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҉ ᴍʟ & ǫᴜᴀɴᴛ ғɪɴᴀɴᴄᴇ

A Very Comprehensive Source of Financial Machine Learning, Data Science, and Quantitative Finance Research.
Use this site to explore Seminar Resources | Trending Research (Top 25) | Interesting Links and more.
Open and search in any database by clicking the name for example GitHub Repos | ArXiv | SSRN and others.
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Code, Seminars, and Links

GitHub Repos
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The repositories are sorted by last updated, click the arrow symbol next to the table name to open a large explorable table where you can sort by 'stars' ☆ and day created.
Emerging Manager Essays
A
Employee at MIT endowment fund giving advice to aspiring managers
Articles
Quantitative Tightening Step-by-Step - Fed Guy
A
Interesting Blog on how quantitative tightening might evolve
Articles
DLM-Masterclass-v2-talk.pdf - Dropbox
A
Derivative pricing machine learning Savoine
Articles
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The table snippets are derived from bots, as such we expect around 20-30% errors, if you see anything untoward please get in touch and we will fix it as soon as possible.

Articles, Video, and Audio

There has been a few requests by users who have their own feed aggregators for RSS feeds: Blogs, YouTube, Podcasts (the podcast feed can be loaded directly into your favorite podcast player)

Social Media Feeds

Note: all the links are derived from publicly available posts, please refer to the privacy settings on the individual applications like Twitter, LinkedIn, and Reddit to change your preferences.
Twitter
Search
Name
Tweet URL
Tweet
Published
https://twitter.com/DWongResearch/status/1483724149029687299
OMG, why cannot we just universal standard for symbol and exchange name combinations, people? This amount of work to manage a securities master plus just doing some random munging across platforms is just suffering. I am so annoyed whenever I need to create new mappings. https://t.co/fvvcXXVnzT
2022/01/19
investingidiocy
Open
https://twitter.com/investingidiocy/status/1483717270060867585
Short blog post on clustering https://t.co/w2M6Fm66R0
2022/01/19
DWongResearch
Open
https://twitter.com/DWongResearch/status/1483724149029687299
OMG, why cannot we just universal standard for symbol and exchange name combinations, people? This amount of work to manage a securities master plus just doing some random munging across platforms is just suffering. I am so annoyed whenever I need to create new mappings. https://t.co/fvvcXXVnzT
2022/01/19
investingidiocy
Open
https://twitter.com/investingidiocy/status/1483717270060867585
Short blog post on clustering https://t.co/w2M6Fm66R0
2022/01/19
macro_srsv
Open
https://twitter.com/macro_srsv/status/1483706362802720769
"Reinforcement Learning in stock prediction with FinRL [Financial Reinforcement Learning Framework]… helpful for understanding basics." https://t.co/TS4SFPQYCz https://t.co/9eKGKvG2Uj
2022/01/19
JigneshTrade
Open
https://twitter.com/JigneshTrade/status/1483666363139104768
Some levels are very critical for bigger range expansion in Banknifty. 37500+700 = 38200 35000-700 = 34300 https://t.co/IOvK6SHExb
2022/01/19
saeedamenfx
Open
https://twitter.com/saeedamenfx/status/1483567962825056260
18Jan22 / #365APoem / Excuse anew, nobody warned me, means?
2022/01/18
msamonov
Open
https://twitter.com/msamonov/status/1483563911349784577
"Selling Out: “...it certainly doesn’t make sense to sell things just because they’re up.” @HowardMarksBook @Oaktree https://t.co/zy2opZ2qh6
2022/01/18
IBKR_QB
Open
https://twitter.com/IBKR_QB/status/1483489367930904579
In this webinar @TradingCentral Head of North American Research Gary Christie will dive into technicals looking for trading opportunities in stocks that could have a large impact on the Metaverse: https://t.co/w8qWwVjKLW #FinTech https://t.co/WmyuRmmom9
2022/01/18
ArturSepp
Open
https://twitter.com/ArturSepp/status/1483485054852546561
It should have been common prior to 2000 when both equities and long term bonds were tightly linked to inflation expectations. The negative bond-equity correlation is a feature observed between 2000 and 2020. No cheap tail risk hedge with treasuries anymore... https://t.co/KUwsIQAq8Y
