Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. ** We evaluate Alphabet (Class C) prediction models with Transfer Learning (ML) and Pearson Correlation ^{1,2,3,4} and conclude that the GOOG stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy GOOG stock.**

**GOOG, Alphabet (Class C), stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trading Signals
- What are the most successful trading algorithms?
- Trading Interaction

## GOOG Target Price Prediction Modeling Methodology

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. We consider Alphabet (Class C) Stock Decision Process with Pearson Correlation where A is the set of discrete actions of GOOG 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(Pearson Correlation)

^{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(Transfer Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of GOOG 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?

## GOOG Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**GOOG Alphabet (Class C)

**Time series to forecast n: 20 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy GOOG 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

Alphabet (Class C) assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Pearson Correlation ^{1,2,3,4} and conclude that the GOOG stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy GOOG stock.**

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

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 75 | 57 |

Market Risk | 68 | 61 |

Technical Analysis | 47 | 82 |

Fundamental Analysis | 66 | 66 |

Risk Unsystematic | 73 | 53 |

### Prediction Confidence Score

## References

- A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
- Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
- Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
- M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016

## Frequently Asked Questions

Q: What is the prediction methodology for GOOG stock?A: GOOG stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Pearson Correlation

Q: Is GOOG stock a buy or sell?

A: The dominant strategy among neural network is to Buy GOOG Stock.

Q: Is Alphabet (Class C) stock a good investment?

A: The consensus rating for Alphabet (Class C) is Buy and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of GOOG stock?

A: The consensus rating for GOOG is Buy.

Q: What is the prediction period for GOOG stock?

A: The prediction period for GOOG is (n+6 month)

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