Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We evaluate OXFORD CANNABINOID TECHNOLOGIES HOLDINGS PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Statistical Hypothesis Testing1,2,3,4 and conclude that the LON:OCTP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:OCTP stock.

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

## Key Points

2. Stock Rating
3. Can we predict stock market using machine learning?

## LON:OCTP Target Price Prediction Modeling Methodology

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We consider OXFORD CANNABINOID TECHNOLOGIES HOLDINGS PLC Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of LON:OCTP 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(Statistical Hypothesis Testing)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 (Market News Sentiment Analysis)) X S(n):→ (n+4 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:OCTP 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:OCTP Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:OCTP OXFORD CANNABINOID TECHNOLOGIES HOLDINGS PLC
Time series to forecast n: 20 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:OCTP 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

OXFORD CANNABINOID TECHNOLOGIES HOLDINGS PLC assigned short-term Ba1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Statistical Hypothesis Testing1,2,3,4 and conclude that the LON:OCTP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:OCTP stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba1B1
Operational Risk 8554
Market Risk7960
Technical Analysis4460
Fundamental Analysis8073
Risk Unsystematic7248

### Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 867 signals.

## References

1. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
2. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
3. Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
4. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
5. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
6. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
7. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:OCTP stock?
A: LON:OCTP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Statistical Hypothesis Testing
Q: Is LON:OCTP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:OCTP Stock.
Q: Is OXFORD CANNABINOID TECHNOLOGIES HOLDINGS PLC stock a good investment?
A: The consensus rating for OXFORD CANNABINOID TECHNOLOGIES HOLDINGS PLC is Hold and assigned short-term Ba1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:OCTP stock?
A: The consensus rating for LON:OCTP is Hold.
Q: What is the prediction period for LON:OCTP stock?
A: The prediction period for LON:OCTP is (n+4 weeks)