How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. We evaluate SOPHEON PLC prediction models with Statistical Inference (ML) and Logistic Regression1,2,3,4 and conclude that the LON:SPE 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:SPE stock.

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

## Key Points

1. Is Target price a good indicator?
2. Is it better to buy and sell or hold?

## LON:SPE Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider SOPHEON PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:SPE 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(Logistic 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(Statistical Inference (ML)) X S(n):→ (n+8 weeks) $∑ i = 1 n r i$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:SPE SOPHEON PLC
Time series to forecast n: 07 Oct 2022 for (n+8 weeks)

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

SOPHEON PLC assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Logistic Regression1,2,3,4 and conclude that the LON:SPE 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:SPE stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B1
Operational Risk 4074
Market Risk4442
Technical Analysis4672
Fundamental Analysis3743
Risk Unsystematic8046

### Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 683 signals.

## References

1. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
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. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
4. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
5. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
6. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
7. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
Frequently Asked QuestionsQ: What is the prediction methodology for LON:SPE stock?
A: LON:SPE stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Logistic Regression
Q: Is LON:SPE stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SPE Stock.
Q: Is SOPHEON PLC stock a good investment?
A: The consensus rating for SOPHEON PLC is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:SPE stock?
A: The consensus rating for LON:SPE is Hold.
Q: What is the prediction period for LON:SPE stock?
A: The prediction period for LON:SPE is (n+8 weeks)

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