AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Deductive Inference (ML)
Hypothesis Testing : Beta
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.
Abstract
SM Energy Company Common Stock prediction model is evaluated with Deductive Inference (ML) and Beta1,2,3,4 and it is concluded that the SM stock is predictable in the short/long term. Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell
Key Points
- Can statistics predict the future?
- What is prediction in deep learning?
- Is now good time to invest?
SM Target Price Prediction Modeling Methodology
We consider SM Energy Company Common Stock Decision Process with Deductive Inference (ML) where A is the set of discrete actions of SM 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
F(Beta)5,6,7= X R(Deductive Inference (ML)) X S(n):→ 6 Month
n:Time series to forecast
p:Price signals of SM stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Deductive Inference (ML)
Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses.Beta
In statistics, beta (β) is a measure of the strength of the relationship between two variables. It is calculated as the slope of the line of best fit in a regression analysis. Beta can range from -1 to 1, with a value of 0 indicating no relationship between the two variables. A positive beta indicates that as one variable increases, the other variable also increases. A negative beta indicates that as one variable increases, the other variable decreases. For example, a study might find that there is a positive relationship between height and weight. This means that taller people tend to weigh more. The beta coefficient for this relationship would be positive.
For further technical information as per how our model work we invite you to visit the article below:
How do AC Investment Research machine learning (predictive) algorithms actually work?
SM Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: SM SM Energy Company Common Stock
Time series to forecast: 6 Month
According to price forecasts, the dominant strategy among neural network is: Sell
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 Deductive Inference (ML) based SM Stock Prediction Model
- The definition of a derivative refers to non-financial variables that are not specific to a party to the contract. These include an index of earthquake losses in a particular region and an index of temperatures in a particular city. Non-financial variables specific to a party to the contract include the occurrence or non-occurrence of a fire that damages or destroys an asset of a party to the contract. A change in the fair value of a non-financial asset is specific to the owner if the fair value reflects not only changes in market prices for such assets (a financial variable) but also the condition of the specific non-financial asset held (a non-financial variable). For example, if a guarantee of the residual value of a specific car exposes the guarantor to the risk of changes in the car's physical condition, the change in that residual value is specific to the owner of the car.
- An example of a fair value hedge is a hedge of exposure to changes in the fair value of a fixed-rate debt instrument arising from changes in interest rates. Such a hedge could be entered into by the issuer or by the holder.
- When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
- The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.
*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.
SM SM Energy Company Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba2 | B1 |
Income Statement | Ba2 | Ba3 |
Balance Sheet | Ba2 | Baa2 |
Leverage Ratios | Ba1 | B1 |
Cash Flow | Baa2 | C |
Rates of Return and Profitability | Caa2 | C |
*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?
Conclusions
SM Energy Company Common Stock is assigned short-term Ba2 & long-term B1 estimated rating. SM Energy Company Common Stock prediction model is evaluated with Deductive Inference (ML) and Beta1,2,3,4 and it is concluded that the SM stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
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- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
Frequently Asked Questions
Q: What is the prediction methodology for SM stock?A: SM stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Beta
Q: Is SM stock a buy or sell?
A: The dominant strategy among neural network is to Sell SM Stock.
Q: Is SM Energy Company Common Stock stock a good investment?
A: The consensus rating for SM Energy Company Common Stock is Sell and is assigned short-term Ba2 & long-term B1 estimated rating.
Q: What is the consensus rating of SM stock?
A: The consensus rating for SM is Sell.
Q: What is the prediction period for SM stock?
A: The prediction period for SM is 6 Month