This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks.** We evaluate HONG KONG LAND HOLDINGS LD prediction models with Transductive Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the LON:HKLD 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:HKLD stock.**

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

*Keywords:*## Key Points

- How useful are statistical predictions?
- Game Theory
- Is now good time to invest?

## LON:HKLD Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider HONG KONG LAND HOLDINGS LD Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:HKLD 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Lasso Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transductive Learning (ML)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:HKLD HONG KONG LAND HOLDINGS LD

**Time series to forecast n: 13 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:HKLD 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 B2 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the LON:HKLD 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:HKLD stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B2 |

Operational Risk | 78 | 33 |

Market Risk | 34 | 82 |

Technical Analysis | 90 | 33 |

Fundamental Analysis | 40 | 39 |

Risk Unsystematic | 38 | 78 |

### Prediction Confidence Score

## References

- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R MÃ¼ller, pp. 421–36. Berlin: Springer
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
- S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]

## Frequently Asked Questions

Q: What is the prediction methodology for LON:HKLD stock?A: LON:HKLD stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Lasso Regression

Q: Is LON:HKLD stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:HKLD 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 B2 forecasted stock rating.

Q: What is the consensus rating of LON:HKLD stock?

A: The consensus rating for LON:HKLD is Hold.

Q: What is the prediction period for LON:HKLD stock?

A: The prediction period for LON:HKLD is (n+6 month)