ºÝºÝߣshows by User: JessStauth / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: JessStauth / Sun, 07 Apr 2019 17:44:27 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: JessStauth Modeling the Stock Market: Common pitfalls and how to avoid them! /slideshow/modeling-the-stock-market-common-pitfalls-and-how-to-avoid-them/139937896 stauthcommonpitfallsstockmarketmodelingspring2019wanimations-190407174427
The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, and under-estimation of real-world costs.]]>
The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, and under-estimation of real-world costs.]]> Sun, 07 Apr 2019 17:44:27 GMT /slideshow/modeling-the-stock-market-common-pitfalls-and-how-to-avoid-them/139937896 JessStauth@slideshare.net(JessStauth) Modeling the Stock Market: Common pitfalls and how to avoid them! JessStauth The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, and under-estimation of real-world costs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stauthcommonpitfallsstockmarketmodelingspring2019wanimations-190407174427-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting &amp; exploding model complexity, non-stationary processes, time-travel illusions, and under-estimation of real-world costs.
Modeling the Stock Market: Common pitfalls and how to avoid them! from Jess Stauth
]]> 209 2 https://cdn.slidesharecdn.com/ss_thumbnails/stauthcommonpitfallsstockmarketmodelingspring2019wanimations-190407174427-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 PyData NYC 2015 /slideshow/pydata-nyc-2015/54952236 pydatanyc2015-151110132252-lva1-app6891
Portfolio and Risk Analytics in Python with pyfolio - On open source library compatible with Zipline and Quantopian. ]]>
Portfolio and Risk Analytics in Python with pyfolio - On open source library compatible with Zipline and Quantopian. ]]> Tue, 10 Nov 2015 13:22:52 GMT /slideshow/pydata-nyc-2015/54952236 JessStauth@slideshare.net(JessStauth) PyData NYC 2015 JessStauth Portfolio and Risk Analytics in Python with pyfolio - On open source library compatible with Zipline and Quantopian. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pydatanyc2015-151110132252-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Portfolio and Risk Analytics in Python with pyfolio - On open source library compatible with Zipline and Quantopian.
PyData NYC 2015 from Jess Stauth
]]> 4163 9 https://cdn.slidesharecdn.com/ss_thumbnails/pydatanyc2015-151110132252-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 DIY Quant Strategies on Quantopian /slideshow/d-30965323/30965323 bayareaalgotradingmeetupslideshare-140207191852-phpapp01
An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth]]>
An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth]]> Fri, 07 Feb 2014 19:18:52 GMT /slideshow/d-30965323/30965323 JessStauth@slideshare.net(JessStauth) DIY Quant Strategies on Quantopian JessStauth An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bayareaalgotradingmeetupslideshare-140207191852-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth
DIY Quant Strategies on Quantopian from Jess Stauth
]]> 49666 12 https://cdn.slidesharecdn.com/ss_thumbnails/bayareaalgotradingmeetupslideshare-140207191852-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-JessStauth-48x48.jpg?cb=1554658962 Quantitative finance professional with expertise in statistical modeling using R, Matlab, and SQL. Experience with computational neuroscience methods, quantitative techniques including advanced statistical analyses, dimensionality reduction with principle and independent components analysis (PCA and ICA), regression analyses, modeling stochastic processes and development of predictive models in financial and neural systems. https://cdn.slidesharecdn.com/ss_thumbnails/stauthcommonpitfallsstockmarketmodelingspring2019wanimations-190407174427-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/modeling-the-stock-market-common-pitfalls-and-how-to-avoid-them/139937896 Modeling the Stock Mar... https://cdn.slidesharecdn.com/ss_thumbnails/pydatanyc2015-151110132252-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/pydata-nyc-2015/54952236 PyData NYC 2015 https://cdn.slidesharecdn.com/ss_thumbnails/bayareaalgotradingmeetupslideshare-140207191852-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/d-30965323/30965323 DIY Quant Strategies o...