FBHS Fortune Brands Home & Security Stock Forecast Period (n+7) 23 Feb 2021


Stock Forecast


As of Tue Feb 23 2021 00:03:51 GMT+0000 (Coordinated Universal Time) shares of FBHS Fortune Brands Home & Security -0.7 percentage change in price since the previous day's close. Around 688376 of 138929000 changed hand on the market. The Stock opened at 86.57 with high and low of 86.08 and 88.31 respectively. The price/earnings ratio is: 22.04 and earning per share is 3.95. The stock quoted a 52 week high and low of 33.9 and 93.4 respectively.

BOSTON (AI Forecast Terminal) Tue, Feb 23, '21 AI Forecast today took the forecast actions: In the context of stock price realization of FBHS Fortune Brands Home & Security is a decision making process between multiple investors each of which controls a subset of design variables and seeks to minimize its cost function subject to future forecast constraints. That is, investors act like players in a game; they cooperate to achieve a set of overall goals.Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to combine the results from the Neural Network and Support Vector Machines. Machine Learning based technical analysis (n+7) for FBHS Fortune Brands Home & Security as below:
Using machine learning modified The random walk index model RWI equivalent to a model of stock market dynamics with price expectations, we analyze the reaction of investors to speculations. Analyzing those data we were able to establish the amount by which each stock felt the speculative attacks, a dampening factor which expresses the capacity of a market of absorving a shock, and also a frequency related with volatility after the speculation. Using the correlation matrices, the speculative buffer for the shares of FBHS Fortune Brands Home & Security as below:

FBHS Fortune Brands Home & Security Credit Rating Overview


We rerate FBHS Fortune Brands Home & Security because we use the multipliers stemming from the Gaussian distribution (with a 50% add-on for fat tail events) to transform a VaR at a x-confidence level into a VaR at the chosen confidence level. We use econometric methods for period (n+7) simulate with Ring Oscillators Ridge Regression. Reference code is: 4406. Beta DRL value REG 16 Rational Demand Factor LD 6070.7472. Other factors we consider include a company's frequency of debt issuance and market access, especially during times of company-specific stress or credit market turbulence. Credit Rating AI Process rely on primary sources of information: Sec Filings, Financial Statements, Credit Ratings, Semantic Signals. Take a look at Machine Learning section for Financial Deep Reinforcement Learning.

Oscillators are used for generating credit risk signals by using the semantic and financial signals. The value of the oscillators indicate the strength of trend. Using the correlation matrices, the risk map for FBHS Fortune Brands Home & Security as below:
Frequently Asked QuestionsQ: What is FBHS Fortune Brands Home & Security stock symbol?
A: FBHS Fortune Brands Home & Security stock referred as NYSE:FBHS
Q: What is FBHS Fortune Brands Home & Security stock price?
A: On share of FBHS Fortune Brands Home & Security stock can currently be purchased for approximately 86.94
Q: Do analysts recommend investors buy shares of FBHS Fortune Brands Home & Security ?
A: Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to combine the results from the Neural Network and Support Vector Machines. View Machine Learning based technical analysis for FBHS Fortune Brands Home & Security at daily forecast section
Q: What is the earning per share of FBHS Fortune Brands Home & Security ?
A: The earning per share of FBHS Fortune Brands Home & Security is 3.95
Q: What is the market capitalization of FBHS Fortune Brands Home & Security ?
A: The market capitalization of FBHS Fortune Brands Home & Security is 12078487599
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Disclaimers: AC Investment Inc. currently does not act as an equities executing broker, credit rating agency or route orders containing equities securities. In our Machine Learning experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.The rating information provided is for informational, non-commercial purposes only, does not constitute investment advice and is subject to conditions available in our Legal Disclaimer. Usage as a credit rating or as a benchmark is not permitted.

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