Home

Video

Default view
Title
Name
Description
URL
Date
Views
Rating
Dimitri Bianco
During a podcast between Peter Zeihan and Joe Rogan, Peter mentioned we don't have the math to build a social scorecard. This couldn't be further from the truth even if we interpret his statement to "we don't have the technology" where technology would include data collection, data storage, and math to build the scorecards. Original Joe Rogan Interview: https://open.spotify.com/episode/406fOiiKMU0ot5AS1AIwve Specific clip from another channel: https://youtu.be/huG6TWY9jSA DISCLAIMER: the video clip from the Joe Rogan Podcast is being used under "fair use" as it is being used as a reference for a discussion about a comment made during that podcast interview. No copyright infringement is intended. Website: https://www.FancyQuantNation.com Support: https://ko-fi.com/fancyquant 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
https://www.youtube.com/watch?v=AEhGfmwFFnw
2023/01/29
20
5
Supply And Demand Analysis | Price Action Trading Strategies Using Python
Open
Quantra
Course on Price Action Trading Strategies Using Python: bit.ly/3hL9Nvh Welcome to this video on supply and demand analysis. After completing this video, you will be able to explain what supply and demand analysis is and identify supply and demand zones. Consider this price movement of a stock from $100 to $200. You can observe that instead of increasing straight from $100 to $200, the price increases in a zig-zag manner. Why did this happen? This movement of the stock can be explained by the imbalance of supply and demand forces. Let's understand what supply and demand forces are. Supply is a phenomenon which occurs due to the availability of sellers in the market. Whenever there is an increase in sellers and when the sellers are more than buyers, there would be a formation of a supply zone on a price chart. This means that more market participants are willing to sell a particular security as compared to market participants who are willing to buy it. For this reason, whenever there is an increase in supply, the price of the security declines. Let’s understand this with an example. Consider this price chart on-screen. You can observe that whenever the price reaches between $260 to $265, it reverses and starts declining. This is because the sellers in this price zone are greater than the buyers, which indicates that the supply is greater than the demand. This is known as a supply zone. You may be wondering, why is it a zone and not a line? Let’s understand this with an example.
https://www.youtube.com/watch?v=9gI2FspF1s4
2023/01/28
13
5
What is the Feynman-Kac formula?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 8/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB @ComputationsInFinance 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. Wha
https://www.youtube.com/watch?v=o7deOrWRC2I
2023/01/27
8
5
Get to Know Us: Jess from Hospitality at Jane Street
Open
Jane Street
Get to Know Us | Meet Jess who is based in Jane Street's New York office. She works on the Hospitality team and has been full time since 2019. The people featured in these videos were excited to share what makes Jane Street a rewarding place to work. Whether in Trading & Research, HR & Recruiting, or Cybersecurity, everyone plays a key part in fostering our open and collaborative working culture. While it’s hard to capture all the nuance of our firm in a series of short videos, we hope that they give you a real sense of who we are and what we do.
https://www.youtube.com/watch?v=7Yr3gUXWSLI
2023/01/26
15
0
Data Monopolies and Retail Finance
Open
afajof
AFA Panel: Data Monopolies and Retail Finance Panel Session Friday, Jan. 6, 2023 2:30 PM - 4:30 PM (CST) Sheraton New Orleans, Napoleon B & C Hosted By: AMERICAN FINANCE ASSOCIATION Moderator: Laura Veldkamp, Columbia University Panelist(s) Wei Jiang, Emory University Zhiguo He, University of Chicago Thomas Philippon, New York University 0:00 - Beginning 0:08 - Laura Veldkamp, Columbia University 10:59 - Thomas Philippon, New York University 20:30 - Wei Jiang, Emory University 32:23 - Zhiguo He, University of Chicago 42:45 - Q&A
https://www.youtube.com/watch?v=z5L9oZIXoa4
2023/01/25
14
5
Agostino Capponi (Columbia): "Do Private Transaction Pools Mitigate Frontrunning Risk?"
