Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We evaluate IBSTOCK PLC prediction models with Deductive Inference (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:IBST 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:IBST stock.

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

Key Points

1. How can neural networks improve predictions?
2. Technical Analysis with Algorithmic Trading
3. Stock Rating

LON:IBST Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider IBSTOCK PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:IBST 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:IBST IBSTOCK PLC
Time series to forecast n: 10 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:IBST 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

IBSTOCK PLC assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:IBST 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:IBST stock.

Financial State Forecast for LON:IBST Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B1
Operational Risk 5772
Market Risk4935
Technical Analysis5552
Fundamental Analysis3146
Risk Unsystematic6483

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 865 signals.

References

1. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
2. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
3. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
4. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
5. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
6. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
7. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:IBST stock?
A: LON:IBST stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:IBST stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:IBST Stock.
Q: Is IBSTOCK PLC stock a good investment?
A: The consensus rating for IBSTOCK PLC is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:IBST stock?
A: The consensus rating for LON:IBST is Hold.
Q: What is the prediction period for LON:IBST stock?
A: The prediction period for LON:IBST is (n+3 month)