Can we predict stock market using machine learning? (GOOG Stock Forecast) (Forecast)

The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized. We evaluate Alphabet (Class C) prediction models with Reinforcement Machine Learning (ML) and ElasticNet Regression^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 Sell GOOG stock.

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

Key Points

1. How do you decide buy or sell a stock?
2. Can stock prices be predicted?
3. How do you decide buy or sell a stock?

GOOG Target Price Prediction Modeling Methodology

Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. We consider Alphabet (Class C) Stock Decision Process with ElasticNet Regression 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(r_i\right)$

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: 12 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell 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 B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with ElasticNet Regression^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 Sell GOOG stock.

Financial State Forecast for GOOG Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba2
Operational Risk	42	88
Market Risk	48	39
Technical Analysis	73	84
Fundamental Analysis	53	59
Risk Unsystematic	39	64

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 729 signals.

References

1. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
2. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
3. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
4. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
5. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
6. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
7. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.

Frequently Asked Questions

Q: What is the prediction methodology for GOOG stock?
A: GOOG stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and ElasticNet Regression
Q: Is GOOG stock a buy or sell?
A: The dominant strategy among neural network is to Sell GOOG Stock.
Q: Is Alphabet (Class C) stock a good investment?
A: The consensus rating for Alphabet (Class C) is Sell and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of GOOG stock?
A: The consensus rating for GOOG is Sell.
Q: What is the prediction period for GOOG stock?
A: The prediction period for GOOG is (n+6 month)

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