This video is a part of our course on Event Driven Trading Strategies: https://quantra.quantinsti.com/course/event-driven-trading-strategies Welcome to this video lesson on seasonal event-driven trading strategies. After completing this video, you will be able to explain seasonal or calendar trading strategies and list its examples. What is a seasonal/calendar trading strategy? It’s a trading strategy which systematically exploits patterns in financial markets which are dependent on specific calendar dates. It may be an event which occurs on precisely the same day in a month. It could even happen in a month in a year or even the same hour in the day. These events may occur often or only a few times during the year. Human life is full of periodical events. Nearly everybody gets up from the bed at the morning, goes to work/school, goes shopping etc. Part of daily life is random but a significant part is scheduled. It’s the same on financial markets and seasonal/calendar anomalies try to capture profit from such repetitive events. One of the first documented seasonal strategies is the so called “Halloween effect”. It is also called the “Sell in May” strategy. This strategy is based on the historical underperformance of stocks in the "summer" months. this is the six-month period starting in May and ending in October. This underperformance is in comparison to the "wintery" six-month period from November to April. In this strategy, you sell your equity holdings in May (or at least,
In this Lunch and Learn session, we cover Iterators and Generators in python. We highlight what makes a generator, and how you as a quant can benefit from this technique. This lecture is based on the book "High Performance Python" by Micha Gorelick and Ian Ozsvald. To get the book, visit https://www.oreilly.com/library/view/high-performance-python/9781492055013/
Does your mathematics PhD topic matter? Of course having a relevant topic makes it much easier however it isn't impossible to get into quant finance with a mathematics PhD that has a non-relevant research topic. Now that being said, it isn't easy to just apply for a few jobs and get an amazing offer just because you have a math PhD. You are still competing with a range of STEM degrees who are also very bright. And many of them will have degrees that may be focused on a specific topic in which many firms are looking for. Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: https://www.teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
Learn more at: https://hudsonthames.org/mlfinlab/ In this workshop, we will describe how to use and create: time, volume, tick, and dollar bars. Including imbalance and run bars. Next, we show how to roll futures contracts to make a single time series from multiple contracts and we end with the ETF trick to backtest buy and hold strategies as well as statistical arbitrage strategies.
Bank ALM and NMDs: Hedging Bank NMDs in an Environment of Extreme Low / Negative Interest Rates
Non-maturity deposits (NMDs) are a simple and traditional bank product offered universally, but they create interest rate risks that are relatively complex to measure and manage, particularly in an environment of extreme low / negative rates. In this webinar BTRM Faculty member Patrick Carey sets out a framework that: - Integrates the Net Interest Income (NII) and Economic Value (EV) view of the interest rate risks arising on NMDs -Allows the effectiveness of standard hedge prescriptions to be assessed. The lecture addresses specifically the challenges of applying conventional ALM hedging techniques in the presence of negative convexity arising from a zero rate floor and the customer option to vary deposit balances. Presenter: Patrick Carey is a former Head of Group Market Risk for the Bank of Ireland Group (retired 2018), where he was responsible for ensuring that the bank measured, managed and controlled the full range of market risks, IRRBB and other market-related structural risks to which it was exposed. He was a member of Bank of Ireland’s Asset and Liability Committee and the European Banking Federation’s Committee on Interest Rate Risk in the Banking Book (IRRBB). Patrick works as a independent consultant and lecturer, operating as Independent Market Risk Consulting (imr-consult.com). The Certificate of Bank Treasury Risk Management (BTRM) Next Start Date: Wednesday 5th October 2022 The BTRM has partnered with University of Northwestern Switzerland (FHNW) to offer the
Learn more: www.mlfinlab.com * Special bar compression procedures are needed, especially in HFT space. * Tick/Volume/Dollar bars have better statistical properties compared to time bars. * Imbalance/Run bars are sampled more frequently when new information comes to a market. * Futures roll and ETF trick help a researcher to deal with non-perpetual securities (futures, options, ..)
Financial Data Structures: Information Driven Bars (Run and Imbalance)
Learn more: www.mlfinlab.com The purpose of information-driven bars is to sample more frequently when new information arrives to the market. In this context, the word “information” is used in a market microstructural sense. In this case information - volume/tick imbalance is associated with the presence of informed traders. The idea behind tick imbalance bars (TIBs) is to sample bars whenever tick imbalances exceed our expectations. We wish to determine the tick index, T, such that the accumulation of signed ticks (signed according to the tick rule) exceeds a given threshold.
