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 Citigroup prediction models with Modular Neural Network (Market Volatility Analysis) and Stepwise Regression1,2,3,4 and conclude that the C 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 Buy C stock.

Keywords: C, Citigroup, 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 pick a stock?
2. How accurate is machine learning in stock market?
3. Why do we need predictive models?

## C 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 Citigroup Stock Decision Process with Stepwise Regression where A is the set of discrete actions of C 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(Stepwise 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 (Market Volatility Analysis)) X S(n):→ (n+16 weeks) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of C 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?

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

Sample Set: Neural Network
Stock/Index: C Citigroup
Time series to forecast n: 19 Oct 2022 for (n+16 weeks)

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

Citigroup assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Stepwise Regression1,2,3,4 and conclude that the C 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 Buy C stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba3
Operational Risk 8540
Market Risk7653
Technical Analysis7384
Fundamental Analysis6472
Risk Unsystematic6073

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 513 signals.

## References

1. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
2. 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.
3. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
4. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
5. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
6. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
7. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for C stock?
A: C stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Stepwise Regression
Q: Is C stock a buy or sell?
A: The dominant strategy among neural network is to Buy C Stock.
Q: Is Citigroup stock a good investment?
A: The consensus rating for Citigroup is Buy and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of C stock?
A: The consensus rating for C is Buy.
Q: What is the prediction period for C stock?
A: The prediction period for C is (n+16 weeks)