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 evaluate Unilever prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the ULVR 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 ULVR stock.

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

Key Points

1. How can neural networks improve predictions?
2. How do predictive algorithms actually work?
3. What are buy sell or hold recommendations?

ULVR Target Price Prediction Modeling Methodology

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. We consider Unilever Stock Decision Process with Multiple Regression where A is the set of discrete actions of ULVR 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 (Social Media Sentiment Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ULVR 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?

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

Sample Set: Neural Network
Stock/Index: ULVR Unilever
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 ULVR 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

Unilever assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the ULVR 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 ULVR stock.

Financial State Forecast for ULVR Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Operational Risk 7965
Market Risk4832
Technical Analysis3567
Fundamental Analysis3781
Risk Unsystematic7162

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 715 signals.

References

1. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
2. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
3. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
4. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
5. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
6. 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
7. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
Frequently Asked QuestionsQ: What is the prediction methodology for ULVR stock?
A: ULVR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression
Q: Is ULVR stock a buy or sell?
A: The dominant strategy among neural network is to Hold ULVR Stock.
Q: Is Unilever stock a good investment?
A: The consensus rating for Unilever is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of ULVR stock?
A: The consensus rating for ULVR is Hold.
Q: What is the prediction period for ULVR stock?
A: The prediction period for ULVR is (n+6 month)