.svg)
Machine Learning & Quantitative Finance
ArXiv Papers
Stream
Week
Month
Load more
SSRN Papers
Stream
Week
Month
Random
Load more
GitHub Code
Stream
Week
Month
Load more
Papers with Code
Stream
Week
Month
Load more
RePec Papers
Stream
Week
Month
The future of the reading: interaction between professional knowledges social actors and the context
Load more
Journal Trending
Stream
Week
Month
Load more
Video Feed
Stream
Week
Month
Load more
Podcast Feed
Stream
Week
Month
Load more
Twitter Trending
Stream
Week
Month
Load more
News Trending
Stream
Week
Month
ML Trending
Stream
Weekly
Monthly
Load more
LinkedIn Trending
Stream
Week
Month
Name
Date
Link
Content
ML Score
Likes
Gautier Marti
Open
2023/01/01
Happy New Year 2023!My yesterday post on using ChatGPT to write a primer on #quant #trading using #machinelearning got an unexpected reception: 250,000 views! with lots of engagement as well.To continue on the same theme, below some nerdy rap lines on #quant #trading by #ChatGPTVerse 1:I'm all about the numbers, trading with precisionModeling market conditions, with mathematical precisionI leave the emotions at the doorMy strategy is based on algorithms and moreChorus:I'm a quant, trading with my mindData-driven, leaving the emotions behindI let the numbers do the talkingMy portfolio always on the rise, never stallingVerse 2:I analyze market trends and patternsUsing advanced techniques, my returns are never flattenin'I'm constantly learning, always improvingMy portfolio growing, my strategies always movingChorus:I'm a quant, trading with my mindData-driven, leaving the emotions behindI let the numbers do the talkingMy portfolio always on the rise, never stallingBridge:Some may call it cold and calculatedBut to me, it's just how I navigatedThe markets, with a data-driven approachMy success speaks for itself, beyond reproachChorus:I'm a quant, trading with my mindData-driven, leaving the emotions behindI let the numbers do the talkingMy portfolio always on the rise, never stallingMikołaj Bińkowski How is your text-to-speech #AI progressing? Can it turn that to music? 
4
38
Igor Halperin
Open
2023/01/01
The #NFT to the MIT Sloan School of Management Quant & Ai Conference created by the awesome Samson Qian !One last huge thanks to the many brilliant speakers that made it such a special experience!!Igor Halperin Matthew Dixon Gary Marcus Ruchir Puri Carson Boneck, CFA Tony Berkman Raphael Douady Gordon Ritter Alfred Spector Jim Kyung-Soo Liew, Ph.D. Sondra Campanelli Lisa Huang Gilbert Haddad Joseph Simonian, Ph.D. Saeed Amen Christina Qi Yevgeniy Vahlis Philip Ndikum Carol Alexander Dhagash Mehta, Ph.D. Nikita Fadeev Gregory Pelts Bart BaesensJeff Adams Swagato Acharjee George Mylnikov Larry Tabb Mika Kastenholz Mike Agne Kathryn Zhao Arik Ben Dor Peng Cheng Daniel NewmanHan CuiGene Ekster Divya Narendra Joe Marenda
3
29
Antoine Savine
Open
2023/01/01
I’m happy to share that I’m starting a new position as Head of Macro Analytics at Hudson River Trading!
3
62
Gautier Marti
Open
2022/12/31
How much does ChatGPT know about quant trading?Let's figure it out!All of the attached book content was generated by ChatGPT from title, to chapters, sections and sub-sections, and the illustration Python/ML/Quant code as well!Even this abstract:"Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that leverage large amounts of data and advanced modeling techniques. In this book, we provide a comprehensive overview of machine learning for quantitative trading, covering the fundamental concepts, techniques, and applications of machine learning in the financial industry.We start by introducing the key concepts and challenges of machine learning for quantitative trading, including feature engineering, model selection, and backtesting. We then delve into the various machine learning approaches that are commonly used in quantitative trading, including supervised learning, unsupervised learning, and reinforcement learning. We also discuss the challenges and best practices of implementing machine learning models in the live market, including the role of data quality, the importance of risk management, and the need for ongoing model monitoring and validation.Throughout the book, we provide numerous examples and case studies to illustrate the concepts and techniques discussed, and we also include practical tips and resources to help traders and practitioners get started with machine learning for quantitative trading. This book is an essential resource for anyone looking to gain a deeper understanding of how machine learning is transforming the world of finance."#quant #trading #chatgpt #book #ai #ml #machinelearning #learning #finance #data
17
1167
jean-philippe bouchaud
Open
2022/12/31
Fat tails & Diversification I have been fascinated by non Gaussian, fat-tailed distributions ever since I began doing science. My very first paper in 1984 (with P. Le Doussal) was on the so-called “Sinai billiard”, i.e. a point particle bouncing off an infinite, regular array of perfectly elastic, circular obstacles. As proven by Y. Sinai, this is an ergodic system, but with a particular property: the particle undergoes super-diffusion, i.e. its mean square displacement grows faster than linearly with time. The reason is that the particle manages to occasionally find itself on trajectories that travel very far before hitting the next obstacle. In fact, the distribution of distances before the next collision has a diverging variance. The resulting motion is a "Lévy flight”, not a Brownian motion (see Figure). Perhaps by accident or perhaps because I have a special penchant for these types of systems, my subsequent work in statistical physics gyrated around fat-tailed distributions, on the shoulder of the great Benoît B. Mandelbrot. (Q: Do you know what the second B. stands for? A: Benoît B. Mandelbrot…). Fat-tailed distributions arise naturally in many physical systems (avalanches, earthquakes, glassy dynamics, etc.) but also of course in economic and financial systems (wealth, firm sizes, price returns, etc.). This is actually what lured me into quantitative finance: the total disconnect between the Gaussian world of Black & Scholes, where perfect hedging is possible, and the fat-tailed world of financial markets that makes perfect hedging impossible and Black & Scholes a very bad theory to account for option smiles. These ideas led me to join CFM and are at the core of our volatility arbitrage program since 2005. One of the defining feature of fat-tailed random variables is the dominance of rare, but large events. Draw N independent, identically distributed random
