LON:DNL DIURNAL GROUP PLC

Outlook: DIURNAL GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 16 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. (Choudhry, R. and Garg, K., 2008. A hybrid machine learning system for stock market forecasting. International Journal of Computer and Information Engineering, 2(3), pp.689-692.) We evaluate DIURNAL GROUP PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression^1,2,3,4 and conclude that the LON:DNL stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Key Points

1. Reaction Function
2. Can neural networks predict stock market?
3. Which neural network is best for prediction?

LON:DNL Target Price Prediction Modeling Methodology

We consider DIURNAL GROUP PLC Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of LON:DNL 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:DNL stock

j:Nash equilibria (Neural Network)

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

Sample Set: Neural Network
Stock/Index: LON:DNL DIURNAL GROUP PLC
Time series to forecast n: 16 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for DIURNAL GROUP PLC

1. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
2. The characteristics of the hedged item, including how and when the hedged item affects profit or loss, also affect the period over which the forward element of a forward contract that hedges a time-period related hedged item is amortised, which is over the period to which the forward element relates. For example, if a forward contract hedges the exposure to variability in threemonth interest rates for a three-month period that starts in six months' time, the forward element is amortised during the period that spans months seven to nine.
3. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
4. A contractual cash flow characteristic does not affect the classification of the financial asset if it could have only a de minimis effect on the contractual cash flows of the financial asset. To make this determination, an entity must consider the possible effect of the contractual cash flow characteristic in each reporting period and cumulatively over the life of the financial instrument. In addition, if a contractual cash flow characteristic could have an effect on the contractual cash flows that is more than de minimis (either in a single reporting period or cumulatively) but that cash flow characteristic is not genuine, it does not affect the classification of a financial asset. A cash flow characteristic is not genuine if it affects the instrument's contractual cash flows only on the occurrence of an event that is extremely rare, highly abnormal and very unlikely to occur.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

DIURNAL GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Multiple Regression^1,2,3,4 and conclude that the LON:DNL stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Financial State Forecast for LON:DNL DIURNAL GROUP PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	40	37
Market Risk	65	82
Technical Analysis	70	36
Fundamental Analysis	45	54
Risk Unsystematic	42	86

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 712 signals.

References

1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you know when a stock will go up or down?(STJ Stock Forecast). AC Investment Research Journal, 101(3).
2. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
3. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
4. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
5. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
6. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
7. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014

Frequently Asked Questions

Q: What is the prediction methodology for LON:DNL stock?
A: LON:DNL stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression
Q: Is LON:DNL stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:DNL Stock.
Q: Is DIURNAL GROUP PLC stock a good investment?
A: The consensus rating for DIURNAL GROUP PLC is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:DNL stock?
A: The consensus rating for LON:DNL is Sell.
Q: What is the prediction period for LON:DNL stock?
A: The prediction period for LON:DNL is (n+6 month)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login