**Outlook:**Host Hotels & Resorts Inc. Common Stock assigned short-term Ba1 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 12 Dec 2022**for (n+3 month)

**Methodology :**Modular Neural Network (Market Volatility Analysis)

## Abstract

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted.(Vachhani, H., Obiadat, M.S., Thakkar, A., Shah, V., Sojitra, R., Bhatia, J. and Tanwar, S., 2020. Machine learning based stock market analysis: A short survey. In International Conference on Innovative Data Communication Technologies and Application (pp. 12-26). Springer, Cham.)** We evaluate Host Hotels & Resorts Inc. Common Stock prediction models with Modular Neural Network (Market Volatility Analysis) and Factor ^{1,2,3,4} and conclude that the HST 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

- What are main components of Markov decision process?
- Buy, Sell and Hold Signals
- What are main components of Markov decision process?

## HST Target Price Prediction Modeling Methodology

We consider Host Hotels & Resorts Inc. Common Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of HST 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(Factor)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of HST 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?

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

**Sample Set:**Neural Network

**Stock/Index:**HST Host Hotels & Resorts Inc. Common Stock

**Time series to forecast n: 12 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 Host Hotels & Resorts Inc. Common Stock

- For the purposes of measuring expected credit losses, the estimate of expected cash shortfalls shall reflect the cash flows expected from collateral and other credit enhancements that are part of the contractual terms and are not recognised separately by the entity. The estimate of expected cash shortfalls on a collateralised financial instrument reflects the amount and timing of cash flows that are expected from foreclosure on the collateral less the costs of obtaining and selling the collateral, irrespective of whether foreclosure is probable (ie the estimate of expected cash flows considers the probability of a foreclosure and the cash flows that would result from it). Consequently, any cash flows that are expected from the realisation of the collateral beyond the contractual maturity of the contract should be included in this analysis. Any collateral obtained as a result of foreclosure is not recognised as an asset that is separate from the collateralised financial instrument unless it meets the relevant recognition criteria for an asset in this or other Standards.
- Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.
- 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.
- An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.

*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

Host Hotels & Resorts Inc. Common Stock assigned short-term Ba1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Factor ^{1,2,3,4} and conclude that the HST 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 HST Host Hotels & Resorts Inc. Common Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba1 | Ba3 |

Operational Risk | 86 | 74 |

Market Risk | 88 | 75 |

Technical Analysis | 51 | 30 |

Fundamental Analysis | 90 | 62 |

Risk Unsystematic | 44 | 64 |

### Prediction Confidence Score

## References

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- Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
- E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., When to Sell and When to Hold AQN Stock. AC Investment Research Journal, 101(3).
- A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.

## Frequently Asked Questions

Q: What is the prediction methodology for HST stock?A: HST stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Factor

Q: Is HST stock a buy or sell?

A: The dominant strategy among neural network is to Sell HST Stock.

Q: Is Host Hotels & Resorts Inc. Common Stock stock a good investment?

A: The consensus rating for Host Hotels & Resorts Inc. Common Stock is Sell and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of HST stock?

A: The consensus rating for HST is Sell.

Q: What is the prediction period for HST stock?

A: The prediction period for HST is (n+3 month)