4
862
Álvaro Cartea
Open
2022/12/31
Really excited to share this work with Bruno Dupire on functional expansions. Bonne lecture! Abstract: Path dependence is omnipresent in social science, engineering, and finance. It reflects the influence of the past on the future, often expressed through functionals. However, non-Markovian problems are often infinite-dimensional, thus challenging from a conceptual and computational perspective. In this work, we shed light on expansions of functionals. First, we treat static expansions made around paths of fixed length and propose a generalization of the Wiener series−the intrinsic value expansion (IVE). In the dynamic case, we revisit the functional Taylor expansion (FTE). The latter connects the functional Itô calculus with the signature to quantify the effect in a functional when a "perturbation" path is concatenated with the source path. In particular, the FTE elegantly separates the functional from future trajectories. The notions of real analyticity and radius of convergence are also extended to the path space. We discuss other dynamic expansions arising from Hilbert projections and the Wiener chaos, and finally show financial applications of the FTE to the pricing and hedging of exotic contingent claims.#quantitativefinance #mathematics
4
76
Jonathan Regenstein
Open
2022/12/31
just making my way through Housing is the Business Cycle by Edward Leamer, and thanks for the recommendation Francois Trahan.I will reproduce a few results from the paper over the weekend. Attached is a quick chart showing residential investment as percent of GDP, with recessions shaded pink. The time series goes back to 1950. This time series tends to peak in advance of recessions and trough at the end of recessions. If the current trend continues downward, it's further confirmation of a recession in 2023. This chart wasn't on my radar until now. Highly recommend the Leamer paper!
3
36
Markus Leippold
Open
2022/12/31
As an #NLP and #AI enthusiast, I am always eager to learn about the latest developments in large language models (#LLM) like #chatgpt. I already interviewed #GPT3 (#davinci-002) about #climatechange last September. Now #openaccess at #Finance Research Letters: https://lnkd.in/eMGC-aXQ But we must remember what exactly these models do and how they work. Sometimes, it reminds me of my time as a student before the big exams, when we had to memorize tons of material (admittedly) without having the time to understand everything. I guess it's kind of like that with LLMs. Also, certain classification tasks can't (yet) be solved as well with LLMs as with, e.g., a fine-tuned BERT model. So I'm excited to see where LLMs will take us in the world of NLP, and I look forward to exploring their potential even further in the future. Happy New Year!#research #future #work #language #climatechange University of Zurich - Department of Banking and FinanceSFI Swiss Finance Institute
3
68
Ariel Silahian
Open
2022/12/31
We are doing #AI the wrong way. It is a dead-end road.Terminators are not coming anytime soon.Having learned about the basics of the human brain (#neuroscience) and its mechanisms, I strongly believe that what we know about Artificial Intelligence is all wrong.I also believe that digital computing is not the way. Probably, an unknown technology (but not digital).Clearly, we are way behind on the available technology (probably 1000s of years)Computing AI and Deep Learning could be way faster than a human being's brain... but by no means smarter. This is not a recommendation, is just my humble opinion.#technology #artificialintelligence #deeplearning #computing #digital Tesla
2
19
Joris Bastien
Open
2022/12/31
Quantaraxia, LLC, Joris Bastien, me, myself and I wish you all a happy new year !!!I also specifically want to thank my clients for their continued support. This is very much appreciated, as truth, loyalty and integrity is everything to me.Let's keep building in 2023 on the momentum we've already build this year, specifically in the crypto space. 2023 is the one when everything changes.Only a selected few will be part of this journey, as I need partners, not only investors.I'm excited for what's coming and if you want to know more, please reach out.In the meantime, enjoy your time with family and friends, and again, HAPPY NEW YEAR !!!#quantaraxia #money #management #investing #markets #hedgefunds #business #entrepreneurship
2
11
Load more
COUNT1329
Reddit Trending
Stream
Week
Month
Title
Date Posted
Permalink
Score
Subreddit
I wrote a tiny library this morning for openai's (suddenly really good, really cheap) embeddings to search stuff - Semantic search, smarter replies w/ GPT-3, easier. (r/DataScience)
Open
2023/01/23
8
datascienceproject
Should I Jump ship to Citadel
Open
2023/01/23
21
quant
Best platform for historical intraday API
Open
2023/01/23
23
algotrading
Looking for 5-6 collaborators for Jane Street puzzles
Open
2023/01/22
28
quant
Looking for 5-6 collaborators for Jane Street puzzles
Open
2023/01/22
35
quant
How does this derivation work?
Open
2023/01/22
47
quant
Do you guys come up with new indicators ?
Open
2023/01/22
17
algotrading
If you could start from the beginning without much knowledge, what would your path in learning algo trading be like?
Open
2023/01/22
44
algotrading
Stationarity of time series for copula modeling
Open
2023/01/21
16
quant
Hello World Programs for API Stock Trading
Open
2023/01/21
66
algotrading
Load more
ᗰᒪ & Ɋᑌᗩᑎ丅 ᖴIᑎᗩᑎᑕᗴ
Join the Slack group to send any feedback and chat with like-minded researchers. We will probably restrict access at 1000 to maintain conversational quality in the next year; currently 743 members.