Alla Petriaieva's Avatar
Alla Petriaieva
published in Blogs
Feb 24, 2021
What’s the Difference Between Data Analytics and Machine Learning?

What’s the Difference Between Data Analytics and Machine Learning?

Artificial intelligence (AI) technology is developing rapidly. Leaps in applicability and efficacy mean that industries around the world are looking to integrate it into their businesses. With risk lower than ever, and the technology now in place to achieve real, tangible results, adoption is soaring. But it is important to understand the difference between data analytics (coupled with predictive analytics) and machine learning to use the technology effectively.

Data Analytics

Data analytics are ubiquitous at this point – most readily-available products make reams of data available to its users. Marketers, for example, have their pick of Google Analytics, newsletter services like MailChimp, and other dashboard-based systems to ensure data is never in short supply. Data mining can deliver raw numbers, but it does not necessarily provide actionable insights.

Structure is necessary to taking abstract information and extracting commonalities, like averages, ratios, and percentages. Aggregation makes it possible to find patterns and explore variables to deliver impactful, targeted analysis. But most data analysis is descriptive, not predictive – it requires information about something that has already happened to yield insight. It is also human-based, as people propose assumptions, then use data to test their validity.

Predictive analytics collects data which is used to test and predict future outcomes. Humans engage with data to validate patterns, then formulate and test hypotheses based on the assumption that future events will follow the same patterns. Predictions are limited by various constraints on human analysts, like volume of information, time limitations, and cost considerations. Going into detail means increased cost or time investments, which limits the predictive scope.

Machine Learning

Machine learning is predictive analytics on steroids. Humans set the parameters, then employ AI systems to automatically make and test assumptions and learn from the results – all without additional human interaction. Machine learning tests and retests data at speeds that are impossible for humans to replicate, which makes predictive analytics more efficient and, by extension, more cost-effective.

When systems can learn quickly and autonomously, data can be put to work with a level of specificity that humans simply are not capable of. That means stronger insights and a greater level of certainty as to their validity.

Machine learning can dredge up new knowledge from existing data, automate simple tasks that previously required a human to complete, and open up new types of data, like audio, video, and images, to analysis. It allows users to ask specific questions on a massive scale – a boon for businesses of all types.

How the A.I.-Driven Revolution is Happening in Finance

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 generate 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:

  • Finding stock patterns in the market
  • Finding trends in the stock market
  • Testing portfolios to see if they are well-diversified
  • Back-testing statistics to see how different stock patterns generated trading results
  • Making Predictions for price movements in the future, with “A.I. Rank” and level of confidence in the trade.

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.

Related Tickers: GOOGL
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%.