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Stock Forecasting By Using AI: Top 10 Stock Picks For This Week + Algorithmic Forecast For DVAX, MU, & WIX ❯❯

I Know First Weekly NewsletterInvestment Selection Using AI Predictive AlgorithmJuly 10th, 2018

This Week's Top Article:Bayesian Inference &I Know First’s ApplicationRead More | Related News
This Week's Top Stock Prediction:Best Stocks Under $5Based on Machine Learning91.08% | 1 Month

This Week's Highlights

Stock Forecasting By Using AI: Top 10 Stock Picks For July, 2018 + Algorithmic Forecast For AAPL, NVDA, & WIX ❯❯
S&P 500 Forecast








Stock Selection By Using Deep Learning: Top 10 Stocks Under $5 For The Next Week+ Top 10 Aggressiv Stocks + AMZ, FL, AAPL & WIX Stock Forecast
NVDA Forecast For July 2018: Buy Or Sell? ❯❯


Deep Reinforcement Learning: Building A “Self-Driving Car” In Financial World.When talking about deep learning, many seems to refer it to only computer science. Today, we will talk about a part of deep learning that utilize many areas of both natural science and social science: Deep Reinforcement Learning. Deep Reinforcement Learning has been changing our world every day. This learning method helps scientists to explore new disease treatments, help engineers to develop self-driving cars, and help investors to maximize their profit.Reinforcement Learning refers to a Deep Learning area that the algorithms are trained based on reward and punishment. Reinforcement Learning is a trial and error self-learning process without a supervisor. They will receive reward signals for each of their action. Moreover, the reward and punishment will be delayed, meaning that the result of an action cannot be observed instantaneously. Instead, effects of an action will be visible many steps later. In fact, reinforcement learning is a combination of many different fields ranging from mathematics, economics to neuroscience. For instance, in economics, people conducted research on game theory. In neuroscience, people studied how our brain function and make decisions. Similarly, in engineering, researchers learned about optimal control. Since in all of these subjects, people try to optimize the reward of a set of action, they can be considered a part of reinforcement learning.In order to find an optimal policy, Markov Decision Process (NYSE:MDP) and Q-learning will be applied. Markov Decision Processes (MDPs) are decision-making models that are used in deep reinforcement learning. There are five elements in the MDPs: state, action, policy, reward, and discount factor. Based on the five elements, the value of each policy will be calculated using Bellman equation. However, in the real world, our algorithm will not know all of the probabilities of each state. Hence, there is no way to “calculate” an optimal policy. To solve this problem, we will need to apply a method called Q-learning. Instead of the value function, we will take the Q-value that represents the value of taking a particular action in a particular state. We can calculate Q-value by adding the immediate expected reward to the best possible outcome of the onward states.Read More about Deep Reinforcement Learning and How it can be applied in market forecastTSLA Stock Forecast: Bringing Electric Vehicles To The Masses

Tesla is trying to bring the world into a more sustainable future through the popularization of electric cars. The company has been producing luxury electric vehicles for a decent amount of time and is now attempting to make a car for the masses: the $35,000 base Model 3. Year to date, Tesla stock has increased by about 10%. However, the electric car company is yet to post a profit as it has consistently burned through cash for R&D and production. The future of Tesla is highly dependent on the production of the Model 3 because the company does not expect an increase in Model S and Model X deliveries this year, but has high expectations for its less luxurious car: the Model 3. Ideally, mass production of the Model 3 will allow economies of scale to present themselves and increase the gross margin on the car, thus increasing the company’s total gross margin. Over the past year, Tesla’s GAAP Gross Margin has decreased as it began ramping up production for its newest model.But the question remained, could Tesla keep up with the massive demand for the Model 3 and when would they finally turn a profit? Elon Musk has been making promises about Model 3 production since its inception, often shooting for goals that are not realistic. Musk told investors to expect Model 3 production to increase to 2,500 cars a week by the end of Q1 2018, yet he missed this target. So when Musk told the world Tesla planned on producing 5,000 Model 3s a week by the end of Q2 2018, more than double the prior quarter’s missed target, analysts were skeptical. Bloomberg even created a Tesla Tracker to monitor the production of Model 3 cars by monitoring VIN data (a VIN must be registered for every car produced). Goldman Sachs analysts were bearish about Tesla’s production in later June with the end of quarter soon approaching. This prompted Musk to send out a company wide email saying that these analysts were in for a “rude awakening” and Tesla was still “quite likely” hit their production target.Read More about Model 3 and I Know First's Forecast for TSLA