2022/01/18
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LinkedIn
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Name
Link
Content
ML Score
Date
Likes
Jacques Joubert
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889875418116091904
I'm looking for #quantitativefinance industry partners for #FinancialDataScience projects at #Cornell in 2022. These are collaborations between the financial industry and Cornell Financial Engineering Manhattan (#cfem) where groups of students and faculty advisors work on practical industry problems. We are particularly interested in topics related to #fintech, #alternativedata #cryptocurrencies, #machinelearning, #financialengineering and #bigdata. If you're interested please message me. Here is a video where I describe some of our 2021 project topics:
3
2022/01/20
0
Petter Kolm
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889608346438008834
I recently interviewed the eminent physicist and expert practitioner jean-philippe bouchaud for Top Traders Unplugged. This should be of interest to listeners interested in price impact models, sources of market volatility and a variety of other crucial topics within the "econophysics" field. Thanks.
5
2022/01/19
73
Petter Kolm
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889607898683469824
"In the words of [SFI Co-founder] Nobel laureate physicist Philip Anderson, we need to free ourselves from 'average' thinking, or focusing on the mean, which, in most cases, is misleading."Harvard Business Review on bell curve assumptions vs. power law realities:https://lnkd.in/dbNpTMEC
5
2022/01/19
233
Igor Halperin
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889590465247625216
A big thanks to jean-philippe bouchaud for a great conversation with Hari P Krishnan on today's episode. Perhaps the most "high-level" conversation we have ever brought to you...giving Hari a run for his money :) https://lnkd.in/gKNEZuh #trendfollowing
3
2022/01/19
63
Graham Giller
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889405743284420609
I just published Americans are Expecting Inflation to Decline from Current Levels https://lnkd.in/dNdhNPDR #finance #datascience #stockmarket #inflation #economics
3
2022/01/19
7
Saeed Amen
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889662537420296193
Paul Bilokon, PhD #Kudos I’ve learnt so much from you over the years about coding, quant, markets and more broadly!!!
2
2022/01/19
7
jean-philippe bouchaud
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889231345290219520
My lectures “From Statistical Physics to Social Sciences” start tomorrow at ENS, Paris. Teaching is a great way to keep in touch with both cutting-edge academic research and with upcoming talents. The lectures are open to physics, math and economics students, with the hope that bright minds with a double curriculum will be able to transpose ideas that emerged in the last decades in the context of complex systems (strongly interacting systems like spin-glasses or constraint satisfaction problems; network theory; self-organized criticality, etc.) into something useful to deal with the “unfortunate complexity” of economic/financial systems, which cannot be accounted for with rational, non-interacting agents.  https://lnkd.in/gxKtcd52 When agents influence each other in all sorts of way, unexpected phenomena can emerge at the macro-scale, and their description require appropriate tools that are still little known in economic and social science departments.   For those who speak French, my lectures at Collège de France last year cover part of what I will speak about at ENS, see:  https://lnkd.in/gCjgpHKx https://lnkd.in/ghacYdh2#complexsystems #teaching #complexity
7
2022/01/18
182
Igor Halperin
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889211722125590528
Hate how slow Pandas is for large datasets but still can't ditch it?Then, Terality - a blazing fast Python package with the exact same syntax as Pandas might be the solution. Check out my article on it to find out more.#pandas #bigdata #python
5
2022/01/18
67
Robert Martin
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889170331337154560
