5/5 The titans Greenwood and Shleifer draft a paper on this in 2012. As usual, their writing and analysis are a level above the others (sorry Steve). Perhaps that's why they get 10x as many cites as the previous paper. Or perhaps it's just the Matthew effect. https://t.co/GBlwcgP9vB
4/5 You can imagine that such heresy will be hard to publish. Well, to be honest it's hard for me to imagine how hard it must have been. Amromin and Sharpe 2005 was not published until 2013. https://t.co/NO9IcsmaAv
3/5 But Amromin and Sharpe 2005 show that if you actually ask people (instead of solve for a stochastic-general-equilibrium), they'll say the opposite. Bad economic times mean low future returns. Realizing this requires being smarter than the market is too hard for most people. https://t.co/oHrfu2hbbg
One fav papers, of the few that changed my views, is Amromin and Sharpe 2005. They show that households have exactly the -opposite- view on expected returns and recessions that I was taught in my Ph.D. This paper is also the saddest example of the Matthew effect I know of. https://t.co/at3CQ3YCWH
In this featured article, Benjamin Smith from bensstats shows us how to fix R's "messy string concatenation" with a special function:
#DataScience #rstats https://t.co/XTcoKKeGnD
In this @QuantInsti tutorial, Anshul Tayal offers step-by-step instructions on getting started with Julia Programming.
Learn how to perform basic arithmetic operations and work with data structures: https://t.co/eGchy9HXwO
#DataScience #DataAnalytics #DataVisualization https://t.co/YTI9lBqFJk
In about 10 days the first quarter of the M6 competition is ending with the winners being awarded $42,000 in prize money. It is interesting the top four teams in the Global score are two experienced data scientists and two students, It seems that the pattern of the M5 competition is followed with a student winning the accuracy challenge and a team of two data scientists winning the uncertainty one, May be the time has come that students can master ML methods equally well than experienced data scientists! Maybe AI is bringing some big changes in the value of experience and expertise in all aspects of our work and lives.
*** Market Microstructure - Quantitative Research ***I am happy to share my last paper on market impact which completes the previous ones that I published on equity and options market during the last years (links in comments). This third paper completes the two previous ones and closes this market impact trilogy I started 6 years ago. In this paper, we propose a theory of the market impact of metaorders based on a coarse-grained approach where the microscopic details of supply and demand is replaced by a single parameter ρ ∈ [0, +∞] shaping the supply-demand equilibrium and the market impact process during the execution of the metaorder. Our model provides an unified explanation of most of the empirical observations that have been reported and establishes a strong connection between the excess volatility puzzle and the order-driven view of the markets through the square-root law.I would like to thank Marcos Lopez de Prado and Prof. Alexander Lipton for their comments on the preliminary version of this paper. I am also particularly grateful to Charles-Albert Lehalle for his careful reading, comments and the many interesting discussions we had about it.
Plenty of research shows that #insider buys contain value-relevant information while insider sales include little to no information. But what about the action of “not trading”?Are the trades of portfolio insiders informative about the stocks they choose not to trade, those they choose not to buy, and those they choose not to sell?To finish reading, article by Elisabetta B. is in comments field.
Interested in draw-downs? Stan Uryasev and Rui D. will be presenting their research on draw-down based risk measures and portfolio optimization with respect to them in the next to last IAQF/Thalesians webinar of the semester. Monday, May 23rd at 12:30 NY time.#research #drawdown #riskmanagement #riskanalytics #quantitativefinance #webinar #portfoliooptimization #optimization
Explainable and interpretable models in machine learning are not easily distinguished. Interpretable models are convenient because they show the exact contribution of each feature to the final result.With non-interpretable models, you only get an estimate of the contribution of each feature to the final outcome. Explainable models then seem to bridge the difference between these models by using explanatory methods, some of which are listed below.We can say that non-interpretable models are blackbox models, interpretable models are whitebox models, and non-interpretable models + explainability techniques are greybox models. I have been thinking about how to represent this idea. notion page: https://lnkd.in/gjJ2-Mg6 #quantitativeresearch #algotrading #explainability
I solved my biggest crypto trading problem.I didn't have any time and was always stressed.This is how I did it:I built an algorithm that learns to trade by itself.It starts by developing two different, random trading strategies.And then compares how they perform on a set of data.The best strategy is then selected and a new strategy is created, and they are backtested again.The algorithm does this thousands of times over, until the portfolio's requirements are met.This strategy has done 3 things for me:1. Reduced my portfolio risk2. Increased my returns3. Reduced how much time I spend trading#crypto #bitcoin #ethereum #blockchain #trading
Our white paper on default modeling is on https://lnkd.in/eUMAPYHU. Nick Costanzino, Albert Cohen and I found that looking at stopping times in terms of their conditional survival curves yields a general framework for default modeling that encompasses both structural and reduced form models. Moreover, whether the stopping time is predictable or totally inaccessible can be directly read off of the survival curves.#default #creditrisk #stoppingtime #predictable #inaccessible #whitepaper
ML-Quant now has 6 additional web scrapers, for a total of 30 scrapers working 24/7 to 'find' trending content in the quant finance and machine learning space. Today, I added a script to find the top general ML papers as per their social media metrics (twitter, news, YouTube etc.) https://www.ml-quant.com/
Ankur Bapna, Isaac Caswell, Julia Kreutzer, Orhan Firat, Daan van Esch, Aditya Siddhant, Mengmeng Niu, Pallavi Baljekar, Xavier Garcia, Wolfgang Macherey, Theresa Breiner, Vera Axelrod, Jason Riesa, Yuan Cao, Mia Xu Chen, Klaus Macherey, Maxim Krikun, Pidong Wang, Alexander Gutkin, Apurva Shah, Yanping Huang, Zhifeng Chen, Yonghui Wu, Macduff Hughes