What is Cointegration? | Statistical Arbitrage Trading Strategy
This video is a part of our course on Statistical Arbitrage Trading. Start For FREE: https://quantra.quantinsti.com/course/statistical-arbitrage-trading In this video, we will discuss cointegration. To understand cointegration, it is essential to first understand the concept of stationarity. A stationary time series is the one whose statistical parameters such as mean and variance do not change over time. Consider two price series, A and B. If the linear combination or spread of A and B stationary then the two price series A and B are said to be cointegrated with each other. To intuitively understand the concept of cointegration, consider this example of a drunk man walking with his unleashed pet dog. The drunkard and his dog are good examples of random walks as both seem to be roaming around in a seemingly aimless manner. The paths of the drunk man and the dog can be referred to as “non-stationary” or “random walks”. However, assume that the dog and the drunk man stay connected to each other using their hearing and smelling senses, then the distance between them is bounded and doesn’t increase indefinitely. Loosely speaking, we can say that the distance between the two paths is stationary and hence, the paths of the drunk and his dog can be considered as co-integrated. In the context of trading, if the spread between two assets is stationary that is, if the spread stays around the mean, then the prices are said to be co-integrated. On the other hand, “Correlated” commodities
I quit (resigned) my job for a variety of reasons with one being an ultimatum to comply and leave. After leaving, the stress went away and I could see more clearly the rate race I was struck in beforehand. As I read articles online about the great resignation months before it seemed like people were quitting for more money and nicer teams. There was a casual sprinkling of different benefits like working from home. The real answer to this is that employees want more flexibility! Working from home has been great for many people. If you have kids you also understand the stress of trying to work and watching kids but as the country opens back up, working from home with kids at school or with a sitter seems much more practical. I can multi-task when times are slow and work more hours when times are busy. I can save hours of commuting by just signing up and jumping right into my work. Not to mention if you work in a big city, you can avoid the stressful driving with so many crazy people. With so many people resigning, companies are also competing much harder for talent. Many of them are offering working from home, flexible hours, and better team culture. Those that are resigning need a break from the nose to the grind mentality and many are finding better opportunities with companies who care. Pew Research: https://www.pewresearch.org/fact-tank/2022/03/09/majority-of-workers-who-quit-a-job-in-2021-cite-low-pay-no-opportunities-for-advancement-feeling-disrespected/ Harvard Business
SoFiE Seminar with Marcelo Medeiros and Michele Lenza
Host: Eric Ghysels (The University of North Carolina at Chapel Hill) Presenter: Marcelo Medeiros (Pontifical Catholic University of Rio de Janeiro (PUC-Rio) ) Paper: "Bridging Factor and Sparse Models" Discussant: Michele Lenza (European Central Bank) Date: May 16, 2022 The Society for Financial Econometrics Seminar Series features bi-monthly presentations of cutting-edge research by leading scholars in financial econometrics. Presentations are followed by discussion and audience participation. The SoFiE Seminar series is organized and moderated by Andrew Patton from Duke University. SoFiE Seminar Series events are held as Zoom webinars and are open to all. If you would like to receive updates about these events, please email firstname.lastname@example.org and request to join our mailing list. Otherwise, please visit the SoFiE website, sofie.stern.nyu.edu, for the most up-to-date list of our events. The Society for Financial Econometrics (SoFiE) is a global network of academics and practitioners dedicated to sharing research and ideas in the fast-growing field of financial econometrics. It is an independent non-profit membership organization, currently housed at New York University. Members receive a complimentary subscription and waived manuscript submission fee to SoFiE's partner publication, the Journal of Financial Econometrics. Other benefits of membership in the Society include the opportunity to submit papers to be reviewed by our program committee for our co-sponsored joint even
Abstract: Time series are, first and foremost, sequences - so it's only natural to apply sequence modeling approach from deep learning to such problems. In this episode we present the vintage DL methods (RNN, GRU and LSTM) and show their applications for single- and multistep forecasting. We also take time to explore the connection RNN have to curve fitting - via the ES-RNN model.
A subscriber asked the question, why are there so few statistics majors in Michigan's quantitative finance and risk management masters? From what have seen this seems common across other programs as well. What else might surprise you is that there aren't very many CS students in Michigan's program either. Let's talk about it! Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: https://www.teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
Using Gamma Scalping to Solve Negative Theta | Quantitative Trading Strategies
Video from the course Quantitative Trading Strategies and Models https://quantra.quantinsti.com/course/quantitative-trading-strategies-models ***START FOR FREE*** We will implement a “gamma scalping” trading strategy to solve this negative theta problem. This would be our primary learning objective in this video lecture. The name “Gamma Scalping” comes from two different concepts. Scalping means repeatedly buying and selling stocks with a goal of obtaining daily profits. This is something the day traders generally do in the markets. But therein lies a major question that is whether to buy or sell and how much to buy or sell? Holding a long gamma position answers these questions. Hence the name Gamma Scalping. Let us have a look as to how this is done. As discussed in the previous video, we would create a "delta hedged portfolio" with long ATM call options and a short position in the underlying stock. If the underlying moves in either direction, the delta-hedged portfolio would be profitable since it is indifferent to change in the price. We have seen this in the previous video. But what if the stock opens at $50 as shown increases or decreases to either $52 or $48 and before the market closes goes back to $50. One would like to believe that we have neither made a profit nor a loss. But that is not the case. We have made a loss equivalent to the total theta position in our portfolio. Hence we have a long gamma, long vega view of our portfolio and the risk we would be facing ho
ABFR Webinar with Svetlana Bryzgalova and Guofu Zhou
Missing Financial Data Presenter: Svetlana Bryzgalova (London Business School) Discussant: Guofu Zhou (Washington University in St. Louis) Host: Lin William Cong (Cornell University) April 28, 2022 12-1pm Eastern Time 00:00:00 Welcome remarks 00:01:39 Presentation 00:33:40 Discussion 00:54:30 Q&A ABFR is an interdisciplinary community of scholars with an interest in the methodology, applications, and socioeconomic implications of AI and big data for a wide range of areas in economics and finance. The forum organizes monthly presentations and discussions of papers by the leading world experts in the area, followed by an informal general post-seminar discussion. The virtual talks take place on the last Thursday of each month from 12-1pm EST. For more information about the seminar series, including registration, please visit our website: https://www.abfr-forum.org ABFR is organized by Svetlana Bryzgalova, Lin William Cong, Maryam Farboodi, and Markus Pelger. It is also supported by The Advisory Committee, which includes Kay Giesecke, Gerald Hoberg, Wei Jiang, Bryan Kelly, Stefan Nagel, Andrew Patton, and Laura Veldkamp. ABFR hosting institutions are the Cornell FinTech Initiative and the Stanford AFTLab.
