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Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin

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In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.


State Super moves to add machine learning tools

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Australia's State Super has hired Neuberger Berman LLC for an equity mandate and to help the fund accelerate development of data science and machine learning tools that can complement its more traditional investment capabilities. The move reflects continued concerns that conventional approaches to managing the Sydney-based fund's A$44 billion ($30.2 billion) portfolio may not meet the moment in unconventional times. "My biggest concern is what happens if the market is behaving in an abnormal way," outside of the industry's knowledge base and modeling conventions, said Charles Wu, State Super's deputy chief investment officer and general manager, defined contribution investments, in an interview. In that regard, Mr. Wu said the emergence of negative sovereign bond yields three years ago was a warning bell. In the current environment, "a different way of thinking is required" and machine learning -- with its potential to come to a problem without prejudices or preconceptions -- can help State Super's team navigate a world growing evermore different from the one everyone has been trained to think about, he said.


Traditional vs Deep Learning Algorithms used in BlockChain in Retail Industry

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This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. The potential of blockchain to solve the retail supply chain manifests in three areas. Provenance: Both the retailer and the customer can track the entire product life cycle along the supply chain. Smart contracts: Transactions among disparate partners that are prone to lag can be automated for more efficiency. IoT backbone: Supports low powered mesh networks for IoT devices reducing the needs for a central server and enhancing the reliability of sensor data.


Helping Democratize AI, A Call to Action

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Companies across the globe are increasingly adopting Artificial Intelligence (AI). Meanwhile, forecasts for its global market value are skyrocketing. AI has the potential to save businesses substantial costs and can generate new revenue in ways that wouldn't have been possible before. A study by Accenture and Frontier Economics found that in manufacturing alone, AI could lead to a worldwide output boost of 4 trillion US$ over the next 15 years. But there are different hurdles to cross to get a piece of the pie, and not everyone fulfills the necessary requirements to benefit from applications of artificial intelligence.


Artificial Intelligence Market Demand & Future Scope Including Top Players – Jewish Market Reports

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Brandessence market research publishes market research reports & business insights produced by highly qualified and experienced industry analysts. Our research reports are available in a wide range of industry verticals including aviation, food & beverage, healthcare, ICT, Construction, Chemicals and lot more. Brand Essence Market Research report will be best fit for senior executives, business development managers, marketing managers, consultants, CEOs, CIOs, COOs, and Directors, governments, agencies, organizations and Ph.D. Students. We have a delivery center in Pune, India and our sales office is in London.


Numerai Tournament: Blending Traditional Quantitative Approach & Modern Machine Learning

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Numerai is a crowdsourced fund, a hedge fund that operates based on the results of stock price predictions made by an unspecified number of people. Numerai holds tournaments in which participants compete for forecasting performance. Numerai is a crowdsourced fund, a hedge fund that operates based on the results of stock price predictions made by an unspecified number of people. Numerai holds tournaments in which participants compete for forecasting performance. Tournament participants will build a predictive model based on an encrypted dataset provided by Numerai, and then use it to create a submission.


Helping Democratize AI, A Call to Action

#artificialintelligence

Companies across the globe are increasingly adopting Artificial Intelligence (AI). Meanwhile, forecasts for its global market value are skyrocketing. AI has the potential to save businesses substantial costs and can generate new revenue in ways that wouldn't have been possible before. A study by Accenture and Frontier Economics found that in manufacturing alone, AI could lead to a worldwide output boost of 4 trillion US$ over the next 15 years. But there are different hurdles to cross to get a piece of the pie, and not everyone fulfills the necessary requirements to benefit from applications of artificial intelligence.


Artificial Intelligence: worth the hype? - BusinessCloud

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The amount of venture capital money flowing into UK artificial intelligence start-ups hit a record-breaking $3.2 billion in 2019, making it one of the hottest sectors to be in. This financial boost, along with bolder algorithms, Big Data and better infrastructure, is bringing founders and funders to the AI equation. Yet according to a recent report, 40 per cent of European firms classified as AI start-ups do not actually use artificial intelligence. Is AI then just a fad – or is it worth the hype? AI makes it possible for human capabilities to be undertaken by technology at scale.


Explain by Example: Machine Learning

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I've been wanting to learn about investments, the stock market, machine learning, and artificial intelligence for a while now so I thought, why not combine them all together? So, today I decided to try and create my own machine learning model using Azure Machine Learning services, some free ASX historical data as my data sets and output a model that helps determine whether I should Buy, Hold, or Sell a stock listed on the Australian Stock Exchange. Since this is for experimental and learning purposes, I have to note that whatever model I am about to create and deploy should not be taken seriously (as a financial advisor, financial guidance, etc). But before I dive in any further, let's take a step back and discuss the basics… Machine Learning is one component of Artificial Intelligence (or AI). Artificial intelligence is essentially trying to use machines to imitate human-like behavior and intelligence. So then you may ask, "Well, is my calculator an AI system because it can calculate answers to equations much faster than I can."


Time Series Analysis & Predictive Modeling Using Supervised Machine Learning

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Time-Series involves temporal datasets that change over a period of time and time-based attributes are of paramount importance in these datasets. The trading prices of stocks change constantly over time, and reflect various unmeasured factors such as market confidence, external influences, and other driving forces that may be hard to identify or measure. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it. Forecasting the future value of a given stock is a crucial task as investing in stock market involves higher risk.. Here, given the historical daily close price for Dow-Jones Index, we would like to prepare and compare forecasting models. The black swan theory, which predicts that anomalous events, such as a stock market crash, are much more likely to occur than would be predicted by the normal distribution.