This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. We evaluate Catalent prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Logistic Regression1,2,3,4 and conclude that the CTLT 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 Strong Sell CTLT stock.

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

## Key Points

1. How can neural networks improve predictions?
2. What statistical methods are used to analyze data?
3. Game Theory ## CTLT Target Price Prediction Modeling Methodology

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We consider Catalent Stock Decision Process with Logistic Regression where A is the set of discrete actions of CTLT 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of CTLT stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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

Sample Set: Neural Network
Stock/Index: CTLT Catalent
Time series to forecast n: 18 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Strong Sell CTLT 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

Catalent assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Logistic Regression1,2,3,4 and conclude that the CTLT 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 Strong Sell CTLT stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 4133
Market Risk8082
Technical Analysis7890
Fundamental Analysis5832
Risk Unsystematic4437

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 736 signals.

## References

1. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
2. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
3. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
4. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
5. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
6. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
7. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
Frequently Asked QuestionsQ: What is the prediction methodology for CTLT stock?
A: CTLT stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Logistic Regression
Q: Is CTLT stock a buy or sell?
A: The dominant strategy among neural network is to Strong Sell CTLT Stock.
Q: Is Catalent stock a good investment?
A: The consensus rating for Catalent is Strong Sell and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of CTLT stock?
A: The consensus rating for CTLT is Strong Sell.
Q: What is the prediction period for CTLT stock?
A: The prediction period for CTLT is (n+16 weeks)

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