Can stock prices be predicted? (LON:MNDI Stock Forecast)

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. We evaluate MONDI PLC prediction models with Active Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:MNDI 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 Buy LON:MNDI stock.

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

Key Points

1. What is statistical models in machine learning?
2. What is Markov decision process in reinforcement learning?
3. How do you know when a stock will go up or down?

LON:MNDI Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider MONDI PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:MNDI 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(Wilcoxon Rank-Sum Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:MNDI 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:MNDI Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:MNDI MONDI PLC
Time series to forecast n: 24 Sep 2022 for (n+1 year)

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

MONDI PLC assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:MNDI 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 Buy LON:MNDI stock.

Financial State Forecast for LON:MNDI Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba3
Operational Risk	79	84
Market Risk	72	81
Technical Analysis	59	40
Fundamental Analysis	65	36
Risk Unsystematic	76	64

Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 730 signals.

References

1. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
2. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
3. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
4. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
5. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
6. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
7. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678

Frequently Asked Questions

Q: What is the prediction methodology for LON:MNDI stock?
A: LON:MNDI stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:MNDI stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:MNDI Stock.
Q: Is MONDI PLC stock a good investment?
A: The consensus rating for MONDI PLC is Buy and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:MNDI stock?
A: The consensus rating for LON:MNDI is Buy.
Q: What is the prediction period for LON:MNDI stock?
A: The prediction period for LON:MNDI is (n+1 year)

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