GJR Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1

Outlook: Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 assigned short-term Baa2 & long-term B2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 11 Dec 2022 for (n+1 year)
Methodology : Multi-Task Learning (ML)

Abstract

Market systems are so complex that they overwhelm the ability of any individual to predict. But it is crucial for the investors to predict stock market price to generate notable profit. We have taken into factors such as Commodity Prices (crude oil, gold, silver), Market History, and Foreign exchange rate (FEX) that influence the stock trend.(Umer, M., Awais, M. and Muzammul, M., 2019. Stock market prediction using machine learning (ML) algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), pp.97-116.) We evaluate Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 prediction models with Multi-Task Learning (ML) and ElasticNet Regression^1,2,3,4 and conclude that the GJR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Key Points

1. How accurate is machine learning in stock market?
2. Why do we need predictive models?
3. Market Outlook

GJR Target Price Prediction Modeling Methodology

We consider Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of GJR 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Task Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of GJR stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

GJR Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: GJR Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1
Time series to forecast n: 11 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

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%

Adjusted IFRS* Prediction Methods for Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1

1. For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).
2. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
3. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
4. The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with ElasticNet Regression^1,2,3,4 and conclude that the GJR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Financial State Forecast for GJR Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	B2
Operational Risk	79	42
Market Risk	88	36
Technical Analysis	78	86
Fundamental Analysis	70	59
Risk Unsystematic	66	39

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 796 signals.

References

1. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
2. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
3. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
4. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
5. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
6. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
7. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.

Frequently Asked Questions

Q: What is the prediction methodology for GJR stock?
A: GJR stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and ElasticNet Regression
Q: Is GJR stock a buy or sell?
A: The dominant strategy among neural network is to Sell GJR Stock.
Q: Is Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 stock a good investment?
A: The consensus rating for Synthetic Fixed-Income Securities Inc. STRATS Trust for Procter&Gamble Securities Series 2006-1 is Sell and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of GJR stock?
A: The consensus rating for GJR is Sell.
Q: What is the prediction period for GJR stock?
A: The prediction period for GJR is (n+1 year)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login