Stock market is basically nonlinear in nature and the research on stock market is one of the most important issues in recent years. People invest in stock market based on some prediction. For predict, the stock market prices people search such methods and tools which will increase their profits, while minimize their risks. Prediction plays a very important role in stock market business which is very complicated and challenging process. We evaluate Eversource prediction models with Modular Neural Network (DNN Layer) and Multiple Regression1,2,3,4 and conclude that the ES 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 ES stock.

Keywords: ES, Eversource, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What are the most successful trading algorithms?
2. How can neural networks improve predictions?
3. Nash Equilibria

## ES Target Price Prediction Modeling Methodology

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We consider Eversource Stock Decision Process with Multiple Regression where A is the set of discrete actions of ES 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(Multiple Regression)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(Modular Neural Network (DNN Layer)) X S(n):→ (n+6 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of ES stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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How do AC Investment Research machine learning (predictive) algorithms actually work?

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

Sample Set: Neural Network
Stock/Index: ES Eversource
Time series to forecast n: 13 Sep 2022 for (n+6 month)

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

Eversource assigned short-term B1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Multiple Regression1,2,3,4 and conclude that the ES 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 ES stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Baa2
Operational Risk 6050
Market Risk5777
Technical Analysis3870
Fundamental Analysis8390
Risk Unsystematic7184

### Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 839 signals.

## References

1. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
2. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
3. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
4. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
5. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
6. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
7. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
Frequently Asked QuestionsQ: What is the prediction methodology for ES stock?
A: ES stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Multiple Regression
Q: Is ES stock a buy or sell?
A: The dominant strategy among neural network is to Hold ES Stock.
Q: Is Eversource stock a good investment?
A: The consensus rating for Eversource is Hold and assigned short-term B1 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of ES stock?
A: The consensus rating for ES is Hold.
Q: What is the prediction period for ES stock?
A: The prediction period for ES is (n+6 month)

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