Knowledge Graph applied to Twitter Sentiment Estimation - paper
Knowledge Graphs supports the analysis of sentiment polarities, based on similarity measurements between the text’s graph and pre-determined polarity graphs.
https://t.co/I74UklnlgV or https://t.co/wf97aRymYK https://t.co/3z42zkiLPo
Micro-prices as better estimator of price dynamics in a limit order book #lob #obi and order book imbalance framework
Simple example code for "applying Monte Carlo simulation to price both a European Call and Put Option [in Python], following the Black-Scholes Market Model using Risk-Neutral Pricing." https://t.co/LytZItFS4p https://t.co/ZGsIbM5Vnt
Paper applys signature transformations to model the underlying shape of the input equity returns; further assuming the underlying shape remains the same, predicting future values based on that shape.
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
Join SESAMm on a deep dive into their financial intelligence platform TextReveal Streams, which is used by quantitative and fundamental asset managers to optimise trade timing and identify new investment opportunities. https://t.co/HtrtdviEaQ
"A Machine Learning Framework for Asset Pricing": "Building on [mathematical] representations of asset prices…we develop a solution strategy using neural networks and further machine learning techniques." https://t.co/PRi9bVza7k https://t.co/1JAajXFkm0
Python decorator patterns
This post shows you toy implementations of Python decorator patterns such as @measure, @repeat, @trace, @count, @singleton, and @app.route (made famous by Flask).