Open
Cornell Financial Engineering Manhattan CFEM
Abstract: Blockchain users who submit transactions through private pools are guaranteed pre-trade privacy but face execution risk. We argue that private pools serve the intended purpose of eliminating frontrunning risk, only if such risk is high. Otherwise, some validators may decide to avoid monitoring private pools to preserve rents extracted from frontrunning bots. Private pools intensify the execution arms race for bots, thus decreasing their payoffs and increasing validators’ rents. The private pool option reduces blockspace allocative inefficiencies and raises aggregate welfare.
https://www.youtube.com/watch?v=vaLOWICz9kA
2023/01/25
0
0
What sanity checks can you perform for a simulated stock process?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 7/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. What are the deficiencies
https://www.youtube.com/watch?v=vr4_ySb5DSo
2023/01/24
5
5
Why Independent Quant Don't Exist
Open
Dimitri Bianco
Why don't independent quants exist? Well it comes down to opportunity cost and scalability. Even with a million dollars and 10% returns (which is higher than average) you would only bring in $100,000 the first year. If you took that $100,000 as a salary and kept investing with a 10% return you only make $100,000. Working for yourself and trading has large risks and fluctuations. Most quants starting their first job will make between $80k-$100k plus a bonus and other benefits like 401k and healthcare. From this perspective it is easier to work for someone else where you are compensated more and have less stress and risk. Now if you are really good at investing you'll want to scale your operation so you make a lot more than $100k. To do this you'll need investors and this will require other costs such as hiring a lawyer to set up your fund and manage all the investing regulations. As you get more capital you'll also need to hire people to do the accounting, data engineering, trading, and quant finance as you won't have enough time to invest all of the money. Now you end up working for investors and not yourself AND you now will spend most of your time running a firm and not doing the quant finance research unless you find a partner to help with the business side. Overall people typically end up working for someone else or starting their own firm. Both are from from being independent as you either work for a firm or for investors and both are demanding. Website: https://www.Fanc
https://www.youtube.com/watch?v=9g-JrmyGJvU
2023/01/22
8
5
Simple Moving Average | Technical Indicators Strategies in Python
Open
Quantra
Course on Technical Indicators Strategies in Python: https://bit.ly/3D3eGs2 Welcome to this video on simple moving average. After completing this video, you will be able to explain the moving average as well as the simple moving average, or the SMA. You will learn how weights are assigned to each data point while calculating SMA. You'll also be able to list the applications of the simple moving averages. What is a moving average? A moving average is also called a rolling average. It's simply the average of the specified data points over a selected period of time. For example, a 5-moving average represents the average of five data points. And a 10-moving average represents the average of ten data points. This is the daily price data of stock A for the past 5 days. Can you guess the 5-moving average on the fifth day? It would simply be the average of all the prices during the past 5 days. Now we have the price data for the past 6 days, what would be the 5-MA in this case? As the new data for the 6th day has become available to us, we will drop the oldest value, that is, the price as on 1st Jan, and add the latest value, that is, the price as on 6th Jan. So, in essence, the mean or average is rolling along with the data, and hence the name moving average. The reason for using a rolling window to compute the moving average is that unlike the regular average, which only considers stagnant data the moving average takes into account the most recently available data and replaces it w
https://www.youtube.com/watch?v=fDSSgzl0GVw
2023/01/21
10
0
What are the advantages and disadvantages of using ABM/GBM for modelling a stock process?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 6/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB @ComputationsInFinance 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. Wha
https://www.youtube.com/watch?v=jHnVj1_Zj38
2023/01/20
14
5
QuantUniversity Guest Lecture series:NYC AI Bias Audit : An overview regarding automated employme…
Open
QuantUniversity Channel
Powered by Restream https://restream.io Starting April 15th 2023, New York city is expected to regulate the use of automated employment decision tools (AEDT). Specifically, employers are expected to conduct bias audits on AEDT, (both home grown and vendor sourced) through an independent algorithmic auditor. In addition, employers need to provide notices about usage of AEDT to employees or job candidates. [1] provides more details about the upcoming law. While regulatory efforts on use of AI and automation is slowing picking up traction in the US, this law is one of the first that mandates employers to carry annual audits leading to a lot of discussion on what the expectations are. In this master class, we seek to discuss the published law and bring about clarity on expectations both from a technical and legal perspective.
https://www.youtube.com/watch?v=LG4lNMK6L_A
2023/01/19
9
5
QuantUniversity Guest Lecture series:NYC AI Bias Audit : An overview regarding automated employme…
Open
QuantUniversity Channel
Powered by Restream https://restream.io Starting April 15th 2023, New York city is expected to regulate the use of automated employment decision tools (AEDT). Specifically, employers are expected to conduct bias audits on AEDT, (both home grown and vendor sourced) through an independent algorithmic auditor. In addition, employers need to provide notices about usage of AEDT to employees or job candidates. [1] provides more details about the upcoming law. While regulatory efforts on use of AI and automation is slowing picking up traction in the US, this law is one of the first that mandates employers to carry annual audits leading to a lot of discussion on what the expectations are. In this master class, we seek to discuss the published law and bring about clarity on expectations both from a technical and legal perspective.
