Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods We evaluate HONG KONG LAND HOLDINGS LD prediction models with Multi-Task Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and conclude that the LON:HKLJ stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:HKLJ stock.

Keywords: LON:HKLJ, HONG KONG LAND HOLDINGS LD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Market Outlook
2. Is Target price a good indicator?
3. How do you know when a stock will go up or down?

## LON:HKLJ Target Price Prediction Modeling Methodology

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We consider HONG KONG LAND HOLDINGS LD Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of LON:HKLJ 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(Multi-Task Learning (ML)) X S(n):→ (n+6 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of LON:HKLJ 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:HKLJ Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:HKLJ HONG KONG LAND HOLDINGS LD
Time series to forecast n: 12 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:HKLJ 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

HONG KONG LAND HOLDINGS LD assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Statistical Hypothesis Testing1,2,3,4 and conclude that the LON:HKLJ stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:HKLJ stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 4032
Market Risk6150
Technical Analysis4175
Fundamental Analysis8969
Risk Unsystematic3876

### Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 648 signals.

## References

1. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
2. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
3. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
4. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
5. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
6. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
7. 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.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HKLJ stock?
A: LON:HKLJ stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Statistical Hypothesis Testing
Q: Is LON:HKLJ stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HKLJ Stock.
Q: Is HONG KONG LAND HOLDINGS LD stock a good investment?
A: The consensus rating for HONG KONG LAND HOLDINGS LD is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:HKLJ stock?
A: The consensus rating for LON:HKLJ is Hold.
Q: What is the prediction period for LON:HKLJ stock?
A: The prediction period for LON:HKLJ is (n+6 month)