Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network.** We evaluate General Motors prediction models with Active Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the GM 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 GM stock.**

**GM, General Motors, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Which neural network is best for prediction?
- How accurate is machine learning in stock market?
- Buy, Sell and Hold Signals

## GM 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 General Motors Stock Decision Process with Logistic Regression where A is the set of discrete actions of GM 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(Logistic 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(Active Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of GM 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?

## GM Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**GM General Motors

**Time series to forecast n: 05 Oct 2022**for (n+8 weeks)

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

General Motors assigned short-term B2 & long-term Caa1 forecasted stock rating.** We evaluate the prediction models Active Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the GM 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 GM stock.**

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

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

Outlook* | B2 | Caa1 |

Operational Risk | 72 | 45 |

Market Risk | 47 | 35 |

Technical Analysis | 81 | 36 |

Fundamental Analysis | 40 | 35 |

Risk Unsystematic | 49 | 31 |

### Prediction Confidence Score

## References

- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]

## Frequently Asked Questions

Q: What is the prediction methodology for GM stock?A: GM stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Logistic Regression

Q: Is GM stock a buy or sell?

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

Q: Is General Motors stock a good investment?

A: The consensus rating for General Motors is Hold and assigned short-term B2 & long-term Caa1 forecasted stock rating.

Q: What is the consensus rating of GM stock?

A: The consensus rating for GM is Hold.

Q: What is the prediction period for GM stock?

A: The prediction period for GM is (n+8 weeks)

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