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-Square1,2,3,4 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.

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

## Key Points

1. What are main components of Markov decision process?
2. What are main components of Markov decision process?
3. 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 γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Chi-Square)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Multi-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-Square1,2,3,4 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*B1Ba1
Operational Risk 6371
Market Risk4541
Technical Analysis8989
Fundamental Analysis4956
Risk Unsystematic5390

### Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 541 signals.

## References

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2. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
3. 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.
4. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
5. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
7. 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 QuestionsQ: 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)