Welcome to the first in my series on systematic trading, credit trading, and where the two meet. To keep things tidy I will periodically take them down and combine them into a Word doc, Comments always welcome and if anyone wants a discussion on a particular topic, narrow or broad, just shout. #credit #trading #strategiesA systematic trading strategy means that the position in the traded instrument, theta_t say, is a deterministic function of information known at time t. The process of design/fitting is simply to decide what that function is: what it depends on, and how.Some models depend only on returns (changes) in the underlying: momentum models form an important part of that category. They are agnostic to the level, and will simply buy when the market is going up, sell when it is going down: here, going up/down is usually assessed by comparing moving averages of different speed. Other models are level-dependent, and relative value models fall into this category: they assess whether an asset is rich or cheap.I like to make another distinction between models. Much effort goes into statistical fitting, by which I mean we assess the mean and variance of what we are trading, and the position is then a function of those quantities. According to what is known as the Kelly criterion, the optimal position, in a mean-variance sense, is proportional to the mean and inversely proportional to the variance. That is to say, theta_t is  mu_t/sigma_t^2 times some scaling parameter. This can be thought of as risk-adjusted return (mu/sigma) divided by standard deviation. NB: mu/sigma has dimension time^( -1/2).  A further discussion is in eq.(5) of the attached paper, which goes much further than needed here.My preferred approach is different, and I call it 'designed': we just specify theta_t via a suitable function, dependent on a small number of calibration parameters, and then we
3
2022/01/18
129
Peter Cotton, PhD
Open
https://www.linkedin.com/feed/update/urn:li:activity:6889303249023725568
An open source developer told big business to "fork off". Nobody listened. So a while later, the developer pushed some breaking changes, and all hell broke loose. GitHub suspended his account ... for having the temerity to modify his own code! Where do you stand? Does free-riding for long enough grant business some version of squatter's rights? #opensourcedevelopment #opensource
2
2022/01/18
17
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Reddit
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Title
Permalink
Date Posted
Score
Have you ever rented bare metal GPU machines to run your machine learning/ai models?
Open
https://www.reddit.com/r/algotrading/comments/s8ah4g/have_you_ever_rented_bare_metal_gpu_machines_to/
2022/01/20
44
tda-api-client v2.0.0 is out for Node
Open
https://www.reddit.com/r/algotrading/comments/s7n74v/tdaapiclient_v200_is_out_for_node/
2022/01/19
33
What are the professional quant's takes on Forex?
Open
https://www.reddit.com/r/algotrading/comments/s6vrdt/what_are_the_professional_quants_takes_on_forex/
2022/01/18
58
Interactive Brokers streaming live market data
Open
https://www.reddit.com/r/algotrading/comments/s735a2/interactive_brokers_streaming_live_market_data/
2022/01/18
43
Unlimited Market data (circumventing server limits.)
Open
https://www.reddit.com/r/algotrading/comments/s60ab2/unlimited_market_data_circumventing_server_limits/
2022/01/17
34
Building a probabilistic model to predict the chances of a bank run based on historical deposits/withdrawals
Open
https://www.reddit.com/r/algotrading/comments/s5cc0l/building_a_probabilistic_model_to_predict_the/
2022/01/16
78
How do you model news impulses?
Open
https://www.reddit.com/r/algotrading/comments/s4p5tu/how_do_you_model_news_impulses/
2022/01/15
20
For those here who are data scientists by profession…
Open
https://www.reddit.com/r/algotrading/comments/s4w20y/for_those_here_who_are_data_scientists_by/
2022/01/15
101
Diversification over systems or markets?
Open
https://www.reddit.com/r/algotrading/comments/s3suig/diversification_over_systems_or_markets/
2022/01/14
32
R in Finance conference, call for presentations is open!
Open
https://www.reddit.com/r/algotrading/comments/s2secr/r_in_finance_conference_call_for_presentations_is/
2022/01/13
36
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Preprints

