global markets risk analysis, asset allocation, investment style and risk management, relative value analysis and other quantitative and global macro aspects of risk and portfolio management.
Kamila is a Quantitative Research and Analytics Associate in the Investment Risk Management of Northwestern Mutual Life Insurance Company.Kamilla has extensive experience using analytical and research techniques (time series analysis and forecasting, econometrics, financial mathematics) in developing internal capital market assumptions and economic scenario generator used by Actuarial and Investment departments across the company.
Global executive and thought leader with deep experience employing AI for Health (e.g., founding advisor to entity that’s the nucleus of ICAD), Wealth (sold AI / ML asset-mgmt. boutique into SSgA; worked with marquee institutional clients and allocators like Paloma Partners) & Wisdom (co-PI for many iARPA projects: Novel Intelligence from Massive Data (NIMD), Pro-Active Intelligence (PAINT), NLP (METAPHOR), Multi-media analytics (ALADDIN ); multiple patents) Mentor / advisor at many entities (e.g., TiE.org, Sabudh.org, FDPinstitute.org; IvyCap Ventures).
Katharina Schwaiger, PhD, Director, is an investment researcher within the Factor Based Strategies Group at BlackRock. She is responsible for managing factor-based risk premia strategies and risk parity strategies for institutional and retail clients. Her research area focusses on ESG in factors. Prior to joining BlackRock, she worked as a Financial Engineer in the City of London, as a Quantitative Researcher at a London-based hedge fund and as a lecturer in Operational Research at the London School of Economics (LSE). Her career in finance started at OptiRisk Systems where she worked in the area of Liability Driven Investment (LDI) and she edited the Asset and Liability Management Handbook. She earned a BSc degree in Financial Mathematics in 2005, and a PhD degree in Mathematics/Operational Research from Brunel University in 2009.
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