We’ve all been told to “work smarter, not harder.” But what does working smarter really mean? In the world of finance and trading, working smarter means working differently. None of us can compete against computers stacked inches away from the stock exchange or blue chip companies with multi-million dollar marketing campaigns. The key to winning is to go where the big guys haven’t and the way to do that is through diverse datasets. In this talk, you will discover the theory and tools to discover new datasets from unexpected sources in order to gain an upper-hand in both finance and business. So whether you’re a quant that trades in his bedroom or a restaurateur looking to grow his business, you’ll learn how the diversity of data can be the sharpest knife if your set.
3. OUR AGENDA
• My Story With Quantitative Investing
• The Problem Of Small Fish Big Pond
• The Solution: Big Fish Small Pond
• How This Applies To Quantitative Finance
• How You Can Do This Too
• Conclusion
4. MY STORY
• I work with algorithms, clients, and ideas
• My job is to combine all three into one finished product
• Everyone has different ideas but wants the same thing
• Alpha but everyone uses the same data
• Wall Street Analyst Estimates – PEAD
• Build something that beats the market?
5. THE PROBLEM
• Too many people compete in the same arena
• Small fish in a big pond
• Finance: Wall Street
• Business: Samsung, Apple
6. THE SOLUTION
• Where no one is, is the best place for you to succeed
• Wall Street: Don’t go to MS, go to the boutique firm next
door
• Business: A smartphone for 3rd world toxin detection in
water systems
• AKA new data sources
7. REAL LIFE EXAMPLE
• EVERYONE uses wall street analyst earnings estimates
• Wall Street vs. Crowd (Estimize)
• Estimize crowd predictions are 67% more accurate
• Algorithm perform 2x better on Crowd vs. Wall Street
10. AVOIDING THE TWITTER LEAK
• April 28, 2015 Twitter earnings were leaked
• 20% drop in market cap
• $5 billion drop in market cap
• Accern (News Sentiment) analyzed +100,000 articles
• 1 day before leak, Accern shorted Twitter
• SunTrust downgraded Twitter -> Strong Negative
sentiment
12. HOW YOU CAN DO THIS
TOO
• Kimono Labs
• API for scraping websites
• Quandl
• Thousands and thousands of datasets
• Social Media
• CO Everywhere, Ground Signal
• Location based social media information
And from working with all of these different clients, I’ve realized that everyone has different ideas.
Most of these different ideas are generally small tweaks on existing methods
The question is not a yes/no answer, but what will give me the highest probability of beating JP Morgan? Morgan Stanley? Renaissance
Establish the PEAD first
An extremely well documented phenomon that happens on the earnings announcement date
So a companuy like apple will announe earnings about four times a year
When I first started my job at Quantopian, Jess Stauth, our VP of Quant Strategy, came to me with the challenge of writing a PEAD algorithm
Too many people are competing in the same arena
Take Wall Street -> Boston College graduate competing against Harvard Graduate
Take Business -> HTC or Nokia competing against AAPL and Samsung
Finally, something that actually relevant -> let’s look at a bar
If you’re a small business, you need to compete in a different arena
The world of quantitative finance, there are two different ways to compete in this arena: mathematics, data
Does it seem weird that we have the solution in different slides -> can we make them all congruent? E.g. put this slide towards the end where wall street, business, and life are afterwards?
Explain WHY the crowd is unconventional
Sticking point – Need to talk about the algorithm itself briefly?
Looking at an earnings surprise, buy/sell and hold position for 5 days.
This should be extremely specific for different types of life situations
