The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We evaluate Hang Seng Index prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression1,2,3,4 and conclude that the Hang Seng Index 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 Hang Seng Index stock.

Keywords: Hang Seng Index, Hang Seng Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Market Signals
2. How accurate is machine learning in stock market?

## Hang Seng Index Target Price Prediction Modeling Methodology

Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We consider Hang Seng Index Stock Decision Process with Ridge Regression where A is the set of discrete actions of Hang Seng Index 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(Ridge 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) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of Hang Seng Index 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?

## Hang Seng Index Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: Hang Seng Index Hang Seng Index
Time series to forecast n: 15 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell Hang Seng Index 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%

## Adjusted IFRS* Prediction Methods for Hang Seng Index

1. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
2. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
3. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
4. Contractual cash flows that are solely payments of principal and interest on the principal amount outstanding are consistent with a basic lending arrangement. In a basic lending arrangement, consideration for the time value of money (see paragraphs B4.1.9A–B4.1.9E) and credit risk are typically the most significant elements of interest. However, in such an arrangement, interest can also include consideration for other basic lending risks (for example, liquidity risk) and costs (for example, administrative costs) associated with holding the financial asset for a particular period of time. In addition, interest can include a profit margin that is consistent with a basic lending arrangement. In extreme economic circumstances, interest can be negative if, for example, the holder of a financial asset either explicitly or implicitly pays for the deposit of its money for a particular period of time (and that fee exceeds the consideration that the holder receives for the time value of money, credit risk and other basic lending risks and costs).

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

Hang Seng Index assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Ridge Regression1,2,3,4 and conclude that the Hang Seng Index 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 Hang Seng Index stock.

### Financial State Forecast for Hang Seng Index Hang Seng Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Operational Risk 7354
Market Risk6032
Technical Analysis4690
Fundamental Analysis8977
Risk Unsystematic5377

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 711 signals.

## References

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3. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
4. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
5. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
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7. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked QuestionsQ: What is the prediction methodology for Hang Seng Index stock?
A: Hang Seng Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression
Q: Is Hang Seng Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell Hang Seng Index Stock.
Q: Is Hang Seng Index stock a good investment?
A: The consensus rating for Hang Seng Index is Sell and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of Hang Seng Index stock?
A: The consensus rating for Hang Seng Index is Sell.
Q: What is the prediction period for Hang Seng Index stock?
A: The prediction period for Hang Seng Index is (n+1 year)