Around 50% of the papers are unrelated to Quant finance, we cast a wide net so that you can use the search functionality, to do that simple enlarge the chosen dataset e.g., RePec and search
Papers that have a more quantitative economics slant, which includes some download statistics.

Journals

This feed is very experimental, it can be a bit hit an miss, it scours the internet for papers that are available for download, note I just point to these papers the coverage is not always great.
Title
Author
Abstract
Published
Journal
Download
View
Year
The one-year non-life insurance risk
Open
Dorothea Diers and Martin Eling and Christian Kraus and Marc Linde
In this paper the authors present a dependence model for non-life-insurance risk based on risk factors, analogous to those generally used for life insurance or
2022/01/21
https://www.sciencedirect.com/science/article/pii/S0167668709000638
https://mega.co.nz/#!qG4xiAzT!L3awteJNYgxH-bGpX5DT-pHVP9zU0ibj5zWyOE6qNSs
https://sci-hub.st/10.1108/jrf-04-2013-0036
2013
Selectivity and market timing ability of fund managers: Comparative analysis of Islamic and conventional HSBC Saudi mutual funds
Open
Zouaoui
In this study, we evaluate performance of the Indian fixed income mutual funds using a comprehensive sample of funds over a period from April 2015 to March 2020. We examine selection ability, market timing ability and persistence of performance of 254 fixed income funds across 16 fund categories. Using monthly holding period returns for the fund and 22 different benchmarks, we assess (a) selection ability of funds using single- and multi-factor models; (b) timing ability of funds using Treynor-Mazuy model; and (c) performance persistence of funds using recursive portfolio formation test. Our findings indicate inferior securities selection ability, positive market timing ability for some intermediate duration and longer duration funds, and positive performance persistence for the top-decile and bottom-decile funds. Findings also indicate that credit spread has a stronger role than term spread in performance of Indian fixed income funds. This is possibly the first comprehensive study that examines the performance of Indian fixed income mutual funds and contributes to the growing body of research on performance evaluation of fixed income funds. In light of the prominence of fixed income securities, the findings of this study are all the more pertinent to investors, asset management companies and policy-makers.
2022/01/20
https://www.mdpi.com/527520
https://mega.co.nz/#!rbYzGCZL!Pz-VHNT5eNyZIs8lsQ0kAA4kO3ZoIYOBSgcMNrC-vRg
https://sci-hub.st/10.3390/ijfs7030048
2019
A structural credit risk model based on purchase order information
Open
S Yamanaka;M Kinoshita
This paper proposes a credit risk model based on purchase order information to address the deficiencies of monitoring methods that use only financial
2022/01/19
https://www.boj.or.jp/en/research/wps_rev/wps_2018/data/wp18e11.pdf
https://mega.co.nz/#!bbpgjJJY!zf9vn8kd7Zy4hEB5BWGAFBYbX_fv0iJVLivpqjMEM0Y
https://sci-hub.st/
2018
Heterogeneity and Biases in Inflation Expectations
Open
CARLOS CAPISTR{\'{A}}N and ALLAN TIMMERMANN
This study examines asymmetry in loss functions of consumer’s perceived and expected consumer price index inflation in Japan. We find strong upward bias of perceived and one-year-ahead inflation expectations, and evidence against rationality under symmetric loss functions. We find considerable evidence of asymmetric loss in perceived and expected inflation and support for rationality upon assuming asymmetric loss functions. Strong biases in consumers’ perceived and expected inflation result from asymmetric loss rather than irrationality. Using epidemiology models, we find that expected inflation is strongly related to perceived inflation with no significant role for actual inflation. Moreover, consumers gradually incorporate central bank forecasts, but not professional forecasts, into their inflation expectations. This indicates that asymmetric loss in perceived inflation is important in forming inflation expectation. The central bank should take into consideration the asymmetric loss in consumers’ inflation expectations and the close relationship between the inflation expectations and perceived inflation in formulating monetary policy.
2022/01/18
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.876.7334&rep=rep1&type=pdf
https://mega.co.nz/#!zHph1KzY!-1MvmrM-uMoRqJhYAVkuLpf0rD0t1LyqZ0imLS0BheM
https://sci-hub.st/10.1111/j.1538-4616.2009.00209.x
2009
Bank-sourced transition matrices: Are banks' internal credit risk estimates Markovian?
Open
Barbora {\v{S}}t{\v{e}}p{\'{a}}nkov{\'{a}}
This study explores banks’ internal credit risk estimates and the associated banksourced transition matrixes.
2022/01/17
https://www.econstor.eu/handle/10419/203221
https://mega.co.nz/#!We5i2AIK!pdcBHkkEtdo-D5EF-pEpAURgZWeVmn116rKYrM3dwHc
https://sci-hub.st/10.21314/jcr.2021.015
2022
The roles of big data in the decision-support process: an empirical investigation
Open
Thiago Poleto and Victor Diogho Heuer de Carvalho and Ana Paula Cabral Seixas Costa
