Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We evaluate CERES POWER HOLDINGS PLC prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Factor1,2,3,4 and conclude that the LON:CWR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CWR stock.

Keywords: LON:CWR, CERES POWER HOLDINGS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. What is prediction model?
3. How useful are statistical predictions?

## LON:CWR Target Price Prediction Modeling Methodology

Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value. We consider CERES POWER HOLDINGS PLC Stock Decision Process with Factor where A is the set of discrete actions of LON:CWR 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of LON:CWR 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?

## LON:CWR Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:CWR CERES POWER HOLDINGS PLC
Time series to forecast n: 24 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CWR 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

CERES POWER HOLDINGS PLC assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Factor1,2,3,4 and conclude that the LON:CWR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CWR stock.

### Financial State Forecast for LON:CWR Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 4132
Market Risk7489
Technical Analysis8360
Fundamental Analysis4868
Risk Unsystematic6482

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 870 signals.

## References

1. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
2. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
3. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
4. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
5. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
6. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
7. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CWR stock?
A: LON:CWR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Factor
Q: Is LON:CWR stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CWR Stock.
Q: Is CERES POWER HOLDINGS PLC stock a good investment?
A: The consensus rating for CERES POWER HOLDINGS PLC is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:CWR stock?
A: The consensus rating for LON:CWR is Hold.
Q: What is the prediction period for LON:CWR stock?
A: The prediction period for LON:CWR is (n+1 year)