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 evaluate CACI International prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Multiple Regression1,2,3,4 and conclude that the CACI stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy CACI stock.

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

## Key Points

1. What is neural prediction?
2. Dominated Move
3. Understanding Buy, Sell, and Hold Ratings ## CACI Target Price Prediction Modeling Methodology

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We consider CACI International Stock Decision Process with Multiple Regression where A is the set of discrete actions of CACI 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(Multiple 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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 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 CACI 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?

## CACI Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: CACI CACI International
Time series to forecast n: 24 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy CACI 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

CACI International assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Multiple Regression1,2,3,4 and conclude that the CACI stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy CACI stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B1
Operational Risk 5084
Market Risk4276
Technical Analysis4353
Fundamental Analysis3943
Risk Unsystematic6939

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 621 signals.

## References

1. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
2. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
3. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
4. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
5. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
6. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
7. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
Frequently Asked QuestionsQ: What is the prediction methodology for CACI stock?
A: CACI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Multiple Regression
Q: Is CACI stock a buy or sell?
A: The dominant strategy among neural network is to Buy CACI Stock.
Q: Is CACI International stock a good investment?
A: The consensus rating for CACI International is Buy and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of CACI stock?
A: The consensus rating for CACI is Buy.
Q: What is the prediction period for CACI stock?
A: The prediction period for CACI is (n+8 weeks)