Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We evaluate DUNELM GROUP PLC prediction models with Modular Neural Network (CNN Layer) and Logistic Regression1,2,3,4 and conclude that the LON:DNLM 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 Hold LON:DNLM stock.

Keywords: LON:DNLM, DUNELM GROUP PLC, 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. What is prediction model?
3. Operational Risk ## LON:DNLM Target Price Prediction Modeling Methodology

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. We consider DUNELM GROUP PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:DNLM 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(Logistic 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 (CNN Layer)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:DNLM 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:DNLM Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:DNLM DUNELM GROUP PLC
Time series to forecast n: 17 Sep 2022 for (n+4 weeks)

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

DUNELM GROUP PLC assigned short-term B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Logistic Regression1,2,3,4 and conclude that the LON:DNLM 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 Hold LON:DNLM stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Ba2
Operational Risk 3139
Market Risk7850
Technical Analysis7881
Fundamental Analysis3081
Risk Unsystematic3786

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 508 signals.

## References

1. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
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. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
4. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
5. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
6. 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.
7. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
Frequently Asked QuestionsQ: What is the prediction methodology for LON:DNLM stock?
A: LON:DNLM stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Logistic Regression
Q: Is LON:DNLM stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:DNLM Stock.
Q: Is DUNELM GROUP PLC stock a good investment?
A: The consensus rating for DUNELM GROUP PLC is Hold and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of LON:DNLM stock?
A: The consensus rating for LON:DNLM is Hold.
Q: What is the prediction period for LON:DNLM stock?
A: The prediction period for LON:DNLM is (n+4 weeks)