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market sentiment

Data Science in Crypto


Under the hood of every cryptocurrency protocol, we will always find that Blockchain Technology is the engine that allows it to keep running. If we trace back the technologies that made its application possible, we will find that the science behind it has been around for decades, and only just recently became ubiquitous. Gradual changes over the last couple of decades contributed to the recent uptake of cryptocurrencies. More and more companies are now able to collect increasingly larger amounts of data. All that data was just lying there, like gasoline waiting for the spark that would transform it into valuable, usable information with real-world applications.

How to Use NLP to Take on Wall Street


In January, 2021, retail investors - Robinhood army - came together on Reddit's Wall Street Bets group and other social media outlets to take down prominent hedge funds by causing a short squeeze and pushing up GameStop's stock price by 400% in just one week¹. This amount of volatility is not normal, the retail investors were urged on by the Reddit group to punish hedge funds that had taken an outsized short bet against GameStop. Tracking market sentiment can be a powerful tool for investors because understanding the mood of where the market is going can allow one to capitalize from the changing direction. Combining market sentiment with market fundamental will result in more sound investments. I was fascinated by the showdown between Wall Street and Reddit and inspired to understand how machine learning (ML) can be used to track market sentiment.

A Complete Guide To Sentiment Analysis And Its Applications


Sentiment analysis is a technique through which you can analyze a piece of text to determine the sentiment behind it. It combines machine learning and natural language processing (NLP) to achieve this. Using basic Sentiment analysis, a program can understand if the sentiment behind a piece of text is positive, negative, or neutral. It is a powerful technique in Artificial intelligence that has important business applications. For example, you can use Sentiment analysis to analyze customer feedback.

Foreseeing Armageddon: Could AI have predicted the Financial Crisis?


The Global Financial Crisis (hereafter, GFC) of 2007–2008 had far reaching financial and legal consequences, affecting millions of livelihoods. Sparked by the proliferation of subprime mortgages and exemplified by fall of Lehman Brothers, it's aftershocks were widely felt around the world. Following a period of recovery and growth, the world plunged into the European Sovereign Debt Crisis (hereafter, ESDC) beginning in late 2009, the effects of which have been argued to be still ongoing today. As global markets tend to operate in a cyclical fashion, scenario planning for the next financial crisis is not a matter of if, but when. Indeed, a Google search would lead to hundreds of differing opinions on the matter, from conclusions based on quantitative metrics to others based on the prophecies of Nostrodamus.