Clean Energy Fuels Corp Returns Up To 44.1% In A Month As ForecastedIncorporated on April 17, 2001, Clean Energy Fuels Corp. (NASDAQ:CLNE) is a transportation-focused natural gas provider supplying compressed natural gas (CNG), liquefied natural gas (NYSEMKT:LNG) and renewable natural gas (NYSE:RNG) for light, medium and heavy-duty vehicles, and providing operation and maintenance (O&M) services for natural gas fueling stations in the United States and Canada. On May 10th, Clean Energy Fuels reported first-quarter earnings and announced that oil and gas giant Total SA was taking a 25% stake in the company. The stock price has surged by 77% since then thanks to climbing oil prices. Diesel prices increased by 10% and crude futures are up 16% since March 1st and CLNE has surged 136% in the same time as a result. Natural gas is an alternative for diesel, so when diesel prices increase, many opt to take advantage of the cleaner and cheaper option instead. The natural gas is promised to have great potential of demand in the future. Currently natural gas accounted for less a quarter of the energy consumption. According to BP’s Energy Outlook 2035 forecast, a third of the probable rise in global energy demand will be supplied by natural gas. The market opportunity owes much to its intrinsic properties: natural gas is cleaner than coal or oil, and also more readily available and affordable while alternatives like EVs and hydrogen have not been road ready especially for heavy transit for the near future.On May 9th, the company entered the agreement with Total SA where Total will buy 25% of Clean Energy’s outstanding stocks and support the company to “accelerate the remarkable innovation capacities”. The acquirer also agreed to provide $100 million in credit support for Clean Energy’s leasing program to accelerate adoption of natural gas trucks. On June 22nd, the company announced the opening of a compressed natural gas station in Kansas that would provide over 1.2 million-gallon equivalents of natural gas to 170 transit buses and refuse vehicles, which encouraged investors to be positive about the company’s future prospects.Read More about the reason behind an incredible return of CLNE3 Ways To Cluster Data Using Unsupervised Learning

The I Know First algorithm builds relationships between the various assets in the stock market in its prediction process. For example, on May 27, 2018, the machine learning algorithm gave a bullish prediction for Clean Energy Fuels Corp. for a one month time period. The natural gas provider’s stock increased over this time period in accordance with the forecast mostly thanks to climbing diesel prices. While creating the forecast, the algorithm considered the relationship between CLNE and diesel and incorporated this information into the forecast to make it more accurate. While it seems like common sense to a human that if diesel, a substitute for clean energy, increases in price then there will be an increased demand in natural gas and natural gas providers like Clean Energy Fuels Corp. will benefit. However, the I Know First algorithm does not know what goods and services Clean Energy provides, it simply knows the ticker symbol. Yet, the algorithm deduces the relationship using machine learning techniques.One of the most popular methods to implement this is cluster analysis which uses a layered method in order to group the data into relevant groups of clusters (hence the name) to reveal a previously unseen structure in the system. There are many different similarity metrics that can be used to create these clusters such as probabilistic or Euclidean distance and the ideal one depends on what the end goal is. Additionally, some clustering methods change greatly depending on which metric is used, whereas some are less susceptible. Clustering can be applied to many different fields.There are many different types of clustering, but we will discuss 3 of the main ones in depth: K-Means clustering, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN). K-Means clustering is one of the most popular and works by separating the data into k distinct groups based on each data point’s distance to the centroid of the cluster. Hierarchical clustering builds a cluster tree that depicts a multilevel hierarchy of the data. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is similar to Mean Shift Clustering which uses a sliding window to create clusters based on dense areas of data points.Read More about Three Types of Clustering and How I Know First implements themAMD Stock Forecast: AMD Soars Beyond Optimal Buying Price

