Bloomberg

Algorithmic stock trading—aka “algo-trading”—uses machine learning algorithms to make stock market trades faster than a human could, calculating the best time, price, and amount to trade in an instant.

Companies post content to their websites, newsletters, and social media accounts based on social media metrics that indicate when posts receive the most engagement. This engagement varies by social media site, industry, content type (promotion, article, etc.), and the time of posting: post engagement varies by weekday and even time of day. A content calendar, then, is the scheduling of content publication to increase engagement and conversion.

Companies also schedule site maintenance on these calendars, to ensure they only occur during times of least engagement.

Other terms for content calendar include editorial calendar, social media posting schedule, and other variations of these. An editorial calendar, however, focuses on content planned for company-managed websites plus social media accounts. Social media content schedules, on the other hand, focus on social media posts, as the name indicates.

Hedge fund investments use several different strategies to achieve returns in both domestic and international markets. They are often aggressively managed and trade in land, real estate, stocks, derivatives, currencies, etc. markets.

The most lucrative hedge funds also have a diverse portfolio—which means investors need to analyze large amounts of data to make investment decisions.

Organizations must satisfy the unique demands of their customers. The good old days of mass marketing and predictions on limited mixture of products in one location are gone. Conversely, the use of artificial intelligence in inventory generates huge dividends to companies willing to reshape their supply chain orders.

This technological development results from the availability of massive amounts of real-time data now routinely generated by enterprise software systems and smart products. By collecting this data, organizing it, and interpreting it, artificial intelligence and machine learning have introduced an entirely new level of data processing leading to deeper business insights.

Portfolio management is the management of investments to meet long-term financial objectives.

Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward.
Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward. Portfolio management models and use cases include any collection of investment instruments like shares, mutual funds, bonds, FDs and other cash equivalents, etc.