Artificial intelligence (AI) has progressed to the point that it is now integrated into real-world decision support systems and significantly impacts decision-making in management. It is used in a range of areas, including computer science and information technology. This research presents the Integrated Multi-Criteria Decision-Making Model (IMCDMM). Deep algorithms are used to make multi-criteria derivations. This paper focuses on a subset of the components that come together to form an integrated decision-making model that incorporates big data, business intelligence, and decision support systems for organizational learning to deliver the decision-maker with a consistent visualization of decision-related opportunities. The proposed research can provide a comprehensive literature analysis demonstrating how deep learning algorithms and multi-criteria decision-making techniques can be applied to handle significant managerial data problems. Besides, this study discovers by merging multiple criteria with deep learning technologies to construct a decision-support system for big data processing. The proposed IMCDMM framework achieve an acceptability ratio of 92.4%, a performance ratio of 94.5%, an efficiency ratio of 95.5%, a satisfactory user level of 91.8%, a prediction ratio of 93.7%, a decision-making level of 91.5%, and the precision ratio of 92.5% when compared to other methods.
2022/01/17
https://link.springer.com/chapter/10.1007/978-3-319-18533-0_2
https://mega.co.nz/#!ifx2hTrJ!ifOCHJw4R99j3usnRivIVtpj4Yjjj0Yg76XRZKKR8WY
https://sci-hub.st/10.1007/978-3-319-18533-0_2
2015
ESG Investing through ETFs-An effective way to circumvent volatility?
Open
A Strignert;E Malm
This study examines the performance of 49 so-called ESG ETFs in the UK. These funds apply environmental, social and governance criteria in their investing strategies. Raw and risk-adjusted returns are estimated with standard methodology including the Capital Assets Pricing Model, the Fama and French (Journal of Financial Economics 116:1–22, 2015) Five-Factor Model, and the Sharpe and Treynor ratios. On average terms, no significant alpha is achieved by ESG ETFs in the UK, whereas there are not differences in Sharpe and Treynor ratios between ETFs and their benchmarks. However, some empirical evidence obtained indicates that ESG ETFs outperform the FTSE 100 Index, which stands as a proxy for the UK stock market. Along with performance, we examine whether investors award responsible ETFs by entrusting more money to them. However, no significant relationship is found between the ESG rating of ETFs and their assets. On the contrary, it is revealed that the return of ETFs is negatively related to their ESG metrics.
2022/01/17
https://lup.lub.lu.se/student-papers/record/9036881
https://mega.co.nz/#!LD52DLbB!myD3XJ3zZ7x5jzkBNSJSoWV3k3Qd2ZsQxVvsvN3XAsk
https://sci-hub.st/
2021
Aplicações de Algoritmos de Aprendizado de Máquina em CRM: Revisão Sistemática da Literatura.
Open
link.springer.com
Customer churn is detrimental to corporate revenue. Hence, accurate customer churn prediction is vital for enterprises to improve customer retention and corporate revenue. However, in e-commerce, there are challenges when predicting customer churn using traditional models. First, the conventional recency, frequency, and monetary (RFM) analysis based on single-source data can hardly accurately predict the e-commerce customer churn since such customers are often non-contractual. Second, in general, the data about e-commerce customers is high-dimensional and unbalanced, making traditional models even less effective. In this paper, we propose an improved prediction model by integrating data pre-processing and ensemble learning to solve these two problems. Specifically, two new features are first integrated into the RFM analysis to better capture customer behaviors. Second, the principal component analysis is adopted to reduce data dimensions. Third, adaptive boosting (AdaBoost) is employed to cascade multiple decision trees to minimize the impacts from the unbalanced data. For clarity, this model is called the PCA-AdaBoost model. We use an e-commerce dataset published on the Kaggle platform to demonstrate its effectiveness by conducting numerical experiments. We compare the performance of the PCA-AdaBoost model developed in this paper with several models proposed in the literature. Our results confirm that the PCA-AdaBoost model can achieve more accurate custom
2022/01/16
http://www.pecs.uema.br/wp-content/uploads/2020/01/aplicacoes_de_algoritmos_de_AM_em_-Beatriz-Nery.pdf
https://mega.co.nz/#!KDwyhAJD!-ACFgwRJIIHuVO1CEkQFJnC1UOIkcWcXXMCQdByzn-c
https://sci-hub.st/
A deep learning based dynamic COD prediction model for urban sewage
Open
Zifei Wang and Yi Man and Yusha Hu and Jigeng Li and Mengna Hong and Peizhe Cui
The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers.
2022/01/16
https://pubs.rsc.org/en/content/articlehtml/2019/ew/c9ew00505f
https://mega.co.nz/#!fPZTxQgT!dYC_1wfRb91mq54a_NmWs_1Mk8YECaYZ3PsoENszST8
https://sci-hub.st/10.1039/c9ew00505f
2019
The real effects of household debt in the short and long run
Open
Marco J. Lombardi and Madhusudan Mohanty and Ilhyock Shim
Household debt levels relative to GDP have risen rapidly in many countries over the past decade. We investigate the relationship between household debt and growth by employing a novel estimation technique proposed by (Chudik et al. in: Hill, Gonzalez-Rivera, Lee (eds) Advances in econometrics volume 36 essays in honour of Aman Ullah, Emerald Publishing, pp 85–135, 2016), which helps to separate short-run from long-run relationships. Using data for 54 economies over 1990‒2016, we show that an increase in household debt is associated with higher GDP growth in the short run, mostly within one year. By contrast, a 1 percentage point increase in the household debt-to-GDP ratio predicts lower GDP growth in the long run by 0.1 percentage point. Moreover, the negative long-run relationship between household indebtedness and GDP growth intensifies as the household debt-to-GDP ratio exceeds 70%, suggesting that policy makers are likely to face non-trivial, real costs in stimulating the economy through credit expansion.
2022/01/16
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2906555
https://mega.co.nz/#!rCwxwa5L!J9ln2ui2opXdDiIQgzqrEvGkCapGGWa-46qxQnpwRcs
https://sci-hub.st/10.1007/s00181-021-02188-z
2022
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Video Feed

