Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We evaluate GETBUSY PLC prediction models with Transductive Learning (ML) and Independent T-Test1,2,3,4 and conclude that the LON:GETB stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:GETB stock.

Keywords: LON:GETB, GETBUSY 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 Markov decision process in reinforcement learning?
2. Probability Distribution
3. Reaction Function

## LON:GETB Target Price Prediction Modeling Methodology

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We consider GETBUSY PLC Stock Decision Process with Independent T-Test where A is the set of discrete actions of LON:GETB 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(Independent T-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(Transductive Learning (ML)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:GETB 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:GETB Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:GETB GETBUSY PLC
Time series to forecast n: 10 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:GETB 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

GETBUSY PLC assigned short-term Ba1 & long-term B2 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Independent T-Test1,2,3,4 and conclude that the LON:GETB stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:GETB stock.

### Financial State Forecast for LON:GETB Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1B2
Operational Risk 8466
Market Risk7041
Technical Analysis6130
Fundamental Analysis5145
Risk Unsystematic9080

### Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 553 signals.

## References

1. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
2. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
3. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
4. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
5. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
6. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
7. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
Frequently Asked QuestionsQ: What is the prediction methodology for LON:GETB stock?
A: LON:GETB stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Independent T-Test
Q: Is LON:GETB stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:GETB Stock.
Q: Is GETBUSY PLC stock a good investment?
A: The consensus rating for GETBUSY PLC is Hold and assigned short-term Ba1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:GETB stock?
A: The consensus rating for LON:GETB is Hold.
Q: What is the prediction period for LON:GETB stock?
A: The prediction period for LON:GETB is (n+8 weeks)