Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators.** We evaluate Liberty Formula 1 (Series C) prediction models with Statistical Inference (ML) and Ridge Regression ^{1,2,3,4} and conclude that the FWONK stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold FWONK stock.**

**FWONK, Liberty Formula 1 (Series C), stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Why do we need predictive models?
- Investment Risk
- What are main components of Markov decision process?

## FWONK Target Price Prediction Modeling Methodology

Market systems are so complex that they overwhelm the ability of any individual to predict. But it is crucial for the investors to predict stock market price to generate notable profit. We have taken into factors such as Commodity Prices (crude oil, gold, silver), Market History, and Foreign exchange rate (FEX) that influence the stock trend. We consider Liberty Formula 1 (Series C) Stock Decision Process with Ridge Regression where A is the set of discrete actions of FWONK 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of FWONK 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?

## FWONK Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**FWONK Liberty Formula 1 (Series C)

**Time series to forecast n: 24 Oct 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold FWONK 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

Liberty Formula 1 (Series C) assigned short-term Ba1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Ridge Regression ^{1,2,3,4} and conclude that the FWONK stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold FWONK stock.**

### Financial State Forecast for FWONK Stock Options & Futures

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

Outlook* | Ba1 | B1 |

Operational Risk | 62 | 66 |

Market Risk | 62 | 66 |

Technical Analysis | 73 | 59 |

Fundamental Analysis | 71 | 61 |

Risk Unsystematic | 82 | 43 |

### Prediction Confidence Score

## References

- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.

## Frequently Asked Questions

Q: What is the prediction methodology for FWONK stock?A: FWONK stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Ridge Regression

Q: Is FWONK stock a buy or sell?

A: The dominant strategy among neural network is to Hold FWONK Stock.

Q: Is Liberty Formula 1 (Series C) stock a good investment?

A: The consensus rating for Liberty Formula 1 (Series C) is Hold and assigned short-term Ba1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of FWONK stock?

A: The consensus rating for FWONK is Hold.

Q: What is the prediction period for FWONK stock?

A: The prediction period for FWONK is (n+3 month)