I said in a recent video and in many past videos that program rigor is important. And it is! A subscriber asked the question, "would a math masters be better than a quant finance masters because it has more rigor?" First off not all math masters are more rigorous than quant masters but I get the main point that most math programs will be rigorous. A math masters (either pure or applied) is a possible path into quant finance as well as other hot fields like data science. The one drawback is that these fields (industries) need an array of skills in math, stats, CS, and finance (or the industry focus). A math masters will teach you the math portion however making sure you cover relevant topics is important but you also need to make sure to cover stats, CS and finance. Some of these will be easier to obtain than others. Now there are math heavy areas of quant finance like financial engineering where a math masters would be preferred given you have a great attitude and can be taught the other skills. Should you get a math masters or a quant masters? It depends on your interests. If you enjoy a topic you will often do better at it in school and in the industry. If you absolutely love math the a math masters could be a great fit. The one big downside of a math masters could be finding a job in other industries if you decide later on that you don't want to work in quant finance. Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: https://www.teespring.com/sto
CFA Institute Information Session: Python and Data Science for Investment Professionals
Powered by Restream https://restre.am/yt Join CFA Institute and QuantUniversity for an information session about the upcoming CFA Institute Professional Learning course: Python and Data Science for Investment professionals. https://www.cfainstitute.org/en/events/professional-learning/python-training
Heteroskedasticity and Autocorrelation | Quantitative Trading Strategies and Models
Video from the course Quantitative Trading Strategies and Models https://quantra.quantinsti.com/course/quantitative-trading-strategies-models ***START FOR FREE*** Welcome to this video lecture! The objective of this video lecture, is to cover the required concepts for understanding the ARIMA and GARCH models. The concepts that we will learn in this video lecture are as follows, 1.Heteroskedasticity, 2.Serial Correlation or Autocorrelation. Let us begin with heteroskedasticity. If we correctly recall, one of the assumptions of linear regression is that the variance of its errors is constant across all the observations of the financial data. In other words, we can say that the errors are homoskedastic. This graph shows the values of the dependent and independent variables and a fitted regression line with homoskedastic errors. These errors or residuals are the vertical lines between the plotted or actual points and the fitted regression line or forecasted points. However, in heteroskedasticity errors are not constant. You may look at the difference between the two graphs. Unconditional heteroskedasticity occurs when the variance in errors is not correlated with independent variables or in other words error variance does not systematically increase or decrease with the changes in the values of independent variables. Though this violates the assumption, it is not statistically significant and causes no major problems while forecasting variables using regression analysis. On the o
Milind Sharma is the QuantZ / QMIT CEO with a deep background in quantamental investing. His experience started with a solid education from Carnegie Mellon with the Milind Sharma is the QuantZ / QMIT CEO with a deep background in quantamental investing. His experience started with a solid education from Carnegie Mellon with the MS in Computational Finance program back in 1995 when the program was just starting. On top of that he build a great career around quantitative finance running trading teams at some of the largest global banks. Today we discuss his background, quantamental finance, and QWAFAxNew. Milind Sharma: https://www.linkedin.com/in/milind-s-0879b4/ QuantZ: http://www.quantzcap.com/index.htm QWAFAxNew: https://qwafaxnew.org/ Podcast Version: On all major platforms: Talking Tuesdays with Fancy Quant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: https://www.teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
As part of the "2022 Fancy Quant Honorable Mentions" the University of Michigan's Masters in Quantitative Finance and Risk Management was ranked. What really sets apart Michigan's program is the focus on mathematics. Six out of the eight core courses are math classes with the other two being statistics classes. The program brings in undergraduate students who have a strong math background and teaches them more math but focused on quantitative finance and risk management. Another big advantage of the program is the use of an industry practitioner who is teaching a two semester course on machine learning for finance. The big downside of the program is the career services and location. Being located in either NYC or Chicago can make job placement much easier. There are career resources available however the industry connections and a dedicated career advisor with a strong quant background is missing. UM's Program: https://sites.lsa.umich.edu/quant/ Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: https://www.teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
I made the statement in a video recently that it is better to not live in big expensive cities like NYC because you can make the same salary in other cheaper cities like Dallas. A subscriber asked if this is true for specific banks and firms and if seniority plays an important part of this. They know smaller banks do not pay the same and JP Morgan Chase has stated they will not pay the same. Yes it is true that my salary did not change between cities. It is also true that not every firm will match your salary and seniority plays a role. However the biggest factor is how the firm views your value. Does the firm want to lose you over the location you live? For new analysts many firms assume they can just replace you. Some firms realize they are already paying you a specific amount so why should it matter where you live? As remote working becomes more popular and other industries such as tech and fintech are sucking up the best talent, I think salaries will start to level out across locations for the same position. Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: https://www.teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
