In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. We evaluate Guardant prediction models with Deductive Inference (ML) and Spearman Correlation1,2,3,4 and conclude that the GH stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy GH stock.

Keywords: GH, Guardant, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Nash Equilibria
2. Buy, Sell and Hold Signals
3. How useful are statistical predictions?

## GH Target Price Prediction Modeling Methodology

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We consider Guardant Stock Decision Process with Spearman Correlation where A is the set of discrete actions of GH 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(Spearman Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of GH 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?

## GH Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: GH Guardant
Time series to forecast n: 16 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy GH 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

Guardant assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Spearman Correlation1,2,3,4 and conclude that the GH stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy GH stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 3266
Market Risk5547
Technical Analysis7570
Fundamental Analysis6843
Risk Unsystematic3546

### Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 789 signals.

## References

1. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
2. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
3. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
4. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
5. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
6. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
7. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
Frequently Asked QuestionsQ: What is the prediction methodology for GH stock?
A: GH stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Spearman Correlation
Q: Is GH stock a buy or sell?
A: The dominant strategy among neural network is to Buy GH Stock.
Q: Is Guardant stock a good investment?
A: The consensus rating for Guardant is Buy and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of GH stock?
A: The consensus rating for GH is Buy.
Q: What is the prediction period for GH stock?
A: The prediction period for GH is (n+1 year)