Blog & Essays

The quantification of finance should not come as a surprise, as far back as 1962, GPE Clarkson showed how portfolio selection could be automated using discriminator nets - learn more here.

General

Everything from here onward is purely subjective, I will keep adjusting the lists depending on observed changes, and I am happy to take suggestions from others, please reach out.
Competitions
CME Group ★★★★☆ (frequent)
Cornell Portfolio ★★★☆☆ (frequent)
IAQF ★★★☆☆ (infrequent)
Kaggle ★★★★★ (frequent)
Quantiacs ★★★★☆ (frequent)
Numerai ★★★★★ (frequent)
UChicago Trading ★★★★☆ (frequent)
Rotman Trading ★★★★★ (frequent)
Southeastern ★★★☆☆ (frequent)
Traders@MIT ★★★★☆ (frequent)
Algorithmic (Mixed)
Jane Street ★★★★★
PDT Partners ★★★★★
Hudson River Trading ★★★★★
Acquatic Capital ★★★★★
Voleon Group ★★★★★
Five Rings ★★★★★
G-Research ★★★★★
IMC Trading ★★★★★
Renaissance Technologies ★★★★★
Investment Management
ADIA Global Science Lab ★★★★★
Arabesque ★★★★★
Capital Fund Management ★★★★★
DE Shaw ★★★★★
Goldman CoreAI Research ★★★★★
JP Morgan AI Research ★★★★★
Millennium ★★★★★
MAN Group ★★★★★
Two Sigma ★★★★★
Balyasny, Fidelity etc, forthcoming.
Conferences/Seminars
ACM Conference & Fields ★★★★★
QuantMinds & Insights ★★★★☆
London Math Finance ★★★★☆
Quant Finance Weekly ★★★★★
Thalesians ★★★★★
AI & Big Data in Finance ★★★★★
Bloomberg Quant Seminars ★★★★★
AI in Trading ★★★★☆
Make a Recommendation
Make a recommendation inside the website, by opening the above drop down.
Recommendation
Rating
URL
Machine Learning and AI in Finance
Open
Good
https://www.amazon.com/dp/B08SY2PW5J?ref=KC_GS_GB_US
Advances in Financial Machine Learning
Open
Excellent
https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089
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