Welcome to DeepMind: Embarking on one of the greatest adventures in scientific history
At DeepMind, we’re embarking on one of the greatest adventures in scientific history. Our mission is to solve intelligence, to advance science and benefit humanity. To make this possible, we bring together scientists, designers, engineers, ethicists, and more, to research and build safe artificial intelligence systems that can help transform society for the better. By combining creative thinking with our dedicated, scientific approach, we’re unlocking new ways of solving complex problems and working to develop a more general and capable problem-solving system, known as artificial general intelligence (AGI). Guided by safety and ethics, this invention could help society find answers to some of the most important challenges facing society today. We regularly partner with academia and nonprofit organisations, and our technologies are used across Google devices by millions of people every day. From solving a 50-year-old grand challenge in biology with AlphaFold and synthesising voices with WaveNet, to mastering complex games with AlphaZero and preserving wildlife in the Serengeti, our novel advances make a positive and lasting impact. Incredible ideas thrive when diverse people join together. With headquarters in London and research labs in Paris, New York, Montreal, Edmonton, and Mountain View, CA, we’re always looking for great people from all walks of life to join our mission. Learn more at deepmind.com/about and apply for open roles at deepmind.com/careers. #LifeAtDeepMind #a
"Careers in Motion" is the fifth season of the podcast, "Talking Tuesdays with Fancy Quant." In this season I will talk a lot about career development as well as some issues going on in the finance industry. I will have a guest or two as usual and we'll talk about their careers and career advice. The career advice will specifically be on quant finance however it is typically applicable to other careers such as data science, tech, and finance. Podcast: On all major platforms: Talking Tuesdays with Fancy Quant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
Data privacy has been a hot topic however the historic approach has been to try and hide our data. In this presentation I make the case that we should generate enough random data to hide the true data. Data privacy is often thought of as just a marketing tool and so not many people care. The real issue with data is that it can be used for fraud (think about your credit card being stolen for example) or it can be used to manipulate the masses. Free speech comes into the picture as data that is gathered on someone or some group can be used to manipulate them. Manipulation occurs by either providing only facts from one side of an issue or creating fake news (fake data) in an attempt to convince you to do what someone, some company, or some government would like you to do. This is really scary when you starting thinking about governments around the world. Better TOR Explanation: https://youtu.be/QRYzre4bf7I Support me with Ko-Fi (coffee) https://ko-fi.com/fancyquant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
QUANTITATIVE FINANCE 3: We don't use Black-Scholes, the simpler derivation vindicating Bachelier.
Part of the course 'Trading Using Options Sentiment Indicators' on Quantra. Start for FREE! https://quantra.quantinsti.com/course/trading-using-options-sentiment-indicators Implied volatility is forward looking, and it is an indication of the volatility of the future market. Volatility Index or VIX is one such indicator that helps us understand the sentiment in the market using implied volatility. VIX is a trademark ticker symbol for the Chicago Board Options Exchange (C B O E) Volatility Index. It is a measure of the implied volatility over the next 30 days for the S AND P 500 index options. VIX was introduced in 1993. It was originally a weighted measure of the eight S AND P 100 stocks. Later, it expanded and options were taken from a broad range in the S AND P 500 index. VIX is an up-to-the-minute market estimate for implied volatility of the S AND P 500 Index. It is calculated by taking the midpoints of the bid-ask quotes (that is price of options), of the real-time S AND P 500 index options. For each tick VIX provides an instantaneous measure of how much the market would fluctuate in the next 30 days. Hence the volatility index is forward looking and it predicts the volatility of the market in future. While calculating VIX of S AND P 500 index, the variance of all the options that are included in VIX are taken into consideration. The variance of options with expirations of near month and next month that is first and second month are considered. So, options with more than
Financial Engineering Course: Lecture 13/14, part 2/2, (Value-at-Risk and Expected Shortfall)
Financial Engineering: Interest Rates and xVA Lecture 13- part 2/2, Value-at-Risk and Expected Shortfall ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This course is based on the book: "Mathematical Modeling and Computation in Finance: With Exercises and Python and MATLAB Computer Codes", by C.W. Oosterlee and L.A. Grzelak, World Scientific Publishing, 2019. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - Codes and the slides can be found at: https://github.com/LechGrzelak/FinancialEngineering_IR_xVA - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 0:00 Introduction 2:23 Historical Value-at-Risk (HVar) and Python Experiment 33:22 Missing Data, Arbitrage and Re-Gridding 43:28 VaR Computation with Monte Carlo 55:41 Backtesting 1:04:00 Summary of the Lecture + Homework ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ CONTENT OF THIS COURSE: Lecture 1- Introduction and Overview of the Course Lecture 2- Understanding of Filtrations and Measures Lecture 3- The HJM Framework Lecture 4- Yield Curve Dynamics under Short Rate Lecture 5- Interest Rate Products Lecture 6- Construction of Yield Curve and Multi-Curves Lecture 7- Pricing of Swaptions and Negative Interest Rates Lecture 8- Mortgages and Prepayments Lecture 9- Hybrid Models and Stochastic Interest Rates Lecture 10- Foreign Exchange (FX) and Inflation Lecture 11- Market Models, Convexity
Brooklyn Quant Experience Lecture Series: Leon Tatevossian & Andrew Brenner
The Department of Finance and Risk Engineering welcomed Leon Tatevossian, NYU Tandon FRE Adjunct Professor, and Andrew Brenner, senior partner and head of international fixed income at NatAlliance Securities LLC, to the BQE Lecture Series on April 21, 2022. Title Risk and Reward in the Fixed-Income Market: Where are We Now?