https://www.youtube.com/watch?v=LG4lNMK6L_A
2023/01/18
4
5
QuantUniversity Guest Lecture series:NYC AI Bias Audit : An overview regarding automated employme…
Open
QuantUniversity Channel
Powered by Restream https://restream.io Starting April 15th 2023, New York city is expected to regulate the use of automated employment decision tools (AEDT). Specifically, employers are expected to conduct bias audits on AEDT, (both home grown and vendor sourced) through an independent algorithmic auditor. In addition, employers need to provide notices about usage of AEDT to employees or job candidates. [1] provides more details about the upcoming law. While regulatory efforts on use of AI and automation is slowing picking up traction in the US, this law is one of the first that mandates employers to carry annual audits leading to a lot of discussion on what the expectations are. In this master class, we seek to discuss the published law and bring about clarity on expectations both from a technical and legal perspective.
https://www.youtube.com/watch?v=LG4lNMK6L_A
2023/01/18
4
5
What is the difference between a stochastic process and a random variable?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 5/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB @ComputationsInFinance 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. Wha
https://www.youtube.com/watch?v=V2fXrxqnGr4
2023/01/17
16
5
Does GPA Matter?
Open
Dimitri Bianco
Does GPA matter? I get asked this question a lot! Even after explaining why I don't care about GPA as a hiring manager for quants, I still find students not taking the advice to take meaningful classes and not worry about the GPA. For undergrad your GPA matters as it is used to select students for a graduate program (masters and PhDs). Graduate schools is a minimum for quants. Now for business students (such as MBAs), the GPA is extremely important. Investment banks and consulting companies care. I don't find it adds much value but business people are different. Website: https://www.FancyQuantNation.com Support: https://ko-fi.com/fancyquant 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
https://www.youtube.com/watch?v=HXornhwJb04
2023/01/15
16
5
All About Candlesticks | Candlestick Patterns Based Automated Trading
Open
Quantra
Course on Candlestick Patterns based Automated Trading: https://quantra.quantinsti.com/course/candlestick-patterns-automated-trading If you are new to candlestick trading, you don’t need to worry! In this video, we will take you through all of the basic information that you need to be aware of before you decide to learn more about candlestick patterns. Firstly, let us begin by understanding the difference between the regular price charts vs candlesticks. The chart shown below may look familiar to you, especially if you have tried learning about technical analysis by yourself in the past. This is known as a line chart. And it is the simplest way of representing the price data of a stock where the close price series is plotted over a given period of time. However with line charts, because we are only plotting the close price series of the asset, we end up missing out on other important information such as the high, low and open price series data. For example, assume that for a particular day, the price of Stock ABC moves in a manner as shown. Based on the above, we can say that the start of the trading day seemed bullish for the asset. As we can see, the prices opened at $50 and moved up all the way to $52 which was the highest price for the day. However, in the second half, the sellers seem to have dominated and the prices hit a low price of $48. And eventually, towards the end of the trading day, the prices recovered a bit and closed at $49. All of this useful information tha
https://www.youtube.com/watch?v=fhdYULFoeEU
2023/01/14
12
0
Sustainable Finance and E, S, & G Issues: Values versus Value
Open
afajof
Laura Starks, President (2022), University of Texas at Austin 0:00 - Beginning 0:07 - Markus Brunnermeier, President-Elect (2022) 4:36 - Laura Starks, President (2022), University of Texas at Austin
https://www.youtube.com/watch?v=yGm6KezuSgQ
2023/01/13
4
0
2023 AFA Business Meeting and Awards Presentation
Open
afajof
2023 AFA Business Meeting and Awards Presentation 0:00 - Beginning 0:07 - James Schallheim, Executive Secretary and Treasurer 4:38 - Francine Montemurro, AFA Ombuds 9:20 - Laura Starks, President (2022) 13:25 - Markus Brunnermeier, President-Elect (2022) 16:34 - Antoinette Schoar, Editor of the Journal of Finance 34:45 - John Graham, President (2021) 36:09 - Johannes Stroebel, New York University 38:03 - John Graham, President (2021) 39:58 - Andrew Lo, MIT
