Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. We evaluate ASOS PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Linear Regression1,2,3,4 and conclude that the LON:ASC 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:ASC stock.

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

## Key Points

1. Market Risk
2. Market Risk
3. How accurate is machine learning in stock market? ## LON:ASC Target Price Prediction Modeling Methodology

Stock market predictions are one of the challenging tasks for financial investors across the globe. This challenge is due to the uncertainty and volatility of the stock prices in the market. Due to technology and globalization of business and financial markets it is important to predict the stock prices more quickly and accurately. Last few years there has been much improvement in the field of Neural Network (NN) applications in business and financial markets. Artificial Neural Network (ANN) methods are mostly implemented and play a vital role in decision making for stock market predictions. We consider ASOS PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:ASC 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(Linear 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(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:ASC ASOS PLC
Time series to forecast n: 10 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:ASC 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

ASOS PLC assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Linear Regression1,2,3,4 and conclude that the LON:ASC 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:ASC stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B2
Operational Risk 5846
Market Risk5443
Technical Analysis3954
Fundamental Analysis5148
Risk Unsystematic3469

### Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 876 signals.

## References

1. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
2. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
3. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
4. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
5. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
6. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
7. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
Frequently Asked QuestionsQ: What is the prediction methodology for LON:ASC stock?
A: LON:ASC stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Linear Regression
Q: Is LON:ASC stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:ASC Stock.
Q: Is ASOS PLC stock a good investment?
A: The consensus rating for ASOS PLC is Hold and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:ASC stock?
A: The consensus rating for LON:ASC is Hold.
Q: What is the prediction period for LON:ASC stock?
A: The prediction period for LON:ASC is (n+4 weeks)