Can we predict stock market using machine learning? (LON:PVN Stock Forecast)

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We evaluate PROVEN VCT PLC prediction models with Active Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the LON:PVN 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 Hold LON:PVN stock.

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

Key Points

1. Is now good time to invest?
2. How can neural networks improve predictions?
3. What is the best way to predict stock prices?

LON:PVN Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider PROVEN VCT PLC Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:PVN 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(Wilcoxon Sign-Rank 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(Active Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:PVN PROVEN VCT PLC
Time series to forecast n: 25 Sep 2022 for (n+3 month)

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

PROVEN VCT PLC assigned short-term Ba2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the LON:PVN 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 Hold LON:PVN stock.

Financial State Forecast for LON:PVN Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	Ba1
Operational Risk	80	75
Market Risk	49	30
Technical Analysis	41	81
Fundamental Analysis	89	87
Risk Unsystematic	79	84

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 463 signals.

References

1. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
2. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
3. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
4. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
5. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
6. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
7. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98

Frequently Asked Questions

Q: What is the prediction methodology for LON:PVN stock?
A: LON:PVN stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is LON:PVN stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:PVN Stock.
Q: Is PROVEN VCT PLC stock a good investment?
A: The consensus rating for PROVEN VCT PLC is Hold and assigned short-term Ba2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:PVN stock?
A: The consensus rating for LON:PVN is Hold.
Q: What is the prediction period for LON:PVN stock?
A: The prediction period for LON:PVN is (n+3 month)

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