Abstract: For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm. We test the performance of the algorithm on semi-synthetic heterogenous networks reconstructed to match aggregate data on the Korean financial sector. Our method allows for recovery of sub-sectors with significantly lower error rates compared to existing algorithms. Our second application is to overlapping portfolio networks, for which we uncover a clustering structure. Speaker Bio: Andreea Minca is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University. She holds degrees from Sorbonne University (PhD in Applied Mathematics) and Ecole Polytechnique (Diplome de l'Ecole Polytechnique). In recognition of "her fundamental research contributions to the understanding of financial instability, quantifying and managing systemic risk, and the control of interbank contagion", Andreea received the 2016 SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize. This award distinguishes contributions to the mathematical modeling of financial markets and is the highest early career distinction in the field of financial engineering and mathematics. Andreea is also a recipient of the NSF CAREER Award (2017), a Research Fellow of the Global Association of Risk Professionals (GARP) (2014), and an AXA Research Fund Awardee (2020). She serves on
Towards a Standard for Identifying and Managing Bias in AI with Reva Schwarz, NIST
#QuantUniversity Guest Lecture Series: As individuals and communities interact in and with an environment that is increasingly virtual, they are often vulnerable to the commodification of their digital footprint. Concepts and behavior that are ambiguous in nature are captured in this environment, quantified, and used to categorize, sort, recommend, or make decisions about people’s lives. While many organizations seek to utilize this information in a responsible manner, biases remain endemic across technology processes and can lead to harmful impacts regardless of intent. These harmful outcomes, even if inadvertent, create significant challenges for cultivating public trust in artificial intelligence (AI). While there are many approaches for ensuring the technology we use every day is safe and secure, there are factors specific to AI that require new perspectives. AI systems are often placed in contexts where they can have the most impact. Whether that impact is helpful or harmful is a fundamental question in the area of Trustworthy and Responsible AI. Harmful impacts stemming from AI are not just at the individual or enterprise level, but are able to ripple into the broader society. The scale of damage, and the speed at which it can be perpetrated by AI applications or through the extension of large machine learning models across domains and industries requires concerted effort. Current attempts for addressing the harmful effects of AI bias remain focused on computational fac
What Is Fear And Greed In Markets? | Trading using Options Sentiment Indicators
Part of the course 'Trading Using Options Sentiment Indicators' on Quantra. Start for FREE! https://quantra.quantinsti.com/course/trading-using-options-sentiment-indicators There is an old Wall Street saying that “The financial markets are majorly driven by the two most powerful Emotions Fear & Greed.” This oversimplified statement helps in understanding the true value of a security. Succumbing to these emotions, can have a profound and detrimental effect on investor's portfolio. Investors get caught up by greed, there is a desire that the securities they are holding will make them rich. During such time, there is positivity in the market, and investors keep on buying securities even when the prices are at their peak. Nobody understands the real value amid such high prices, Leading to overpricing of the security. Such irrational exuberance in buying leads to a bubble. The bubble that the markets experienced in 2008 was the ‘real estate bubble.’ In the US Housing market during 2000-2006, there was a sharp increase in real estate prices. There was an expectation that the only way the prices in the real estate market could go were, ‘up’, leading to overpricing of real estate properties. This, in turn, led to excessive lending by banks to individual investors who wanted to invest in the housing market. Banks were expecting real estate property prices to be a safe bet, as, if the investor defaults, the bank will recover its money by selling the underlying property. As a result, th
A subscriber messaged me and asked about how to get the most out of an internship. Some of the big takeaways: 1) Carry a notebook and take notes in meetings. This reduces the times you ask a question multiple times. 2) Be excited and learn about others...networking. 3) Take on projects if you have the time. I have seen many interns who just do the bare minimum. Often they don't ask many questions and their work suffers because of it. I have only come across a few interns who I would hire afterwards. They were excited to be there and exceled at the assignments given to them. They ask many questions which resulted in interesting conversations. These conversation helped them build network contacts for the future and they learned insights that most interns won't learn. Support me with Ko-Fi (coffee) https://ko-fi.com/fancyquant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
The STEP UP Seminar series (Student Training to Enhance Preparation, Utilizing Practitioners) is a co-curricular initiative which brings in top practitioners to share their insights and knowledge with Georgetown students. This session features Erin Browne from PIMCO. This seminar series is sponsored by: The Georgetown Graduate Investment Fund (GIF), Psaros Center for Financial Markets and Policy, Georgetown University Alumni Association, Georgetown University Investment Office, and the MSB PILLARs initiative.