Advanced Micro Devices, Inc. (NASDAQ:AMD) is an American semiconductor company. Incorporated on May 1, 1969, the company offers x86 microprocessors, chipsets, GPUs, APU, server and embedded processors, and SoC products and technology. The company had a remarkable first quarter this year. YoY, operating revenue increased 39.81%, EBIT 1.92K%, cash by 21.51%, and operating expense decreased by 4%. The company also showed confidence in the future of itself by increasing treasury stock YoY by 9.09%. AMD increased gross margin by 4% to 36%.AMD saw great success in the computing and graphics segment with revenue up 95% to $1.12 billion. This success was due in part to their Radeon and Ryzen products, with a sizable portion of sales being laptops which carry this chip. This includes HP and Lenovo which had solid sales for the quarter. The company also stated that 10% of revenue came from their Radeon lineup being used for cryptocurrency mining. AMD’s brand new EPYC data center chips have been growing by double digits from last quarter, providing the company with rising revenue.However, currently AMD’s price is inflated. Right now, their PE ratio is 78.89, compared to the 19.49 industry average; the price to book value is 20.32, compared to the industry’s 3.848; and the PEG is 5.49, compared to the industry’s 1.653. This is showing that investors are paying too much for the expected growth of the stock. The PE 10 ratio for AMD is at -17.30, compared to its competitors, Skyworks Solutions (NASDAQ:SWKS), Intel (NASDAQ:INTC), and Micron (NASDAQ:MU), who have PE 10 ratios of 42.25, 25.96, and 46.38, respectively. AMD’s PE 10 is extremely low and concerning for stockholders. Although future earnings are expected to increase, it’s too risky to invest in this stock holding no current value.Read More about the potential of AMD and I Know First's forecast

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Dear Readers,This week, we would like to explain to you more of how our algorithms work using Bayesian Neural Network. As we all know, deep learning has become a buzzward in recent years. In fact, it has once gained much attention and excitements under the name neural networks early back in 1980’s. As a starting point, a deep learning vanilla method called multilayer perceptron (NYSE:MLP), or feed-forward neural networks, will be discussed here. MLP serves as a basic tool for both classification and regression, two fundamental tasks of machine learning. To improve the accuracy of the results, Bayesian inference has been applied. Bayesian neural networks adhere to probabilistic model, which has a long history and is undergoing a tremendous wave of revival. Since most real-world problems have a particular structure, machine learning packages would be much better and powerful if they are customized to the problems with the structure embedded. The structure, or the mathematical model of the situation for which you are predicting, is commonly expressed by certain probability theory, of which Bayes’ Theory is one of the most famous and widely used one.In neural network model, the parameters we care about is the great amount of uncertain network weights between layers. With Bayesian inference, we can come up with a probability distribution over the weights, given the distributional training data, to be used as next layer’s inputs. Therefore in the end, we will obtain an entire distribution over the network output, which increase our predicting accuracy and confidence level. At the same time, the issues related with regularization, overfitting and model comparison that appear in pure neural network can be naturally addressed. Undoubtedly, it is a natural and nice marriage between these two frameworks.From the founding of the company I Know First has used probabilistic approach to forecasting. In Bayesian terms the historical data represents our prior knowledge of the market behavior with each variable (asset) having its own statistical properties which vary with time. After each trading day, new data is added to the data set and is learned, resulting in the updated view of the markets. Thus, each new forecast is based on the latest updated model of the market (Bayesian posterior).Read More About How I Know First Applied Bayesian Neural NetworksWarmest RegardsYaron Golgher, Co-Founder and CEO


