Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We evaluate BALTIC CLASSIFIEDS GROUP PLC prediction models with Modular Neural Network (CNN Layer) and Linear Regression1,2,3,4 and conclude that the LON:BCG 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 Buy LON:BCG stock.

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

## Key Points

1. What are the most successful trading algorithms?
2. What statistical methods are used to analyze data?
3. Understanding Buy, Sell, and Hold Ratings ## LON:BCG Target Price Prediction Modeling Methodology

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We consider BALTIC CLASSIFIEDS GROUP PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:BCG 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(Linear 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(Modular Neural Network (CNN Layer)) X S(n):→ (n+3 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:BCG BALTIC CLASSIFIEDS GROUP PLC
Time series to forecast n: 18 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:BCG 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

BALTIC CLASSIFIEDS GROUP PLC assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Linear Regression1,2,3,4 and conclude that the LON:BCG 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 Buy LON:BCG stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Operational Risk 3332
Market Risk6775
Technical Analysis4787
Fundamental Analysis8748
Risk Unsystematic5578

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 819 signals.

## References

1. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
2. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
3. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
4. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
5. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
6. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
7. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:BCG stock?
A: LON:BCG stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Linear Regression
Q: Is LON:BCG stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:BCG Stock.
Q: Is BALTIC CLASSIFIEDS GROUP PLC stock a good investment?
A: The consensus rating for BALTIC CLASSIFIEDS GROUP PLC is Buy and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:BCG stock?
A: The consensus rating for LON:BCG is Buy.
Q: What is the prediction period for LON:BCG stock?
A: The prediction period for LON:BCG is (n+3 month)