https://www.youtube.com/watch?v=am40ms8cdO8
2023/01/13
7
0
How to Come Up with Great Research Ideas in Finance
Open
afajof
AFA Panel: How to Come Up with Great Research Ideas in Finance Panel Session Saturday, Jan. 7, 2023 2:30 PM - 4:30 PM (CST) Sheraton New Orleans, Napoleon B & C Hosted By: AMERICAN FINANCE ASSOCIATION Moderator: Markus K. Brunnermeier, Princeton University Panelist(s) Ulrike Malmendier, University of California-Berkeley Ralph Koijen, University of Chicago Johannes Stroebel, New York University 0:00 - Beginning 3:04 - Ralph Koijen, University of Chicago 21:25 - Ulrike Malmendier, University of California-Berkeley 37:38 - Johannes Stroebel, New York University 1:01:50 - Q&A
https://www.youtube.com/watch?v=UkPoAktls14
2023/01/13
7
0
Can you price options using Arithmetic Brownian motion?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 4/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB @ComputationsInFinance 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. Wha
https://www.youtube.com/watch?v=sYS-qFCDaSA
2023/01/13
10
5
Exchange Rate Puzzles and Policies
Open
afajof
AFA Lecture: Exchange Rate Puzzles and Policies Lecture Saturday, Jan. 7, 2023 10:15 AM - 12:15 PM (CST) Sheraton New Orleans, Napoleon B & C Hosted By: AMERICAN FINANCE ASSOCIATION Presiding: Markus K. Brunnermeier, Princeton University Speaker(s) Oleg Itskhoki, University of California-Los Angeles 0:00 - Beginning 2:11 - Oleg Itskhoki, University of California-Los Angeles 1:18:30 - Q&A
https://www.youtube.com/watch?v=-e-5AL0rWYs
2023/01/12
4
5
Inflation
Open
afajof
AFA Panel: Inflation – What Lies Ahead Panel Session Friday, Jan. 6, 2023 10:15 AM - 12:15 PM (CST) Sheraton New Orleans, Napoleon B & C Hosted By: AMERICAN FINANCE ASSOCIATION Moderator: Ricardo Reis, London School of Economics Panelists: Carolin Pflueger, University of Chicago David Romer, University of California-Berkeley Philipp Schnabl, New York University Lisa Cook, Federal Reserve Board 0:00 - Beginning 4:28 - Lisa Cook, Federal Reserve Board 19:36 - Carolin Pflueger, University of Chicago 35:01 - David Romer, University of California-Berkeley 51:49 - Philipp Schnabl, New York University 1:09:28 - Discussion and Q&A
https://www.youtube.com/watch?v=nb9maP5K3MY
2023/01/12
15
5
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
Open
Hudson & Thames
Join the Reading Group: http://hudsonthames.org/reading-group/ The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. Although this ranking step is traditionally performed using heuristics or by sorting the outputs produced by pointwise regression or classification techniques, strategies using learning-to-rank algorithms have recently presented themselves as competitive and viable alternatives. Although the rankers at the core of these strategies are learned globally and improve ranking accuracy on average, they ignore the differences between the distributions of asset features over the times when the portfolio is rebalanced. This flaw renders them susceptible to producing suboptimal rankings, possibly at important periods when accuracy is actually needed the most.
https://www.youtube.com/watch?v=g_ZU1YIOQI4
2023/01/12
14
0
Get to Know Us: Sergei from Research at Jane Street
Open
Jane Street
Get to Know Us | Meet Sergei who is based in Jane Street's New York office. He works on the Research team and has been full time since 2021. The people featured in these videos were excited to share what makes Jane Street a rewarding place to work. Whether in Trading & Research, HR & Recruiting, or Cybersecurity, everyone plays a key part in fostering our open and collaborative working culture. While it’s hard to capture all the nuance of our firm in a series of short videos, we hope that they give you a real sense of who we are and what we do.
https://www.youtube.com/watch?v=beL4DGgGB2I
2023/01/11
8
0
Get to Know Us: Jake from Accounting at Jane Street
Open
Jane Street
Get to Know Us | Meet Jake who is based in Jane Street's New York office. He works on the Accounting team and has been full time since 2021. The people featured in these videos were excited to share what makes Jane Street a rewarding place to work. Whether in Trading & Research, HR & Recruiting, or Cybersecurity, everyone plays a key part in fostering our open and collaborative working culture. While it’s hard to capture all the nuance of our firm in a series of short videos, we hope that they give you a real sense of who we are and what we do.