QUANTITATIVE FINANCE 2: Deriving Black-Sholes via Itô's lemma (the dynamic hedging approach)
Do you need statistics on the Buy Side or Sell Side of quant finance? The answer is short...BOTH! This can be a common question as what many people debate is which side uses statistics more. I think this question is also challenging to answer without defining what "quant finance" really is. The buy side can use less statistics and even none however without statistics is it really quantitative finance? Now the sell side is much more tied into understanding the products they create and sell which requires statistics. Again it doesn't mean someone can't create products without statistics but it would be somewhat unethical to sell something without understanding it. Support me with Ko-Fi (coffee) https://ko-fi.com/fancyquant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
The STEP UP Seminar series (Student Training to Enhance Preparation, Utilizing Practitioners) is a co-curricular initiative which brings in top practitioners to share their insights and knowledge with Georgetown students. This session features Erin Browne from PIMCO. This seminar series is sponsored by: The Georgetown Graduate Investment Fund (GIF), Psaros Center for Financial Markets and Policy, Georgetown University Alumni Association, Georgetown University Investment Office, and the MSB PILLARs initiative.
The STEP UP Seminar series (Student Training to Enhance Preparation, Utilizing Practitioners) is a co-curricular initiative which brings in top practitioners to share their insights and knowledge with Georgetown students. This session features John Roque from 22V Research. This seminar series is sponsored by: The Psaros Center for Financial Markets and Policy, Georgetown University Alumni Association, Georgetown University Investment Office, and the MSB PILLARs initiative.
Classification Decision Tree Model | Decision Tree for Trading
FREE PREVIEW! ** Decision Tree for Trading: https://quantra.quantinsti.com/course/decision-trees-analysis-trading-ernest-chan ** In this section, we will code a classification decision tree model in Python. We will use the scikit-learn library which is a comprehensive Python library for creating machine learning algorithms. The problem statement for our decision tree model is to predict the next day’s trend of the daily returns of a stock. Following are the steps involved in the process. In this video, we will cover the first three steps. Import the data We will input raw data of a stock from a csv file. You can get raw data in csv format from different online sources. The data consists of Open-High-Low-Close prices and Volume data. Predictor and target variables are created using this raw data. Create and Define predictor variables and target variable The predictor variables which we need to create are Average Directional Index, Relative Strength Index, Simple Moving Average. To create these indicators, we will use the TA-Lib package that has the in-built functions for various technical indicators. The target variable is the 1-day future returns. We will classify it in two labels 0 for negative returns and 1 for positive returns. We label the predictor variables as X and the target data label as y. As you can see, we have defined certain time periods for each indicator. You can modify these numbers to say 7 days instead of 14 days and check the changes in model predictions.
One of the hardest concepts is to see the advantage of abstract thought. For quants and other degrees such as pure math, you learn really interesting skills however it can be challenging to see how to apply these to real world problems. However once you start working in an industry that abstract knowledge becomes useful to solve not only current problems but future problems that may not have any solutions today. I am a big proponent of teaching highly rigorous topics even though they lack application from the view of a graduate student. The general concepts of math and statistics seemed to lack any real meaning until I started working in the industry. A colleague of mine once commented, "Dimitri's best skill in the ability to learn new complicated concepts quickly and deeply." I don't think I am a genius, I just think I understand the fundamentals better than most. This is a short clip from a longer discussion linked below: https://youtu.be/KCz1F24Awy0 Podcast: On all major platforms: Talking Tuesdays with Fancy Quant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBianco
Financial Data Structures in Financial Machine Learning: Futures Roll
There are several caveats in backtesting futures and options strategies. Due to the non-continuous nature of this asset class, one needs to stitch them into a so-called "continuous contract." However, naive stitching is not the correct way to deal with this problem. In our latest video, we discuss how the "Futures Roll" trick helps to solve this issue and how the mlfinlab library is used to apply the concept.
