Prediction markets (also known as betting markets, information markets, decision markets, idea futures or event derivatives) are open markets where specific outcomes can be predicted using financial incentives. Essentially, they are exchange-traded markets created for the purpose of trading the outcome of events. The market prices can indicate what the crowd thinks the probability of the event is. A prediction market contract trades between 0 and 100%. The most common form of a prediction market is a binary option market, which will expire at the price of 0 or 100%. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief. (Wikipedia).
Time Series Forecasting on Stock Prices
Watch this talk to learn how to set up a process for stock price forecasting using Python and Machine Learning. PUBLICATION PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=72g4V6Ucnlc
From playlist Python
Stock Market Predictions : Python for Finance 10
In previous videos we made a wonderful investment portfolio and now we will use regression analysis to make stock market predictions about the future performance of our portfolio. I’ll be using the ARIMA model for making stock market predictions in this video. It focuses on trying to fit
From playlist Python for Finance
Acetock - Stock Prediction Tool for Amateur Investors | Workshop Capstone
Visit https://ai.science for more content like this, and to see the upcoming workshops! Investing in the stock market have many advantages such as potential for high return, staying ahead of inflation, high liquidity, no limitation of investment, and flexibility of portfolio. However, man
From playlist Community Projects
The Stock Market - Can we predict it?
Yes but no. Let's use a regression approach to try to predict the markets. We also go through a better evaluation function that can see through the apparent "amazing performance" of our model CODE: https://github.com/ajhalthor/stock-price-prediction Daily Google Trend Code used in video:
From playlist Time Series Forecasting
What Can't We Predict With Math?
Solar eclipses are fairly predictable, but the behavior of the stock market over the next couple days...not so much. But why? Is any given problem simply a matter of having a big enough computer and a complex enough algorithm to solve it, or are there certain things that lie beyond the rea
From playlist Technology
An easy to understand and easy to use method that I use to pick stocks! Link to the code : https://github.com/ritvikmath/Time-Series-Analysis/blob/master/Investing.ipynb Link to file containing ticker data : https://github.com/ritvikmath/Time-Series-Analysis/blob/master/series_tickers.p
From playlist Stock Trading Principles
What are index options? What are currency options?
In todays video we will learn about options on foreign exchange and index options. These classes are all based on the book Trading and Pricing Financial Derivatives, available on Amazon at this link. https://amzn.to/2WIoAL0 Check out our website http://www.onfinance.org/ Follow Patrick
From playlist Class 5 - Options Wrap Up
Make Money Betting on Politics - Arbitrage with Predictit
Step-by-step tutorial to find and profit from arbitrage opportunities on Predictit. Predictit is market with unique attributes that makes it the perfect place for arbitrage: it is closed to big players like banks and hedge funds, and it lets you bet on political outcomes. - PredictIt arb
From playlist Finance, Probability, and Other Stuff
Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
Learn about the four basic steps to make predictions, about making predictions using more than one piece of data (feature), and about how businesses around the world can make money from AI by predicting sales (time series forecasting). This and more in the second video in this visual intro
From playlist Intro to AI
Becoming a Utopian Data-Driven Enterprise
Originally aired October 1, 2014. The "data-driven organization" is the utopian enterprise: Teams evaluate the output of their activities and managers guarantee a precise outlay of expenses to meet and exceed business goals. What will it take to build this ideal scenario? In this webcast
From playlist O'Reilly Webcasts 3
Stanford Seminar - How to Make Better Forecasts
"How to make better forecasts" - Lyle Ungar of University of Pennsylvania About the talk: We report the results of a four year experiment in which over twenty thousand volunteers made predictions about hundreds of real world geopolitical events in dozens of carefully controlled experiment
From playlist Engineering
Real-world Applications of ML in Investing - Deep Random Talks - Episode 7
Notes and resources: https://ai.science/l/eacc6e3f-f1e5-4d46-b63c-bb0f45ce8749@/assets -Join our ML slack community: https://join.slack.com/t/aisc-to/shared_invite/zt-f5zq5l35-PSIJTFk4v60FML177PgsPg -Visit our website: https://ai.science -Book a 20-min AMA with Amir: https://calendly.
From playlist Deep Random Talks - Season 1
IMT4889 - Decentralised Finance (2/2)
IMT4889 - Introduction to Decentralised Technology Decentralised Finance (2/2)
From playlist 2021 - IMT4889 - Decentralisation
Web 2.0 Summit 08: Launch Pad Fourth Edition: Web Meets World
Judging Panel: John Battelle (Federated Media Publishing), Vinod Khosla (Khosla Ventures), Chris Albinson (Panorama Capital), Michael L. Goguen (Sequoia Capital), Todor Tashev (Omidyar Network), Patrick Chung (New Enterprise Associates), Erik Straser (MDV-Mohr Davidow Ventures) Launch Pad
From playlist Web 2.0 Summit 2008
ML Tutorial: Adversarial and Competitive Methods in Machine Learning (Amos Storkey)
Machine Learning Tutorial at Imperial College London: Adversarial and Competitive Methods in Machine Learning Amos Storkey (University of Edinburgh) October 28, 2015
From playlist Machine Learning Tutorials
What's Going on in Behavioral Finance? A Survey of the Latest Ideas - N. Barberis - 1/31/2020
"What's Going on in Behavioral Finance? A Survey of the Latest Ideas" Nicholas C. Barberis, Stephen and Camille Schramm Professor of Finance, Yale School of Management Abstract: The field of behavioral finance tries to make sense of financial data using models that make psychologically ac
From playlist HSS Caltech + Finance 2020
Is Finance Ready For Machine Learning? (Vivek Viswanathan) - KNN Ep. 54
Vivek Viswanathan is the portfolio manager of the Rayliant Quantamental China Equity ETF and is the Global Head of Research and Portfolio Management at Rayliant Global Advisors. He has a Ph.D. in Finance from UCI, a Master’s in Financial Engineering from UCLA, and a Bachelor’s in Economics
From playlist Ken's Nearest Neighbors Podcast
Webinar: If I build it, will they come? Understanding Product-Market Fit
Learn more at: https://stanford.io/370yNcZ So your company has a product idea. How do you know if this product is worth building? Will there be a demand for it? Enter: product-market fit. Put simply, product-market fit means that there are enough people out there who will buy what your c
From playlist Leadership & Management