https://www.youtube.com/watch?v=wR-_Rk2egTk
2023/01/11
7
0
Get to Know Us: Haley from Operations at Jane Street
Open
Jane Street
Get to Know Us | Meet Haley who is based in Jane Street's New York office. She works on the Operations team and has been full time since 2017. The people featured in these videos were excited to share what makes Jane Street a rewarding place to work. Whether in Trading & Research, HR & Recruiting, or Cybersecurity, everyone plays a key part in fostering our open and collaborative working culture. While it’s hard to capture all the nuance of our firm in a series of short videos, we hope that they give you a real sense of who we are and what we do.
https://www.youtube.com/watch?v=3TKO6YFI4CY
2023/01/11
14
0
Get to Know Us: Alok from Research at Jane Street
Open
Jane Street
Get to Know Us | Meet Alok who is based in Jane Street's New York office. He works on the Research team and has been full time since 2020. The people featured in these videos were excited to share what makes Jane Street a rewarding place to work. Whether in Trading & Research, HR & Recruiting, or Cybersecurity, everyone plays a key part in fostering our open and collaborative working culture. While it’s hard to capture all the nuance of our firm in a series of short videos, we hope that they give you a real sense of who we are and what we do.
https://www.youtube.com/watch?v=df0A4XVlrTc
2023/01/11
12
0
Turbo-Charge Your Career with Python for Finance
Open
Yves Hilpisch
Introduction & Overview video for the Certificate Program in Python for Finance (Algorithmic Trading, Computational Finance, Asset Management). Slides and notebooks at http://bit.ly/cert_intro.
https://www.youtube.com/watch?v=lba7au5MOkk
2023/01/11
0
0
What are the challenges in the calculation of implied volatilities?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 3/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: @https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. What are the deficiencies
https://www.youtube.com/watch?v=KTffxCRX2WE
2023/01/11
13
5
How is the money savings account related to a zero-coupon bond?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 2/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. What are the deficiencies
https://www.youtube.com/watch?v=9lU18AppEpU
2023/01/09
16
5
Ensemble Meta-Labeling
Open
Hudson & Thames
Join the reading group! http://hudsonthames.org/reading-group/ This study systematically investigates different ensemble methods for meta-labeling in finance and presents a framework to facilitate the selection of ensemble learning models for this purpose. Experiments were conducted on the components of information advantage and modeling for false positives to discover whether ensembles were better at extracting and detecting regimes and whether they increased model efficiency. We demonstrate that ensembles are especially beneficial when the underlying data consists of multiple regimes and is non-linear. Our framework serves as a starting point for further research. We suggest that the use of different fusion strategies may foster model selection. Finally, we elaborate on how additional applications, such as position sizing, may benefit from our framework.
https://www.youtube.com/watch?v=tpLCMVyMOaM
2023/01/09
27
0
Time Value of Decisions
Open
Dimitri Bianco
So you got into graduate school and can't decide if you should accept the offer. There should be a specified about of time in which you need to make the decision. That time has value and you should utilize it. The future is uncertain and waiting to make the decision when you have the upper hand (the option to accept or decline) has value. This value of time only works when you have a clear advantage. If you are neutral, it is a long-term multi play game, or you are at a disadvantage then the time value may sit with the other party. This idea of time value can be applied to many life situations such as dating, job offers, lawsuits, and other business dealings. Website: https://www.FancyQuantNation.com Support: https://ko-fi.com/fancyquant 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
https://www.youtube.com/watch?v=BlVdq94QOpI
2023/01/08
29
5
Micro Alphas | Trading Alphas - Mining, Optimisation, System Design
Open
Quantra
**NEW COURSE LAUNCHED** Link: https://quantra.quantinsti.com/course/trading-alphas-mining-optimisation-system-design Welcome to this section on micro alphas. The efficient market hypothesis states that all information available to the market is contained in the current price. In other words, price movements are random and unpredictable. As a consequence, it would be impossible to consistently generate profits. This very academic view is a good baseline for further assumptions but it is easily overturned by looking at the performances of some of the most successful hedge funds in history. If you take Jim Simon’s Medallion fund, for example, there is less than a 1 in a quadrillion probability that their performance could have been achieved by chance. This is perhaps the most extreme example but there are many others. In reverse, if markets were truly efficient, there would be a diminishingly small probability of seeing managers and funds that consistently outperformed the market year over year for decades. In essence, this tells us that there exist ways to exploit these inefficiencies but it takes technical expertise and creativity to find them. In order to establish a baseline, let’s start with creating a series of stock returns at random. Plotting a histogram of a sufficiently large number of these returns, we can see that it has the characteristic bell-shape of a normal distribution. Next, we generate a series of 750 trading days (approximately 3 years) with a starting price
https://www.youtube.com/watch?v=CtfUcrfOI3s
2023/01/07
8
5
Can we use the same pricing models for different asset classes?
