DIS Stock Forecast: Raise Your Bets On Disney

Going forward, the Marvel and Star Wars assets remain infinite gold mines. Ownership of 21st Century Fox can add billions in Disney’s annual revenue. Disney now also effectively owns a controlling stake in Hulu. Video streaming service Hulu is now valued at $15 billion. Hulu’s 25 million subscribers makes it a potent weapon against Netflix. Marvel, Disney, Star Wars, and Fox entertainment content will help Disney Plus and Hulu disrupt Netflix’s dominant position in paid streaming.



Hit movies are important because they boost the intellectual property licensing business of Disney. Disney is the top licensor in the world, with $56.6 billion worth of licensed retail goods. Marvel or Disney movies is not just about ticket sales. The bigger profits are from licensing Marvel and other Disney-owned characters and assets. IP licensing was worth $180 billion five years ago. Today, Disney still remains the no.1 player in the $200 billion/year global IP licensing business.

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The Next Frontier Of Artificial Intelligence Predictability In Financial Markets

The question of whether or not financial markets are predictable has been a hotly debated topic, with some supporting an "Efficient Market Hypothesis" which essentially means that all asset prices reflect available information. Under this theory, it is not possible to consistently outperform the market without illegal means such as insider trading. Over the course of the history of the stock market, this theory seemed to have some legitimacy. Very few were able to consistently achieve outsized returns compared to benchmark indexes such as the S&P 500.



The introduction of computing and Artificial Intelligence in the financial world seems to have disproved the Efficient Market Hypothesis. Our algorithm is based on the on Artificial Intelligence, but what sets it apart from the top Hedge Funds and other groups is the algorithmic structure engineered by Dr. Roitman. His application of using chemical modeling to predict the stock market is unique and not being done anywhere else. Another aspect of our system that sets us apart from others is that it is able to find correlations and relationships across asset classes and across world markets. We currently predict over 10,000 assets in 50 global exchanges, and we are diversified across many sectors and stock universes.

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Machine Learning Trading, The Stock Market, and Chaos

Have you ever wondered how the I Know First Algorithm is so accurate when there's so many seemingly random and chaotic events happening all the time in the stock market? Well, the first thing to understand is there's a key difference between random and chaotic events. There is a notable difference between chaos and randomness making chaotic systems predictable, while random ones are not. Real world processes may seem random to the untrained eye, but upon closer examination, we see that such processes are in fact chaotic. Natural processes such as seismic events, population growth, and stock markets are all examples of such systems and can be predicted with reasonable accuracy. Chaotic processes are controlled by three competing paradigms: Stability, Memory, and Sudden and Drastic Change.



Stability is seen in the stock market as a stock trend either increases or decreases. While the share price of the stock changes over the given time period, the trend is unchanging. There is also a degree of instability here because of what is called a “tired trend.” As a stock is rising and continues to rise, there comes a point when investors start to question how long the trend can continue as it has. As people begin to lose confidence in the trend the stability decreases. Memory is the influence that past events have on a current trend. A stock that has been known to rise will likely continue to do so. Drastic and unforeseen changes can also occur, completely reversing a trend with little or no warning. Black Swan events, as they are referred to, are themselves unpredictable but are useful in making future predictions. The cycles of rising and falling trends that occur in chaotic processes have varying time periods, quiet periods can be followed by a large jump or vice versa. Together, these properties of chaotic processes make it possible to make predictions about the system using probability.

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EBAY Stock Forecast: Why eBay Remains Relevant

The long-term growth and prosperity of eBay is insured. The company has a powerful global brand that has a loyal army of 180 million active buyers. My takeaway is that EBAY deserves a higher valuation when you consider that it has this massive 180 million-strong customer base. This huge number of active buyers makes eBay a long-term beneficiary of the growing $3.45 trillion global retail e-commerce industry.



Yes, eBay’s American operations is declining. However, the international business of eBay is growing fast enough to offset the decline in the U.S. This year’s return to India is great for eBay’s international expansion. India has more than 1 billion citizens. They could help eBay increase its yearly international sales by 5% to 10%.

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The Future of Risk Management: Quantifying Uncertainty

Chaotic systems such as the stock market have risk and uncertainty inherently associated with them. Even though we can predict these systems, because of the massive amounts of random events that can potentially occur, it is almost impossible to be 100% accurate. However, it is possible to quantify uncertainty and use these numerical values to minimize risk. There are many different sources of uncertainty that can affect a model such as parameter uncertainty and variability as well as structural uncertainty.



There are many different ways to quantify uncertainty and minimize risk. Sensitivity analysis is the process of recalculating different outcomes based on changes to inputs in order to determine the total change and can be used to determine the outcome of a set of inputs. Another way to simulate the most likely outcome is to use probability weighted averages. If one knows the likelihood of specific situations occurring, an algorithm can run all of these various scenarios and then weigh them based of the probability of that specific event occurring, in a process akin to Bayesian updating. Another way to obtain the best possible output and further improve the results from the Monte Carlo method is to use temporal difference learning. This algorithm uses bootstrapping and adapts to create an ideal final outcome instead of a myriad of potential outcomes.

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