Finance is one of the pioneering industries that started using Machine Learning (ML), a subset of Artificial Intelligence (AI) in the early 80s for market prediction. Since then, major firms and hedge funds have adopted machine learning for stock prediction, portfolio optimization, credit lending, stock betting, etc. In this paper, we survey all the different approaches of machine learning that can be incorporated in applied finance.** We evaluate FTSE China A50 Index prediction models with Modular Neural Network (Financial Sentiment Analysis) and Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the FTSE China A50 Index stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold FTSE China A50 Index stock.**

**FTSE China A50 Index, FTSE China A50 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Fundemental Analysis with Algorithmic Trading
- Is Target price a good indicator?
- Trust metric by Neural Network

## FTSE China A50 Index Target Price Prediction Modeling Methodology

The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. We consider FTSE China A50 Index Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of FTSE China A50 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(Wilcoxon Sign-Rank Test)

^{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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of FTSE China A50 Index stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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## FTSE China A50 Index Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**FTSE China A50 Index FTSE China A50 Index

**Time series to forecast n: 12 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold FTSE China A50 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%**

## Conclusions

FTSE China A50 Index assigned short-term Baa2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the FTSE China A50 Index stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold FTSE China A50 Index stock.**

### Financial State Forecast for FTSE China A50 Index Stock Options & Futures

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

Outlook* | Baa2 | Ba3 |

Operational Risk | 71 | 83 |

Market Risk | 85 | 49 |

Technical Analysis | 77 | 78 |

Fundamental Analysis | 69 | 49 |

Risk Unsystematic | 66 | 69 |

### Prediction Confidence Score

## References

- Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221

## Frequently Asked Questions

Q: What is the prediction methodology for FTSE China A50 Index stock?A: FTSE China A50 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Wilcoxon Sign-Rank Test

Q: Is FTSE China A50 Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold FTSE China A50 Index Stock.

Q: Is FTSE China A50 Index stock a good investment?

A: The consensus rating for FTSE China A50 Index is Hold and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of FTSE China A50 Index stock?

A: The consensus rating for FTSE China A50 Index is Hold.

Q: What is the prediction period for FTSE China A50 Index stock?

A: The prediction period for FTSE China A50 Index is (n+8 weeks)

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