We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We evaluate HARVEST MINERALS LIMITED prediction models with Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the LON:HMI 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:HMI stock.

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

## Key Points

1. Is Target price a good indicator?
2. Stock Rating
3. Stock Rating ## LON:HMI Target Price Prediction Modeling Methodology

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. We consider HARVEST MINERALS LIMITED Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:HMI 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 (Financial 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:HMI 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:HMI Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:HMI HARVEST MINERALS LIMITED
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:HMI 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

HARVEST MINERALS LIMITED assigned short-term Ba3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the LON:HMI 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:HMI stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba3Baa2
Operational Risk 4849
Market Risk3480
Technical Analysis8883
Fundamental Analysis8277
Risk Unsystematic7179

### Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 559 signals.

## References

1. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
2. 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
3. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
4. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
5. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
6. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
7. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HMI stock?
A: LON:HMI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression
Q: Is LON:HMI stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:HMI Stock.
Q: Is HARVEST MINERALS LIMITED stock a good investment?
A: The consensus rating for HARVEST MINERALS LIMITED is Sell and assigned short-term Ba3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:HMI stock?
A: The consensus rating for LON:HMI is Sell.
Q: What is the prediction period for LON:HMI stock?
A: The prediction period for LON:HMI is (n+1 year)