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 1/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. What are the deficiencies
https://www.youtube.com/watch?v=brKYNyyI2Zs
2023/01/06
8
5
Volatility Forecasting using Neural SDEs & NLP Models in Finance
Open
Quants Hub & BTRM
Volatility Forecasting using Neural SDEs with Projection Conditioning by Matthew Dixon NLP Models in Finance by Miquel Alonso Miquel Alonso will also be delivering a short info session on the AIFI program. The AIFI – Artificial Intelligence Finance Institute Winter Bootcamp takes place online: 13th February to 24th February 2023. The Artificial Intelligence Finance Institute’s (AIFI) mission is to be the world’s leading educator in the application of artificial intelligence to investment management, capital markets and risk. We offer one of the industry’s most comprehensive and in-depth educational programs, geared towards investment professionals seeking to understand and implement cutting edge AI techniques. Taught by a diverse staff of world leading academics and practitioners, the AIFI courses teach both the theory and practical implementation of artificial intelligence and machine learning tools in investment management. As part of the program, students will learn the mathematical and statistical theories behind modern quantitative artificial intelligence modeling. Our goal is to train investment professionals in how to use the new wave of computer driven tools and techniques that are rapidly transforming investment management, risk management and capital markets. To register, please submit your details. A brochure will also be emailed to you within 24 hours. If you have any questions, feel free to ask: geoff@wbstraining.com
https://www.youtube.com/watch?v=Ogf_SudEVMQ
2023/01/04
6
5
Computational Finance Q&A, Volume 1, Introduction
Open
Computations in Finance
Computational Finance Q&A, Volume 1, Question 0/30 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Materials discussed in this video are based on: 1) FREE online course "Computational Finance" is available at: https://www.youtube.com/playlist?list=PL6zzGYGhbWrPaI-op1UfNl0uDglxdkaOB @ComputationsInFinance 2) 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. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ - The slides can be found at: https://github.com/LechGrzelak/Computational-Finance-Course/tree/main/Questions-and-Answers - See https://quantfinancebook.com/ for more details and for additional materials. - Course syllabus can be found at: https://CompFinance.ddns.net/wordpress/free-courses/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This volume addresses the following questions: 1. Can we use the same pricing models for different asset classes? 2. How is the money savings account related to a zero-coupon bond? 3. What are the challenges in the calculation of implied volatilities? 4. Can you price options using Arithmetic Brownian motion? 5. What is the difference between a stochastic process and a random variable? 6. What are the advantages and disadvantages of using ABM/GBM for modelling a stock process? 7. What sanity checks can you perform for a simulated stock process? 8. What is the Feynman-Kac formula? 9. What is the implied volatility term structure? 10. Wha
https://www.youtube.com/watch?v=k6PKNGvCKKM
2023/01/03
50
5
Fundamental Analysis 2.0: Leveraging Data Science to Enhance Your Investment Process
Open
HKML
In this course, you will learn how to use data science techniques to enhance your fundamental analysis process and make more informed investment decisions. You will start by gaining a strong foundation in the basics of data science, including concepts such as data exploration, visualization, and statistical analysis. Next, you will learn how to apply these concepts to real-world equity analysis scenarios, using tools such as Python and the pandas library to manipulate and analyze financial data. You will also learn how to use machine learning algorithms to identify patterns and trends in data, and how to use these insights to inform your investment decisions. Throughout the course, you will work on hands-on projects and case studies to apply your knowledge and skills in a practical setting. By the end of the course, you will be well-equipped to use data science to complement your fundamental analysis skills and make more informed investment decisions.
