Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods** We evaluate Progyny prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the PGNY stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PGNY stock.**

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

*Keywords:*## Key Points

- What is the best way to predict stock prices?
- Trading Signals
- Nash Equilibria

## PGNY Target Price Prediction Modeling Methodology

Finance is one of the pioneering industries that started using Machine Learning (ML), a subset of Artificial Intelligence (AI) in the early 80s for market prediction. Since then, major firms and hedge funds have adopted machine learning for stock prediction, portfolio optimization, credit lending, stock betting, etc. In this paper, we survey all the different approaches of machine learning that can be incorporated in applied finance. We consider Progyny Stock Decision Process with Lasso Regression where A is the set of discrete actions of PGNY 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(Lasso 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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of PGNY 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?

## PGNY Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**PGNY Progyny

**Time series to forecast n: 15 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PGNY 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

Progyny assigned short-term B2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the PGNY stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PGNY stock.**

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

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

Outlook* | B2 | Ba1 |

Operational Risk | 40 | 77 |

Market Risk | 63 | 72 |

Technical Analysis | 73 | 82 |

Fundamental Analysis | 40 | 43 |

Risk Unsystematic | 44 | 86 |

### Prediction Confidence Score

## References

- 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.
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]

## Frequently Asked Questions

Q: What is the prediction methodology for PGNY stock?A: PGNY stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Lasso Regression

Q: Is PGNY stock a buy or sell?

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

Q: Is Progyny stock a good investment?

A: The consensus rating for Progyny is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of PGNY stock?

A: The consensus rating for PGNY is Hold.

Q: What is the prediction period for PGNY stock?

A: The prediction period for PGNY is (n+16 weeks)

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