LON:BIG Target Price Prediction

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. We evaluate BIG TECHNOLOGIES PLC prediction models with Transfer Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:BIG stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:BIG stock.

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

Key Points

1. What is prediction model?
2. Technical Analysis with Algorithmic Trading
3. How useful are statistical predictions?

LON:BIG Target Price Prediction Modeling Methodology

In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. We consider BIG TECHNOLOGIES PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:BIG 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:BIG 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:BIG Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:BIG BIG TECHNOLOGIES PLC
Time series to forecast n: 06 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:BIG 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

BIG TECHNOLOGIES PLC assigned short-term Ba2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:BIG stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:BIG stock.

Financial State Forecast for LON:BIG Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	Ba2
Operational Risk	53	48
Market Risk	79	60
Technical Analysis	51	68
Fundamental Analysis	81	78
Risk Unsystematic	83	80

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 853 signals.

References

1. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
2. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
3. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
4. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
5. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
6. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
7. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008

Frequently Asked Questions

Q: What is the prediction methodology for LON:BIG stock?
A: LON:BIG stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:BIG stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:BIG Stock.
Q: Is BIG TECHNOLOGIES PLC stock a good investment?
A: The consensus rating for BIG TECHNOLOGIES PLC is Buy and assigned short-term Ba2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of LON:BIG stock?
A: The consensus rating for LON:BIG is Buy.
Q: What is the prediction period for LON:BIG stock?
A: The prediction period for LON:BIG is (n+6 month)

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