State Machine Replication, and Why You Should Care with Doug Patti
Doug Patti is a developer in Jane Street’s Client-Facing Tech team, where he works on a system called Concord that undergirds Jane Street’s client offerings. In this episode, Doug and Ron discuss how Concord, which has state-machine replication as its core abstraction, helps Jane Street achieve the reliability, scalability, and speed that the client business demands. They’ll also discuss Doug’s involvement in building a successor system called Aria, which is designed to deliver those same benefits to a much wider audience. Some links to topics that came up in the discussion: Jane Street’s electronic trading platforms: https://www.janestreet.com/institutional-services/electronic-trading-platforms/ The FIX protocol: https://www.investopedia.com/terms/f/financial-information-exchange.asp More on market data and multicast: https://signalsandthreads.com/multicast-and-the-markets/ UDP multicast: https://en.wikipedia.org/wiki/Multicast Reliable multicast: https://en.wikipedia.org/wiki/Reliable_multicast#:~:text=A%20reliable%20multicast%20is%20any,as%20multi%2Dreceiver%20file%20transfer. Kafka: https://kafka.apache.org/intro You can find the transcript for this episode and all past episodes at signalsandthreads.com.
Course Overview | Financial Data Science & Feature Engineering
FREE PREVIEW: https://quantra.quantinsti.com/course/financial-data-science-feature-engineering Hi! I’m Roger Hunter, Chief Technology Officer at QTS Capital Management. I was the founder and former CEO of a scientific software firm that was sold to Thomson Reuters. I was also a professor of mathematics at New Mexico State University. After completing this course, you will be able to 1. Explore price and volume data to resolve issues with outliers, duplicate values, multiple stock classes, survivorship bias, and look-ahead bias. 2. Work with sentiment data to identify structural breaks and to aggregate categorical features. 3. Examine fundamental data and resolve multiple data merging issues. 4. Create features and target variables for machine learning models. This course will provide you with all the essential skills required to check and rectify financial market data and to create the right features and target variables for machine learning algorithms. The course has two parts. The first part is about financial data science and the second part is about feature engineering. In the financial data engineering part, we will walk you through several different datasets and the challenges they present. The first dataset you start exploring is price and volume data. You will learn to identify and solve problems such as survivorship bias, redundant stock data, multiple stock classes and outliers. Then, you will learn to perform checks in sentiment data derived from news items. The se
As my journey unfolds I am at a cross road of firms in tech, fintech, and banking (risk quant). All of the opportunities have their pros and cons however making this decision is really challenging. Should I leave banking for another industry? Will the other industries have the rigor of banking? Maybe I can bring an academic rigor that would would really help these firms grow? Or maybe I am delusional and these firms just want to make models over and over again. Tech is very free and exploration is encouraged. Working remote full time is a real option and the name of a FAANG might really boost my resume. The skills in both tech and fintech are similar enough to quant finance that I could make a great contribution while learning new skills or enhancing ones I don't get to use as often as a quant. The fintech offer is local and it would be nice to meet real people face to face. Free lunches and snacks would be a great perk as well as working with nice and motivated people. I am still trying to figure out what would really make me happy. I know that sounds cliché however why spend your life working on something that you don't find meaningful. Future updates will be released as they unfold. Be sure to subscribe and hit the bell button. Support me with Ko-Fi (coffee) https://ko-fi.com/fancyquant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.c
DECENTRALIZED FINANCE (DEFI): Deconstructing the Opportunities, Challenges, and Risks
What’s all the Brouhaha About? DeFi involves offering traditional financial services (Payments, Lending, etc.) using Blockchain-based software and Smart Contracts, instead of intermediaries that provide trust and execute transactions in Traditional Finance. How will Traditional Finance and DeFi come together? Market participants are grappling with two seemingly parallel universes of Traditional Finance and the fast-evolving DeFi world. The question is whether these two worlds will collide, coexist, or converge at some point, and how will that happen. Key Questions for Discussion What is DeFi all about? How big is it, how fast is it evolving, and who are the leading players in it? What implications does DeFi have for the financial services industry and for business in general? What does the DeFi stack look like, and how is it different than the TradFi stack? What does the convergence of TradFi/DeFi mean, and what are examples of this today? What will be the onramps/offramps between TradFi and DeFi? What are the regulatory challenges with this new financial model? Bio: Dushyant “D” Shahrawat is a senior member of Rosenblatt Securities’ FinTech Investment Banking group on Wall Street. Rosenblatt is the largest floor broker on the New York Stock Exchange having operated for 40+ years. D leads the Banking group’s business development efforts responsible for building relationships with leading FinTechs, investors and financial institutions. He is also responsible for thought leader
HKML S4E7 - Pricing options on flow forwards by neural networks in Hilbert space
Pricing options on flow forwards by neural networks in Hilbert space Speaker: Luca Galimberti Abstract: We propose a new methodology for pricing options on flow forwards by applying infinite-dimensional neural networks. We recast the pricing problem as an optimization problem in a Hilbert space of real-valued function on the positive real line, which is the state space for the term structure dynamics. This optimization problem is solved by facilitating a novel feedforward neural network architecture designed for approximating continuous functions on the state space. The proposed neural net is built upon the basis of the Hilbert space. We provide an extensive case study that shows excellent numerical efficiency, with superior performance over that of a classical neural net trained on sampling the term structure curves. paper: https://arxiv.org/pdf/2202.11606.pdf
