Machine Learning stock prediction: LON:53GW Stock Prediction

The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We evaluate FULLER, SMITH & TURNER PLC prediction models with Modular Neural Network (Market Direction Analysis) and Lasso Regression^1,2,3,4 and conclude that the LON:53GW 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 Sell LON:53GW stock.

Keywords: LON:53GW, FULLER, SMITH & TURNER PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Can neural networks predict stock market?
2. Should I buy stocks now or wait amid such uncertainty?
3. Is it better to buy and sell or hold?

LON:53GW Target Price Prediction Modeling Methodology

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We consider FULLER, SMITH & TURNER PLC Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:53GW 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(Lasso 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 (Market Direction Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:53GW 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:53GW Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:53GW FULLER, SMITH & TURNER PLC
Time series to forecast n: 29 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:53GW 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%

Adjusted IFRS* Prediction Methods for FULLER, SMITH & TURNER PLC

1. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
2. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.
3. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items
4. If, at the date of initial application, it is impracticable (as defined in IAS 8) for an entity to assess whether the fair value of a prepayment feature was insignificant in accordance with paragraph B4.1.12(c) on the basis of the facts and circumstances that existed at the initial recognition of the financial asset, an entity shall assess the contractual cash flow characteristics of that financial asset on the basis of the facts and circumstances that existed at the initial recognition of the financial asset without taking into account the exception for prepayment features in paragraph B4.1.12. (See also paragraph 42S of IFRS 7.)

*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

FULLER, SMITH & TURNER PLC assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Lasso Regression^1,2,3,4 and conclude that the LON:53GW 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 Sell LON:53GW stock.

Financial State Forecast for LON:53GW FULLER, SMITH & TURNER PLC Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	B2
Operational Risk	79	54
Market Risk	90	32
Technical Analysis	86	36
Fundamental Analysis	59	73
Risk Unsystematic	58	77

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 683 signals.

References

1. 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.
2. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
3. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
4. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
5. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
6. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
7. 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

Frequently Asked Questions

Q: What is the prediction methodology for LON:53GW stock?
A: LON:53GW stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Lasso Regression
Q: Is LON:53GW stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:53GW Stock.
Q: Is FULLER, SMITH & TURNER PLC stock a good investment?
A: The consensus rating for FULLER, SMITH & TURNER PLC is Sell and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:53GW stock?
A: The consensus rating for LON:53GW is Sell.
Q: What is the prediction period for LON:53GW stock?
A: The prediction period for LON:53GW is (n+1 year)

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