## Summary

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend.** We evaluate Shanghai Composite Index prediction models with Transductive Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the Shanghai Composite Index stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to SellHold Shanghai Composite Index stock.**

## Key Points

- What is neural prediction?
- Which neural network is best for prediction?
- Can stock prices be predicted?

## Shanghai Composite Index Target Price Prediction Modeling Methodology

We consider Shanghai Composite Index Stock Decision Process with Transductive Learning (ML) where A is the set of discrete actions of Shanghai Composite 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(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+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of Shanghai Composite Index stock

j:Nash equilibria (Neural Network)

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?

## Shanghai Composite Index Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**Shanghai Composite Index Shanghai Composite Index

**Time series to forecast n: 24 Nov 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to SellHold Shanghai Composite 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 Shanghai Composite Index

- An entity shall apply the amendments to IFRS 9 made by IFRS 17 as amended in June 2020 retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.37–7.2.42.
- The rebuttable presumption in paragraph 5.5.11 is not an absolute indicator that lifetime expected credit losses should be recognised, but is presumed to be the latest point at which lifetime expected credit losses should be recognised even when using forward-looking information (including macroeconomic factors on a portfolio level).
- If an entity has applied paragraph 7.2.6 then at the date of initial application the entity shall recognise any difference between the fair value of the entire hybrid contract at the date of initial application and the sum of the fair values of the components of the hybrid contract at the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.
- An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.

*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

Shanghai Composite Index assigned short-term B3 & 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 Shanghai Composite Index stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to SellHold Shanghai Composite Index stock.**

### Financial State Forecast for Shanghai Composite Index Shanghai Composite Index Stock Options & Futures

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

Outlook* | B3 | B2 |

Operational Risk | 40 | 35 |

Market Risk | 44 | 81 |

Technical Analysis | 69 | 69 |

Fundamental Analysis | 30 | 35 |

Risk Unsystematic | 50 | 38 |

### Prediction Confidence Score

## References

- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276

## Frequently Asked Questions

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

Q: Is Shanghai Composite Index stock a buy or sell?

A: The dominant strategy among neural network is to SellHold Shanghai Composite Index Stock.

Q: Is Shanghai Composite Index stock a good investment?

A: The consensus rating for Shanghai Composite Index is SellHold and assigned short-term B3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of Shanghai Composite Index stock?

A: The consensus rating for Shanghai Composite Index is SellHold.

Q: What is the prediction period for Shanghai Composite Index stock?

A: The prediction period for Shanghai Composite Index is (n+16 weeks)