https://www.youtube.com/watch?v=D53LJGwMtYA
2023/01/02
3
0
Work Dilemma? What I Recommend
Open
Dimitri Bianco
A subscriber asked, "Dimitri, I’m a newly grad on a validation team and I have deep concerns about my team’s practices. I’m not experienced in finance and I only have a bachelors. I feel this does not give me credibility and falsifies my concerns. Any advice?" Today I talk about asking questions and avoiding conflict as the effort and end result usually isn't worth while.
https://www.youtube.com/watch?v=HnsOm7xsG0I
2023/01/01
16
5
Creation of an Options Screener | Systematic Options Trading Strategies
Open
Quantra
Course on Systematic Options Trading: https://quantra.quantinsti.com/course/systematic-options-trading Welcome to this video on the creation of an options screener. After completing this video, you will understand the need to screen options, and how to reduce the option chain to a manageable set. While screening options you will use parameters such as expiry dates, bid and ask prices, open interest, and strike price. John has learned about options and is excited to try his luck with options. On 16th May 2022, the S&P 500 Index is at 4025. John thinks that the fall in S&P 500 is temporary. And he believes that the S&P 500 in the month of June will cross 4500. He buys the SPX Call option for $1. This call option had a strike price of 4500 and an expiry date of 17 Jun 2022. Lo and behold, after a few days due to fear of recession in the US and high inflation, the S&P 500 fell by 3%. The option which John had purchased has no bids or buyers. There is no volume either. John is stuck and faces the risk of options expiring worthless and losing out on the premium he paid to purchase the options. He goes to Sophie. Sophie is a school friend of John and an expert in options trading. John explains to Sophie what happened to him while trading options. That he was stuck in option, there were no buyers and no volume. And asks Sophie for advice. Sophie tells John that there were multiple mistakes he did, including not selecting the right options. Theoretically, options can make unlimited pr
https://www.youtube.com/watch?v=-zL39HfJHWQ
2022/12/30
10
5
Systematic Trading Process | Systematic Options Trading Strategies
Open
Quantra
Course on Systematic Options Trading: https://quantra.quantinsti.com/course/systematic-options-trading Welcome to this video on the systematic trading process. This video will serve as a step-by-step guide for the process of systematic options trading. Let's start with the very first component of building a trading system, which is the options data. You start with getting the data, or in other words, retrieving the data. And there are two ways to go about it. You can either purchase it from a vendor or you get it from a free web resource, for example, Yahoo Finance or Google Finance. Once you have the data you have to store it in a proper structure. With this, you have created a database for carrying out analysis. For storing the data, again, there are two parts here. First is the data structure for an underlying asset. Here, the bare minimum data that you need is the OHLCV data, or in other words, the price and volume data. If you have the information related to corporate events, then that’s even better. The second part of the data structure is the options data. This includes the volume data, bid/ask quotes, open interest, strike price, expiry, and the last traded price. Once you’ve retrieved and stored the data, you have to make sure that its quality is top-notch. It has to be error-free and the reason for that is very simple. The better the quality of your input data, the better would be the output. Now you have a quality database but the problem is that it's too huge. The
https://www.youtube.com/watch?v=H5jA24BsKTo
2022/12/23
9
0
Risk Tiers and Model Definitions
Open
Dimitri Bianco
What are models, EJMs, and non-models? What are risk tiers and how do we set risk tiers? In this video I will discuss the importance of setting definitions and risks tiers for risk management. This sort of task is typically done by teams at firms called, "governance" or "model governance." Risk tiers and definitions are critical as they help assign resources and define what different teams do within a financial institution. These ideas can easily be applied to any firm using models. Website: https://www.FancyQuantNation.com Support: https://ko-fi.com/fancyquant 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
https://www.youtube.com/watch?v=8seNl7jdsQQ
2022/12/18
20
5
Peter Carr Brooklyn Quant Experience Seminar Series: Dilip Madan
Open
NYU Tandon School of Engineering
The Department of Finance and Risk Engineering welcomed Dilip Madan to the PC BQE Seminar Series on December 8, 2022. Title High Dimensional Markovian Trading of a Single Stock
https://www.youtube.com/watch?v=Emli6CL2uy0
2022/12/15
4
0
Peter Carr Brooklyn Quant Experience (BQE) Seminar Series: Bruno Kamdem
Open
NYU Tandon School of Engineering
The Department of Finance and Risk Engineering welcomed NYU FRE Adjunct Professor Bruno Kamdem to the PC BQE Seminar Series on December 1, 2022. Title A Reinforcement Learning Mechanism for Trading Wind Power Futures
