Alla Petriaieva's Avatar
Alla Petriaieva
published in Blogs
Mar 01, 2021
What’s the Difference between Big Data Artificial Intelligence

What’s the Difference between Big Data Artificial Intelligence

In the battle of buzzwords, it would be hard to defeat Big Data and Artificial Intelligence. Each occupies significant space in modern print, thought, and conversation, and while big data and AI are different from each other in strict definition, they remain complementary – symbiotic, even.

More formally defined by renowned research and advisory company Gartner as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation,” big data refers to the idea that almost everything we do – the purchases we make, the internet searches we type, the art we enjoy, the security footage from a convenience store, a photograph – is data that can be collected, examined, and (theoretically) monetized by those who can extract the right insights.

Volume, velocity, and variety – sometimes characterized as the three V’s – are the parameters that define how big data is processed. Looking at big data means examining “high volumes of low-density, unstructured data”, says Oracle in a primer on their website. It requires the capability to receive and react to information at increased velocity, “[operating] in real time or near real time.” It also means having the ability to deal with a wide variety of data, structured and unstructured, that “[requires] additional preprocessing to derive meaning and support metadata.”

Artificial intelligence is an amalgam of different fields (like computer science, logic, neuroscience, psychology, and more) with the goal of creating automated systems that can perform tasks previously requiring human intelligence. AI does so by analyzing vast amounts of data to recognize patterns, in turn using that data to make quick, efficient decisions. It can even “learn” (another buzzword: machine learning) over time through that analysis, retaining insights from information it has examined and using them to glean new ones.

Advances in technology have eased the process of both amassing data and learning from it. AI can now perform natural language processing, which involves analyzing and learning the meaning of human speech; speech and text recognition are advancing in leaps and bounds. It is used in robots in many homes (think Siri or Alexa) and in the systems governing self-driving cars – what feels like the tip of the iceberg.

This ability to pore through, and derive insight from, massive quantities of information means AI is a natural fit for working with big data. AI is typically programmed to perform a specific task –identifying discrepancies in MRIs, for example – at a speed far greater than any human. Once big data is collected and structured, AI reacts to and makes sense of it. The more information available to analyze, the better an AI system works. Big data and artificial intelligence need each other to exist.

 

If You’re Wondering When A.I. Will Start Making Market Predictions…

Guess what – it already is. Hedge funds and large institutional investors have been using Artificial Intelligence to analyze large data sets for investment opportunities, and they have also unleashed A.I. on charts to discover patterns and trends. Not only can the A.I. scan thousands of individual securities and cryptocurrencies for patterns and trends, and it generates trade ideas based on what it finds. Hedge funds have had a leg-up on the retail investor for some time now.

Not anymore. Tickeron has launched a new investment platform, and it is designed to give retail investors access to sophisticated AI for a multitude of functions:

And much more. No longer is AI just confined to the biggest hedge funds in the world. It can now be accessed by everyday investors. Learn how on Tickeron.com.

Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Mar 07, 2021
4 Tricks Hedge Funds Use to Get Ahead

4 Tricks Hedge Funds Use to Get Ahead

If the stock market were Major League Baseball, hedge funds and institutional investors would be the pros on championship teams while everyday self-directed investors (SDIs) are the benchwarmers in the minors.It’s how they get ahead, and it’s why 90% of SDIs lose money trying to play (invest and trade) in the major leagues. The 4 tricks we discuss below are rooted in one common theme: they all use Artificial Intelligence and algorithms to generate data and ideas.
John Jacques's Avatar
John Jacques
published in Blogs
Mar 22, 2018
A.I. Stock Market Predictions: Head & Shoulders

A.I. Stock Market Predictions: Head & Shoulders

Statistics for the Head-and-Shoulders Bottom Pattern The days where only hedge funds used algorithms to trade stocks are officially over. Now retail investors can use Artificial Intelligence (A.I.  Here’s an example of the algorithm in action: Late last year, Tickeron’s A.I.
Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Jul 10, 2020
3 Stocks to Buy if Coronavirus Second Wave Hits

3 Stocks to Buy if Coronavirus Second Wave Hits

By analyzing market trends from the first wave, you can predict behavior for the second. Technology stocks have performed at historic levels this year, but the market is severely overbought.To compensate for that, look at performance during Q1 and Q2, the height of global Covid shutdowns.
Edward Flores's Avatar
Edward Flores
published in Blogs
Feb 06, 2021
How to Become the Millionaire Next Door

How to Become the Millionaire Next Door

The Golden Gate Bridge is always a fixture of these walks too, one of man's most beautiful creations.  As we were walking, at one point she turned to me and said, "Man, I'll never have a million dollars."" My girlfriend is 27 years old and works as a graphic designer, making about $75,000 a year.
Alla Petriaieva's Avatar
Alla Petriaieva
published in Blogs
Feb 23, 2021
Is Ethereum’s Bomb about to Explode?

Is Ethereum’s Bomb about to Explode?

Ethereum’s software is set for an update in October.Until it is finished, participants in the Ethereum blockchain must determine how to delay the difficulty bomb – code that necessitates a steadily increasing amount of computer power to mine blocks and unlock rewards – that is already in place.
Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Aug 07, 2018
When Is the Next Recession Coming?

When Is the Next Recession Coming?

However, we also know that economists predicted 22 recessions out of 11 that took place since 1945. Are there real recession signs we should watch for?Indeed, the answer is yes, and here are a few very important ones: The first one is almost obvious and known to everyone – it is the Fed.
Abhoy Sarkar's Avatar
Abhoy Sarkar
published in Blogs
May 22, 2020
Central banks have been buying $2.4 billion in assets every hour for the past two months

Central banks have been buying $2.4 billion in assets every hour for the past two months

Some $17.8 billion has been poured into  bond markets over the past week, the biggest move in more than three months.Around $3.5 billion has been invested into gold, the second largest on record. 
Rick Pendergraft's Avatar
Rick Pendergraft
published in Blogs
Feb 07, 2021
Mid-January Short Interest Report Shows 8 Stocks with Good Fundamentals and High Short Interest
Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Mar 10, 2021
How to Start Trading Penny Stocks

How to Start Trading Penny Stocks

Penny stocks have long been marginalized within the professional investment community, oftentimes being painted with a broad brush of simply being “too risky.” Leonardo DiCaprio’s depiction of the penny stock peddling conman, Jordan Belfort, in the Wolf of Wall Street certainly didn’t help.Here are four reasons to start trading them now. Reason #1: Let’s State the Obvious -- Penny Stocks are Cheap A single share of Apple Inc. costs over $350.
Abhoy Sarkar's Avatar
Abhoy Sarkar
published in Blogs
May 08, 2020
US unemployment rate jumps to 14.7%, the highest in series history

US unemployment rate jumps to 14.7%, the highest in series history

The U.S. economy’s employment fell by -20.5 million in April. The coronavirus crisis led to unemployment rate soaring to 14.7% in the U.S, the highest rate in the Bureau of Labor Statistics-tracked series history that goes back to 1948. However, the figures were better compared to several economists'/analysts' forecasts of 22 million job losses and 16% unemployment rate.  Another unemployment measure that includes those who have stopped looking for work as well as those holding part-time jobs for economic reasons also touched an all-time high of 22.8%.