- Stock Market Forecasting Using Time Series Analysis - Jan 9, 2020.
Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.
- Top KDnuggets tweets, Aug 14-20: Researcher reproduced 130 research papers on “predicting the stock market”, coded them from scratch. - Aug 21, 2019.
Also: For data pros only - An SQL Query walks into a bar and sees two tables; Deep Learning for NLP: Creating a Chatbot with Keras!; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Wanting to be even more marketable as a data scientist? Check out these trends in the skills employers are looking for today
- Optimization with Python: How to make the most amount of money with the least amount of risk? - Jun 26, 2019.
Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.
- Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices - Nov 21, 2018.
LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.
- How StockTwits Applies Social and Sentiment Data Science - Mar 9, 2018.
StockTwits is a social network for investors and traders, giving them a platform to share assertions and perceptions, analyses and predictions.
- TensorFlow for Short-Term Stocks Prediction - Dec 12, 2017.
In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.
- Data Scientist: The Hottest Job on Wall Street - Nov 8, 2017.
The demand for professionals that can build financial analytics programs is booming. We foresee two main objectives- to predict market movement for profit, and to protect customer assets of banks.
- More than the Hype: Beyond Gartner’s Hype Cycle - Nov 3, 2017.
Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.
- Top 10 Videos on Machine Learning in Finance - Sep 29, 2017.
Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!
- Sentiment Analysis & Predictive Analytics for trading. Avoid this systematic mistake - Jan 25, 2016.
The financial market is the ultimate testbed for predictive theories. With this post we want to highlight the common mistakes, observed in the world of predictive analytics, when computer scientists venture into the field of financial trading and quantitative finance.
- Opentext Data Driven Digest, Sep 18: Money and Finance - Sep 23, 2015.
Pacific Stock Exchange in San Francisco was created 133 years ago this week to serve businesses that struck rich mining for gold during the California Gold Rush. Nowadays, businesses mine for data hoping to strike it rich, using visualizations like the ones below.
- A 3D Analytic Framework for Visual Data Discovery - Nov 19, 2014.
A presented 3D Visual Analytic Framework enables Visual Data Discovery, and allows all discovery actions to performed inside 3D Unified Visual Data Representation Space.
- Interview: Piero Ferrante, BCBS on Why Healthcare is Rich in Data but Poor in Information - Jul 17, 2014.
We discuss role of analytics in healthcare payer firms, major challenges in leveraging healthcare data, shift to value-based payments, personal motivation towards analytics, career advice and more.
- Large Investment Firm: Data Scientist / Statistician / Programmer - May 9, 2014.
Use both analytical and creative approaches to solve unstructured questions related to quantifying the risks and drivers of growth of publicly traded companies.