Can stock prices be predicted? (LON:DVO Stock Forecast)

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate DEVRO PLC prediction models with Multi-Task Learning (ML) and Paired T-Test^1,2,3,4 and conclude that the LON:DVO stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:DVO stock.

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

Key Points

1. Short/Long Term Stocks
2. How can neural networks improve predictions?
3. What is prediction in deep learning?

LON:DVO Target Price Prediction Modeling Methodology

This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models. We consider DEVRO PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:DVO 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(Paired T-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(Multi-Task Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:DVO 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:DVO Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:DVO DEVRO PLC
Time series to forecast n: 09 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:DVO 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

DEVRO PLC assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Paired T-Test^1,2,3,4 and conclude that the LON:DVO stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:DVO stock.

Financial State Forecast for LON:DVO Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B2
Operational Risk	50	34
Market Risk	52	38
Technical Analysis	85	30
Fundamental Analysis	54	84
Risk Unsystematic	63	59

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 532 signals.

References

1. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
2. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
4. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
5. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
6. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
7. 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

Frequently Asked Questions

Q: What is the prediction methodology for LON:DVO stock?
A: LON:DVO stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Paired T-Test
Q: Is LON:DVO stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:DVO Stock.
Q: Is DEVRO PLC stock a good investment?
A: The consensus rating for DEVRO PLC is Sell and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:DVO stock?
A: The consensus rating for LON:DVO is Sell.
Q: What is the prediction period for LON:DVO stock?
A: The prediction period for LON:DVO is (n+16 weeks)