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 evaluate AFARAK GROUP PLC prediction models with Transductive Learning (ML) and Ridge Regression1,2,3,4 and conclude that the LON:AFRK stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:AFRK stock.

Keywords: LON:AFRK, AFARAK 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 statistics predict the future?
2. What is statistical models in machine learning?
3. What is neural prediction? ## LON:AFRK Target Price Prediction Modeling Methodology

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. We consider AFARAK GROUP PLC Stock Decision Process with Ridge Regression where A is the set of discrete actions of LON:AFRK 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(Ridge Regression)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+4 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:AFRK 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:AFRK Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:AFRK AFARAK GROUP PLC
Time series to forecast n: 24 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:AFRK 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

AFARAK GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Ridge Regression1,2,3,4 and conclude that the LON:AFRK stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:AFRK stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 5545
Market Risk7172
Technical Analysis7289
Fundamental Analysis3339
Risk Unsystematic4141

### Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 826 signals.

## References

1. C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
2. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
3. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
4. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
5. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
6. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
7. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
Frequently Asked QuestionsQ: What is the prediction methodology for LON:AFRK stock?
A: LON:AFRK stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Ridge Regression
Q: Is LON:AFRK stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:AFRK Stock.
Q: Is AFARAK GROUP PLC stock a good investment?
A: The consensus rating for AFARAK GROUP PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:AFRK stock?
A: The consensus rating for LON:AFRK is Hold.
Q: What is the prediction period for LON:AFRK stock?
A: The prediction period for LON:AFRK is (n+4 weeks)