Forex Forecast: 72.22% Hit Ratio in 1 MonthJuly 04 | Read MoreCurrencies Ranking: 70.37% Hit Ratio in 7 DaysJuly 03 | Read MoreCurrencies Prediction: 64.81% Hit Ratio in 14 DaysJuly 03 | Read More
Gold Prediction: Returns up to 3.68% in 7 DaysJuly 03 | Read MoreCommodities Outlook: Returns up to 8.12% in 7 DaysJuly 01 | Read MoreGold Forecast: Returns up to 1.84% in 7 DaysJune 28 | Read More
Get The Top Ten Commodities For July, 2018 By AI Algorithm
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Concerns about slow boiling China-US trade war make traders in a holding pattern. The US Dollar was traded in a small range as market participation stay low before the July 4th holiday. US dollar becomes a beneficiary of risk-off trading sentiment and Chinese Yuan keeps depreciating as trade tension remains elevated. The market sentiment improved as the Chinese central bank pledged to keep the exchange rate “basically stable”, but the tension is growing with the approaching July 6th deadline when $34 billion of tariffs on Chinese export is due. The coming US ADP Private Employment report and the US ISM Non-Manufacturing Composite will provide confirmation for the future trend of this currency. It is expected that the increase in June private payrolls will be seen while the non-manufacturing index will fall, as a result of the uncertainty of supply and prices in non-manufacturing sector due to the escalating trade tensions.On the other hand, the oil price has stayed low since December 2014 when robust global production significantly exceeded demand. However, on June 29, WTI (West Texas Intermediate) price surged by more than 4% to $74.15 per barrel, the highest level since the end of 2014. The immediate cause is the production increase decision announced during the OPEC meeting on June 22. The logic here may look counterintuitive as the price is higher while increasing supply. We are now elaborating the causal relationship in more details from the geopolitical background.In the context of steadily rising oil demand and unexpected supply cut as stated above, the market was anticipating extra supply from OPEC members. As expected, the decision of increasing production was made following the meeting, with an intent to put a cap on the rising prices and fill the supply gaps. According to OPEC’s announcement, the change would restore 1 million bpd to the market. However, on the one hand, many analysts think that it is not realistic to push some members who are already pumping at their full capacity to further bump output. On the other hand, even if the output increase is strictly followed through, the volume is much less the 1.5 million bpd from market expectation. Therefore it may still be inadequate to save the market from deficit and future declines in global crude oil inventories is highly possible.


Apple News: Apple Surpasses Streaming Giant SpotifyJuly 08 | Read MoreApple News: Upcoming Product Updates Build On PredecessorsJuly 01 | Read MoreApple News: Apple Admits to Faulty MacBook KeyboardJune 24 | Read More
Apple News: Apple Takes Action to Protect Users’ PrivacyJune 17 | Read MoreAs Always Apple Tops Expectations and Stays Different – WWDC 2018June 06 | Read MoreApple News: What to Expect – Apple WWDC 2018June 03 | Read More
AAPL Stock Forecast: What to Expect From AAPL?
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As of this week, Apple Music has surpassed Spotify’s paid subscriber count in the United States, making it the number one streaming service in the country. According to Digital Music News, Apple music, with a growth rate of 5% and over 20 million U.S. subscribers, is growing faster than Spotify. However, Spotify still holds the reign worldwide with over 70 million subscribers compared to Apples 45 million.This is a big win for Apple, but it will be hard to get a global win on Spotify. The majority of iPhone users reside in the United States, so it is easier for them to use Apple Music since it is built into iOS. That being said, Spotify has an advantage on Apple due to it’s free streaming feature. Although they have to listen to ads, Spotify has 170 million users with the inclusion of those who stream for free.Apple does have an advantage over Spotify in a couple areas. For starters, Apple Music has 10 million more songs than Spotify. Also, being that Apple is such a giant company, it is easier for them to snatch exclusive albums. For example, Dr Dre’s highly anticipated album, Compton, was only available to be streamed on Apple Music upon release. As Apple releases more exclusive content, it will bring more Spotify users over. Also, with Apple’s upcoming video streaming service, there is rumored to be a package that combines both the music and video services, another source of new users.Read More about Apple News.


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