Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. We evaluate ARGO GROUP LIMITED prediction models with Modular Neural Network (Social Media Sentiment Analysis) and ElasticNet Regression1,2,3,4 and conclude that the LON:ARGO 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 Hold LON:ARGO stock.

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

## Key Points

1. Probability Distribution
2. How useful are statistical predictions?
3. Can neural networks predict stock market?

## LON:ARGO Target Price Prediction Modeling Methodology

This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms. We consider ARGO GROUP LIMITED Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:ARGO 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(ElasticNet 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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:ARGO ARGO GROUP LIMITED
Time series to forecast n: 24 Sep 2022 for (n+1 year)

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

ARGO GROUP LIMITED assigned short-term Ba2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with ElasticNet Regression1,2,3,4 and conclude that the LON:ARGO 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 Hold LON:ARGO stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba2Baa2
Operational Risk 8385
Market Risk4862
Technical Analysis7588
Fundamental Analysis8570
Risk Unsystematic4890

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 476 signals.

## References

1. 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
2. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
3. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
4. Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
5. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
6. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
7. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
Frequently Asked QuestionsQ: What is the prediction methodology for LON:ARGO stock?
A: LON:ARGO stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and ElasticNet Regression
Q: Is LON:ARGO stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:ARGO Stock.
Q: Is ARGO GROUP LIMITED stock a good investment?
A: The consensus rating for ARGO GROUP LIMITED is Hold and assigned short-term Ba2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:ARGO stock?
A: The consensus rating for LON:ARGO is Hold.
Q: What is the prediction period for LON:ARGO stock?
A: The prediction period for LON:ARGO is (n+1 year)

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