FSSIU Fortistar Sustainable Solutions Corp. Unit

Outlook: Fortistar Sustainable Solutions Corp. Unit assigned short-term B3 & long-term B1 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 18 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. (Yoo, P.D., Kim, M.H. and Jan, T., 2005, November. Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) (Vol. 2, pp. 835-841). IEEE.) We evaluate Fortistar Sustainable Solutions Corp. Unit prediction models with Modular Neural Network (Financial Sentiment Analysis) and Linear Regression^1,2,3,4 and conclude that the FSSIU stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

Key Points

1. What is Markov decision process in reinforcement learning?
2. Can we predict stock market using machine learning?
3. What is prediction in deep learning?

FSSIU Target Price Prediction Modeling Methodology

We consider Fortistar Sustainable Solutions Corp. Unit Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of FSSIU 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(Linear 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of FSSIU 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?

FSSIU Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: FSSIU Fortistar Sustainable Solutions Corp. Unit
Time series to forecast n: 18 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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 Fortistar Sustainable Solutions Corp. Unit

1. An entity may retain the right to a part of the interest payments on transferred assets as compensation for servicing those assets. The part of the interest payments that the entity would give up upon termination or transfer of the servicing contract is allocated to the servicing asset or servicing liability. The part of the interest payments that the entity would not give up is an interest-only strip receivable. For example, if the entity would not give up any interest upon termination or transfer of the servicing contract, the entire interest spread is an interest-only strip receivable. For the purposes of applying paragraph 3.2.13, the fair values of the servicing asset and interest-only strip receivable are used to allocate the carrying amount of the receivable between the part of the asset that is derecognised and the part that continues to be recognised. If there is no servicing fee specified or the fee to be received is not expected to compensate the entity adequately for performing the servicing, a liability for the servicing obligation is recognised at fair value.
2. Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
3. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
4. The accounting for the forward element of forward contracts in accordance with paragraph 6.5.16 applies only to the extent that the forward element relates to the hedged item (aligned forward element). The forward element of a forward contract relates to the hedged item if the critical terms of the forward contract (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the forward contract and the hedged item are not fully aligned, an entity shall determine the aligned forward element, ie how much of the forward element included in the forward contract (actual forward element) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.16). An entity determines the aligned forward element using the valuation of the forward contract that would have critical terms that perfectly match the hedged item.

*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

Fortistar Sustainable Solutions Corp. Unit assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Linear Regression^1,2,3,4 and conclude that the FSSIU stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

Financial State Forecast for FSSIU Fortistar Sustainable Solutions Corp. Unit Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Operational Risk	33	89
Market Risk	32	35
Technical Analysis	82	31
Fundamental Analysis	54	67
Risk Unsystematic	48	61

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 710 signals.

References

1. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
2. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., When to Sell and When to Hold FTNT Stock. AC Investment Research Journal, 101(3).
4. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
5. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
6. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
7. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42

Frequently Asked Questions

Q: What is the prediction methodology for FSSIU stock?
A: FSSIU stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Linear Regression
Q: Is FSSIU stock a buy or sell?
A: The dominant strategy among neural network is to Sell FSSIU Stock.
Q: Is Fortistar Sustainable Solutions Corp. Unit stock a good investment?
A: The consensus rating for Fortistar Sustainable Solutions Corp. Unit is Sell and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of FSSIU stock?
A: The consensus rating for FSSIU is Sell.
Q: What is the prediction period for FSSIU stock?
A: The prediction period for FSSIU is (n+3 month)

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