A 50% move is likely

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After falling as much as 35% from late October levels, Bitcoin (BTC) is likely on the cusp of a dramatic + or — 50% move. BTC has price memory around 6,500, and a break below should take the market to 2018 lows around 3,000. A bounce could result in a 10,000 price print.

I built a Random Forest model using data going back to 2010 to predict future prices over the coming 3, 6, 9, and 12 months. Predicted prices are:


Michigan Won’t Turn Blue Easily

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Conventional wisdom and polling are that Democrats can regain the Rust Belt firewall in 2020 (Wisconsin, Michigan, Pennsylvania). I built a Random Forest model called CE-VM to predict likely 2020 total votes (turnout) in every U.S. county. CE-VM predicts that~135M votes will be cast in 2020, up from ~129M in 2016. It also predicts that Michigan’s 2020 turnout will be lower than in 2016. The path to flipping Michigan blue will be much harder than it seems.

Votes, not polls, win elections. CE-VM’s features are multiple population and registration data points in the years before the 2008, 2012, and 2016 presidential elections, respectively. The dependent variable is the number of votes in a given election. …


Expect Stocks to Make New Highs as the Economy Firms Up

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Recession fears are everywhere. The US-China trade war, Middle East violence, impeachment in DC, protests in Hong Kong, and Brexit have all weighed on the economy. These fears are likely overblown. Consumer economic data remains robust and the Fed has cut rates twice as manufacturing has softened. US stocks have posted robust gains. And the 3 month/10 year spread in Treasuries has uninverted.


NLP Analysis of Congress Twitter

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Editorial Cartoon by Bob Englehart, CagleCartoons.com

Conventional wisdom is that Congress doesn’t support the trade war. Sentiment analysis of every member’s Twitter feeds paints a different picture.

TL;DR: Congress is about as negative on China as the President is. And their followers are rewarding negative China tweets with more likes and retweets. With Congress providing tacit support, the public behind them, a stock market near all-time highs, and steady approval ratings expect the President to remain tough on China.

Congress Twitter

Using the Twitter API and Tweepy library, I scraped the most recent 3200 tweets from every member of the 116th U.S. Congress. I selected tweets that contained the words China, Hong Kong, President Xi, Xi Jinping, Beijing, or Chinese. From this subset, I filtered out any tweets containing Trump, Administration, or President, in an imperfect attempt to eliminate tweets directed at the President. I fed the resulting 4,099 tweet dataset through an enhanced version of my previously built China sentiment model. …


Using New Deep Learning Optimizers To Cut Through the Hysteria

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Credit: Joshua G. Lam

The yield curve has inverted, the trade war seems to be in early innings, and economic data is being revised downward. Forecasters are warning of a recession and even financial panic. CNBC has been replete with talk of markets in turmoil. A good part of the country is apoplectic about Trump’s actions on trade.

On the flip side, retail sales, consumer confidence, and employment remain robust. Important voices including former Fed Chairwoman Janet Yellen have offered a calming perspective. The YTD total return on the S&P 500 including dividends is 15% as of 8/23/19. Bond investors are having a great year. …


NLP Analysis of the President’s Tweets on Trade (With Some Inspiration From Russians by Sting)

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In Europe and America, There’s a growing feeling of hysteria.

Markets are catching up to the staying power of the trade war. As I wrote on May 23rd here:

Investors continue to assume that what President Trump says and does on trade will be two different things. Actual events have proven otherwise, and global markets have not yet figured this out. We are on the verge of an upshift in geopolitical tension that will impact all markets.

Source: Complexity Everywhere, May 23, 2019

Conditioned to respond to all the threats, In the rhetorical tweets of the President; Mister Trump says, “We will trade with you”. …


Data Models Suggest the Fed Should Hold

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Source: AZQuotes

TL;DR on Fed Rate Cuts

The Federal Reserve Open Market Committee (FOMC) announces its rate decision on Wednesday. A robust review of economic data does not support a rate cut; I built both a neural network and random forest model trained on 72 economic indicators (monthly data going back to 1992) to predict Fed rate decisions. The models, which find relationships only from the data, suggest the Fed should hold rates. Furthermore, as discussed in my last post, NLP sentiment analysis suggests a mixed view on the part of voting FOMC members.

Yet, we know that Fed Funds futures markets are 100 percent certain of at least a 25 basis point cut. Conclusion? A rate cut would call into question the Fed’s data-driven bias. Some have already implied that the Fed is cherry-picking the data. It would also open the Fed up for more critique of its communication strategy and its willingness to succumb to politics and the bond market. Finally, it furthers the concern that low rates drive inequality. …


Building an NLP Model to Predict if the Fed Cuts Rates

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Source: Nathan Slaughter

Deciphering FedSpeak¹, the words and intentions of the Federal Reserve, has become a full-time job in financial markets and media. And markets have placed their bets leading up to the July 31 Federal Open Market Committee (FOMC) decision. As of today, the widely watched CME FedWatch Tool (which calculates probabilities of rate changes using Federal Funds Futures) has a 100% probability of a Fed rate cut on July 31 (75.5% chance of 25 bps cut; 24.5% of a 50 bps cut).

I was inspired to see if Artificial Intelligence could provide a new way to analyze the volumes of communication coming out of the Fed. So I built a Natural Language Processing (NLP) Fed sentiment model using FOMC statements going back to 1994. The model employs a long-short-term-memory (LSTM) Neural Network architecture and is built using PyTorch and Fast.ai libraries. I plan to continue to collect, label, and feed more data into the model to keep expanding its predictive depth and breadth. …


Digging Deep [Learning] to Win March Madness

I built a neural network to make my NCAA men’s tournament picks this year for my office pool. As of this writing, an Auburn win and Michigan State loss in the Final Four, and I win bragging rights for a year! I was inspired to take this approach because I had watched a grand total of zero college basketball games this year and sadly went to a college that isn’t what you’d call an “athletic powerhouse”.

I decided to do this on March 20th, so had a little more than 24 hours before the 12 noon cutoff on March 21st. This is the story of what I was able to get done in that time. …


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Image Credit: Titima Ongkantong/Shutterstock

My first Fast.ai inspired project is in computer vision, a field of artificial intelligence associated with training computers to understand the visual world via digital videos and images. This project lines up with the first and second sessions of their introductory class, Practical Deep Learning for Coders. Fast.ai’s embraces a top-down learning method which gets you building a complete deep learning model out of the gates. As mentioned in my last post here, there is a lot more theory to come as the course progresses. Finally, if you’d like to skip the post and check out my project in production, you can find it here. …

About

Sameer Ahuja

lead @GCsports, serve youth ⚾️🥎🏀 | 2x founder | immigrant | write blog analyzing our world using AI & complexity science - views my own | family guy

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