https://www.youtube.com/watch?v=GSsH3A39hbo
2022/12/15
2
5
PortfolioLab Demo Video
Open
Hudson & Thames
PortfolioLab: Portfolio optimisation and allocation library A modern guide to portfolio optimisation: https://hudsonthames.org/modern-guide-to-portfolio-optimization/
https://www.youtube.com/watch?v=zy7a5KML3T0
2022/12/15
2
0
MlFinLab Demo Video
Open
Hudson & Thames
Learn more: https://hudsonthames.org/mlfinlab/ MlFinLab is a collection of production-ready algorithms (from the best journals and graduate-level textbooks), packed into a python library that enables portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
https://www.youtube.com/watch?v=tvm5zdGo-CQ
2022/12/15
30
5
ArbitrageLab for Pairs Trading
Open
Hudson & Thames
An overview of ArbitrageLab, Hudson and Thames' flagship pairs trading python library. Statistical arbitrage pairs trading strategies: Review and outlook: https://www.econstor.eu/bitstream/10419/116783/1/833997289.pdf The definitive guide to pairs trading: https://hubs.ly/Q01vKtLX0
https://www.youtube.com/watch?v=yFbn5ntlxLU
2022/12/13
4
5
Experimental Design & Common Pitfalls of Machine Learning in Finance: The Four Horsemen
Open
Hudson & Thames
Join the Hudson and Thames Reading Group: https://hudsonthames.org/reading-group/ The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series, focuses on the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance. The most significant barrier to the adoption of machine learning in financial services is the perception that the technology is too complex and risky. This is despite the fact that there are many well-established machine learning techniques that have been proven to work in the financial markets. Another barrier to adoption is the lack of skilled personnel who understand both machine learning and finance. Additionally, there is a lack of understanding of how to effectively use machine learning within the context of financial markets, making it difficult to determine what techniques are most appropriate and how best to use them. In conclusion, the adoption of machine learning in the financial services industry is hindered by a lack of understanding.
https://www.youtube.com/watch?v=sDhrwJ-fG0c
2022/12/13
8
0
Experimental Design & Common Pitfalls of Machine Learning in Finance: Backtesting
Open
Hudson & Thames
Join the Hudson and Thames Reading Group: https://hudsonthames.org/reading-group/ The second lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series, focuses on a protocol for backtesting and how to avoid the 7 sins of backtesting. By implementing the research protocol outlined in these articles, an investment manager can avoid making the seven common mistakes when backtesting and building quant models. The protocol also provides a framework of guidelines that can be used to structure a more optimal investment process. Additionally, the article compares traditional active portfolio management with the new trend of smart beta/factor portfolios. Through this protocol, an investment manager can have an increased chance of meeting or exceeding expectations in live trading.
https://www.youtube.com/watch?v=kUtRdaY59RU
2022/12/13
14
0
Python Isn't That Great
Open
Dimitri Bianco
I've used python over the years however in the past 5 months I have been using it as my primary language. Subscribers have been asking what I think about it and so here is my OPINION. For quants building models and working with data python can do a lot however it takes a lot more code to do the same tasks as SAS. Often the results are close to correct but being open source there is no guarantee that it is actually correct. Packages are available for many things however there are many packages missing or not built very well. But again what do you expect when it is free? It does shine in doing machine learning in comparison to SAS. I do wish SAS would add a lot of the ML models to SAS EG. As a manager it also takes a lot more training on how to do statistical model development as the output in python is very limited. Again, it takes a lot more code and expertise to get the tables, tests, and results one should be using to build and select models. Can python do anything? Yes! Is it the most efficient, complete, and accurate language for statistical model development? No. 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
https://www.youtube.com/watch?v=Aw9PKrdf6Sc
2022/12/11
0
0
ABFR Webinar with Alberto Rossi and David Hirshleifer
Open
AI & Big Data in Finance Research Forum
Crowdsourcing Peer Information to Change Spending Behavior Presenter: Alberto Rossi (Georgetown University) Discussant: David Hirshleifer (University of Southern California) Host: Lea Stern (University of Washington in Seattle) Dec 01, 2022 12-1pm Eastern Time 00:00:00​​​ Welcome remarks 00:01:57 Presentation 00:31:47​​​ Discussion 00:52:15 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, Serhiy Kozak, Markus Pelger, and Lea Stern. 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.
https://www.youtube.com/watch?v=c_Bu4iJmS4Y
2022/12/09
3
5
Load more
TOP