HTH Hilltop Holdings Inc.

Outlook: Hilltop Holdings Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 03 Apr 2023 for (n+16 weeks)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

Hilltop Holdings Inc. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression^1,2,3,4 and it is concluded that the HTH stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. What is the use of Markov decision process?
2. What is the best way to predict stock prices?
3. Prediction Modeling

HTH Target Price Prediction Modeling Methodology

We consider Hilltop Holdings Inc. Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of HTH 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(Polynomial 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 (News Feed Sentiment Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of HTH 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?

HTH Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: HTH Hilltop Holdings Inc.
Time series to forecast n: 03 Apr 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

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%

IFRS Reconciliation Adjustments for Hilltop Holdings Inc.

1. For example, an entity hedges an exposure to Foreign Currency A using a currency derivative that references Foreign Currency B and Foreign Currencies A and B are pegged (ie their exchange rate is maintained within a band or at an exchange rate set by a central bank or other authority). If the exchange rate between Foreign Currency A and Foreign Currency B were changed (ie a new band or rate was set), rebalancing the hedging relationship to reflect the new exchange rate would ensure that the hedging relationship would continue to meet the hedge effectiveness requirement for the hedge ratio in the new circumstances. In contrast, if there was a default on the currency derivative, changing the hedge ratio could not ensure that the hedging relationship would continue to meet that hedge effectiveness requirement. Hence, rebalancing does not facilitate the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item changes in a way that cannot be compensated for by adjusting the hedge ratio
2. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
3. The decision of an entity to designate a financial asset or financial liability as at fair value through profit or loss is similar to an accounting policy choice (although, unlike an accounting policy choice, it is not required to be applied consistently to all similar transactions). When an entity has such a choice, paragraph 14(b) of IAS 8 requires the chosen policy to result in the financial statements providing reliable and more relevant information about the effects of transactions, other events and conditions on the entity's financial position, financial performance or cash flows. For example, in the case of designation of a financial liability as at fair value through profit or loss, paragraph 4.2.2 sets out the two circumstances when the requirement for more relevant information will be met. Accordingly, to choose such designation in accordance with paragraph 4.2.2, the entity needs to demonstrate that it falls within one (or both) of these two circumstances.
4. It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.

*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.

Conclusions

Hilltop Holdings Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating. Hilltop Holdings Inc. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression^1,2,3,4 and it is concluded that the HTH stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

HTH Hilltop Holdings Inc. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B3	Baa2
Balance Sheet	B1	Ba3
Leverage Ratios	Baa2	C
Cash Flow	Baa2	Baa2
Rates of Return and Profitability	Ba3	B3

*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?

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 672 signals.

References

1. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you know when a stock will go up or down?(STJ Stock Forecast). AC Investment Research Journal, 101(3).
3. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
4. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
5. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
6. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you know when a stock will go up or down?(STJ Stock Forecast). AC Investment Research Journal, 101(3).
7. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press

Frequently Asked Questions

Q: What is the prediction methodology for HTH stock?
A: HTH stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression
Q: Is HTH stock a buy or sell?
A: The dominant strategy among neural network is to Hold HTH Stock.
Q: Is Hilltop Holdings Inc. stock a good investment?
A: The consensus rating for Hilltop Holdings Inc. is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of HTH stock?
A: The consensus rating for HTH is Hold.
Q: What is the prediction period for HTH stock?
A: The prediction period for HTH is (n+16 weeks)

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