LOGISTICS DEVELOPMENT GROUP PLC Stock Forecast, Price & Rating (LON:LDG)


Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We evaluate LOGISTICS DEVELOPMENT GROUP PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and ElasticNet Regression1,2,3,4 and conclude that the LON:LDG stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:LDG stock.


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

Key Points

  1. Can machine learning predict?
  2. Fundemental Analysis with Algorithmic Trading
  3. Can statistics predict the future?

LON:LDG Target Price Prediction Modeling Methodology

Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We consider LOGISTICS DEVELOPMENT GROUP PLC Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:LDG 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(ElasticNet Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of LON:LDG 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:LDG Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:LDG LOGISTICS DEVELOPMENT GROUP PLC
Time series to forecast n: 21 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:LDG 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

LOGISTICS DEVELOPMENT GROUP PLC assigned short-term Ba1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with ElasticNet Regression1,2,3,4 and conclude that the LON:LDG stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:LDG stock.

Financial State Forecast for LON:LDG Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1B1
Operational Risk 9064
Market Risk6587
Technical Analysis8455
Fundamental Analysis5137
Risk Unsystematic6658

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 642 signals.

References

  1. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
  2. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
  3. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
  4. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  5. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
  6. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
  7. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:LDG stock?
A: LON:LDG stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and ElasticNet Regression
Q: Is LON:LDG stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:LDG Stock.
Q: Is LOGISTICS DEVELOPMENT GROUP PLC stock a good investment?
A: The consensus rating for LOGISTICS DEVELOPMENT GROUP PLC is Hold and assigned short-term Ba1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:LDG stock?
A: The consensus rating for LON:LDG is Hold.
Q: What is the prediction period for LON:LDG stock?
A: The prediction period for LON:LDG is (n+3 month)

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