A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. ** We evaluate Essex Property Trust prediction models with Multi-Instance Learning (ML) and Chi-Square ^{1,2,3,4} and conclude that the ESS stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ESS stock.**

**ESS, Essex Property Trust, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What are main components of Markov decision process?
- What are main components of Markov decision process?
- Reaction Function

## ESS Target Price Prediction Modeling Methodology

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. We consider Essex Property Trust Stock Decision Process with Chi-Square where A is the set of discrete actions of ESS 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Chi-Square)

^{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(Multi-Instance Learning (ML)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of ESS stock

j:Nash equilibria

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?

## ESS Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**ESS Essex Property Trust

**Time series to forecast n: 14 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ESS stock.**

**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 (Yellow to Green): *Technical Analysis%**

## Conclusions

Essex Property Trust assigned short-term B1 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Multi-Instance Learning (ML) with Chi-Square ^{1,2,3,4} and conclude that the ESS stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ESS stock.**

### Financial State Forecast for ESS Stock Options & Futures

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

Outlook* | B1 | Ba1 |

Operational Risk | 63 | 71 |

Market Risk | 45 | 41 |

Technical Analysis | 89 | 89 |

Fundamental Analysis | 49 | 56 |

Risk Unsystematic | 53 | 90 |

### Prediction Confidence Score

## References

- Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
- Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.

## Frequently Asked Questions

Q: What is the prediction methodology for ESS stock?A: ESS stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Chi-Square

Q: Is ESS stock a buy or sell?

A: The dominant strategy among neural network is to Hold ESS Stock.

Q: Is Essex Property Trust stock a good investment?

A: The consensus rating for Essex Property Trust is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of ESS stock?

A: The consensus rating for ESS is Hold.

Q: What is the prediction period for ESS stock?

A: The prediction period for ESS is (n+6 month)