How accurate is machine learning in stock market? (LON:WISE Stock Forecast) (Forecast)

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. We evaluate WISE PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Beta^1,2,3,4 and conclude that the LON:WISE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:WISE stock.

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

Key Points

1. Which neural network is best for prediction?
2. Market Risk
3. Nash Equilibria

LON:WISE Target Price Prediction Modeling Methodology

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS. We consider WISE PLC Stock Decision Process with Beta where A is the set of discrete actions of LON:WISE 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(Beta)^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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:WISE 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:WISE Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:WISE WISE PLC
Time series to forecast n: 19 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:WISE 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

WISE PLC assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Beta^1,2,3,4 and conclude that the LON:WISE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:WISE stock.

Financial State Forecast for LON:WISE Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba3
Operational Risk	81	61
Market Risk	75	78
Technical Analysis	41	45
Fundamental Analysis	77	82
Risk Unsystematic	81	56

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 666 signals.

References

1. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
2. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
3. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
4. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
5. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
6. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
7. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85

Frequently Asked Questions

Q: What is the prediction methodology for LON:WISE stock?
A: LON:WISE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Beta
Q: Is LON:WISE stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:WISE Stock.
Q: Is WISE PLC stock a good investment?
A: The consensus rating for WISE PLC is Hold and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:WISE stock?
A: The consensus rating for LON:WISE is Hold.
Q: What is the prediction period for LON:WISE stock?
A: The prediction period for LON:WISE is (n+6 month)

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