In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. We evaluate Sunrun prediction models with Deductive Inference (ML) and Factor1,2,3,4 and conclude that the RUN stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy RUN stock.

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

## Key Points

1. Market Outlook
2. Can we predict stock market using machine learning?
3. Nash Equilibria ## RUN Target Price Prediction Modeling Methodology

Stock market or Share market is one of the most complicated and sophisticated way to do business. Small ownerships, brokerage corporations, banking sector, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithm to predict the future stock price for exchange by using open source libraries and preexisting algorithms to help make this unpredictable format of business a little more predictable. We consider Sunrun Stock Decision Process with Factor where A is the set of discrete actions of RUN 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+4 weeks) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of RUN 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?

## RUN Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: RUN Sunrun
Time series to forecast n: 04 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy RUN 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

Sunrun assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Factor1,2,3,4 and conclude that the RUN stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy RUN stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 5946
Market Risk6658
Technical Analysis3767
Fundamental Analysis7546
Risk Unsystematic3052

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 813 signals.

## References

1. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
2. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
3. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
4. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
5. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
6. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
7. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
Frequently Asked QuestionsQ: What is the prediction methodology for RUN stock?
A: RUN stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Factor
Q: Is RUN stock a buy or sell?
A: The dominant strategy among neural network is to Buy RUN Stock.
Q: Is Sunrun stock a good investment?
A: The consensus rating for Sunrun is Buy and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of RUN stock?
A: The consensus rating for RUN is Buy.
Q: What is the prediction period for RUN stock?
A: The prediction period for RUN is (n+4 weeks)