Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate KEYWORDS STUDIOS PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Multiple Regression1,2,3,4 and conclude that the LON:KWS stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:KWS stock.

Keywords: LON:KWS, KEYWORDS STUDIOS 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. Game Theory
3. What are the most successful trading algorithms?

## LON:KWS Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider KEYWORDS STUDIOS PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:KWS 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(Multiple 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 (Market Volatility Analysis)) X S(n):→ (n+1 year) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of LON:KWS 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:KWS Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:KWS KEYWORDS STUDIOS PLC
Time series to forecast n: 13 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:KWS 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

KEYWORDS STUDIOS PLC assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Multiple Regression1,2,3,4 and conclude that the LON:KWS stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:KWS stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Operational Risk 8665
Market Risk3559
Technical Analysis7289
Fundamental Analysis3433
Risk Unsystematic3264

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 733 signals.

## References

1. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
2. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
3. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
4. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
5. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
6. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
7. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:KWS stock?
A: LON:KWS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Multiple Regression
Q: Is LON:KWS stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:KWS Stock.
Q: Is KEYWORDS STUDIOS PLC stock a good investment?
A: The consensus rating for KEYWORDS STUDIOS PLC is Sell and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:KWS stock?
A: The consensus rating for LON:KWS is Sell.
Q: What is the prediction period for LON:KWS stock?
A: The prediction period for LON:KWS is (n+1 year)