AUC Score :
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n:
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Logistic Regression
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Summary
Mercury Systems Inc Common Stock prediction model is evaluated with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the MRCY stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.5 According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Speculative Trend
Key Points
- Multi-Task Learning (ML) for MRCY stock price prediction process.
- Logistic Regression
- Is Target price a good indicator?
- Nash Equilibria
- What is Markov decision process in reinforcement learning?
MRCY Stock Price Forecast
We consider Mercury Systems Inc Common Stock Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of MRCY stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4
Sample Set: Neural Network
Stock/Index: MRCY Mercury Systems Inc Common Stock
Time series to forecast: 8 Weeks
According to price forecasts, the dominant strategy among neural network is: Speculative Trend
n:Time series to forecast
p:Price signals of MRCY stock
j:Nash equilibria (Neural Network)
k:Dominated move of MRCY stock holders
a:Best response for MRCY target price
Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.5 In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.6,7
For further technical information as per how our model work we invite you to visit the article below:
MRCY Stock Forecast (Buy or Sell) Strategic Interaction Table
Strategic Interaction Table Legend:
X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)
Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)
Z axis (Grey to Black): *Technical Analysis%
Financial Data Adjustments for Multi-Task Learning (ML) based MRCY Stock Prediction Model
- To be eligible for designation as a hedged item, a risk component must be a separately identifiable component of the financial or the non-financial item, and the changes in the cash flows or the fair value of the item attributable to changes in that risk component must be reliably measurable.
- When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
- When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
- The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
MRCY Mercury Systems Inc Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B1 | B2 |
Income Statement | Ba3 | Caa2 |
Balance Sheet | B1 | C |
Leverage Ratios | Caa2 | Ba1 |
Cash Flow | Baa2 | C |
Rates of Return and Profitability | B2 | Baa2 |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
References
- S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
Frequently Asked Questions
Q: Is MRCY stock expected to rise?A: MRCY stock prediction model is evaluated with Multi-Task Learning (ML) and Logistic Regression and it is concluded that dominant strategy for MRCY stock is Speculative Trend
Q: Is MRCY stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend MRCY Stock.
Q: Is Mercury Systems Inc Common Stock stock a good investment?
A: The consensus rating for Mercury Systems Inc Common Stock is Speculative Trend and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of MRCY stock?
A: The consensus rating for MRCY is Speculative Trend.
Q: What is the forecast for MRCY stock?
A: MRCY target price forecast: Speculative Trend