SoFiE Seminar with Toby Moskowitz and Albert "Pete" Kyle
Host: Ekaterina Smetanina (The University of Chicago Booth School of Business) Presenter: Toby Moskowitz (Yale University) Paper: "Trading Costs" Discussant: Albert "Pete" Kyle (University of Maryland) Date: Rescheduled: April 18, 2022 The Society for Financial Econometrics Seminar Series features bi-monthly presentations of cutting-edge research by leading scholars in financial econometrics. Presentations are followed by discussion and audience participation. The SoFiE Seminar series is organized and moderated by Andrew Patton from Duke University. SoFiE Seminar Series events are held as Zoom webinars and are open to all. If you would like to receive updates about these events, please email email@example.com and request to join our mailing list. Otherwise, please visit the SoFiE website, sofie.stern.nyu.edu, for the most up-to-date list of our events. The Society for Financial Econometrics (SoFiE) is a global network of academics and practitioners dedicated to sharing research and ideas in the fast-growing field of financial econometrics. It is an independent non-profit membership organization, currently housed at New York University. Members receive a complimentary subscription and waived manuscript submission fee to SoFiE's partner publication, the Journal of Financial Econometrics. Other benefits of membership in the Society include the opportunity to submit papers to be reviewed by our program committee for our co-sponsored joint events, discounted registration rate fo
As data science and machine learning have become more popular in finance I see all kinds of attempts to apply it without understanding either the data science concepts or in this case, not understanding basic theory (time-series). In this example I explain why and how you shouldn't use cross validation (k-fold) on time-series data. The two main issues are information leakage and serial correlation. Information leakage occurs more often with panel data because people assume it is cross sectional based on time which is true however with a large enough sample from each time period you basically have all the information needed. If this doesn't make sense, review the central limit theorem. Serial correlation is the correlation across time. It is a core concept to most financial data. I have come across people who think you can randomly scramble the data and then model it. This was done because time-series is hard and has many assumptions that need to be tested to create a robust and meaningful model. Instead of doing the hard work they put their heads in the sand and pretended it would work in practice. After it failed miserably, they then had a new approach to refit the model every month to "fix" this "mysterious" problem. Support me with Ko-Fi (coffee) https://ko-fi.com/fancyquant Website: https://www.FancyQuantNation.com Quant t-shirts, mugs, and hoodies: teespring.com/stores/fancy-quant Connect with me: https://www.linkedin.com/in/dimitri-bianco https://twitter.com/DimitriBi
Types Of Momentum in Trading | Momentum Trading Strategies
Welcome to this video lesson on the different types of momentum. After completing this, you will be able to explain time series momentum and cross-sectional momentum. You can exploit momentum in two ways which are time series momentum and cross-sectional momentum. In time-series momentum, the performance of a price series is compared to its past performance. In contrast, in cross-section momentum, the performance of a price series is compared relative to the other price series in a portfolio. Let's say you have 10 stocks in a portfolio. You want to create a strategy using time series and cross-sectional momentum. You calculated returns of all stocks for the past 1 year. In time-series momentum, you buy the stocks that have returns above a certain level, say 5% over 1 year. You sell the stocks that have returns below a certain level say -5% over 1 year. In cross-sectional momentum, you rank all stocks in a portfolio in descending order based on returns over 1 year. You buy the top 5 stocks and sell the bottom 5 stocks. You can observe that in time-series momentum, we used absolute performance of stocks over some prior period. While in cross-sectional momentum, we used relative performance of stocks over some prior period. Most of the securities exhibit cross-sectional or time-series momentum. Usually, we look at the price chart for the duration of one year to see momentum. It is observed that if we look at the past month only, it exhibits a mean-reverting property. Thus, insti
Financial Engineering Course: Lecture 13/14, part 1/2, (Value-at-Risk and Expected Shortfall)
Financial Engineering: Interest Rates and xVA Lecture 13- part 1/2, Value-at-Risk and Expected Shortfall ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This course is based on the book: "Mathematical Modeling and Computation in Finance: With Exercises and Python and MATLAB Computer Codes", by C.W. Oosterlee and L.A. Grzelak, World Scientific Publishing, 2019. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - Codes and the slides can be found at: https://github.com/LechGrzelak/FinancialEngineering_IR_xVA - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 0:00 Introduction 4:44 Value at Risk (VaR), Stressed VaR (SVaR) 35:26 Coherent Risk Measures 45:56 Expected Shortfall ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ CONTENT OF THIS COURSE: Lecture 1- Introduction and Overview of the Course Lecture 2- Understanding of Filtrations and Measures Lecture 3- The HJM Framework Lecture 4- Yield Curve Dynamics under Short Rate Lecture 5- Interest Rate Products Lecture 6- Construction of Yield Curve and Multi-Curves Lecture 7- Pricing of Swaptions and Negative Interest Rates Lecture 8- Mortgages and Prepayments Lecture 9- Hybrid Models and Stochastic Interest Rates Lecture 10- Foreign Exchange (FX) and Inflation Lecture 11- Market Models, Convexity Adjustments and Beyond Lecture 12- Valuation Adjustments- xVA (CVA, BCVA and FVA) *** Lecture 13- Histor