Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets.** We evaluate CSX Corporation prediction models with Statistical Inference (ML) and Lasso Regression ^{1,2,3,4} and conclude that the CSX stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell CSX stock.**

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

*Keywords:*## Key Points

- Can we predict stock market using machine learning?
- What is neural prediction?
- Decision Making

## CSX Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider CSX Corporation Stock Decision Process with Lasso Regression where A is the set of discrete actions of CSX 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 Î³ ∈ [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(Statistical Inference (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of CSX 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?

## CSX Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**CSX CSX Corporation

**Time series to forecast n: 20 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell CSX 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

CSX Corporation assigned short-term B1 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Lasso Regression ^{1,2,3,4} and conclude that the CSX stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell CSX stock.**

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

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

Outlook* | B1 | Ba1 |

Operational Risk | 32 | 58 |

Market Risk | 70 | 89 |

Technical Analysis | 74 | 85 |

Fundamental Analysis | 76 | 65 |

Risk Unsystematic | 39 | 63 |

### Prediction Confidence Score

## References

- Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
- T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- Bengio Y, Schwenk H, SenÃ©cal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
- Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London

## Frequently Asked Questions

Q: What is the prediction methodology for CSX stock?A: CSX stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Lasso Regression

Q: Is CSX stock a buy or sell?

A: The dominant strategy among neural network is to Sell CSX Stock.

Q: Is CSX Corporation stock a good investment?

A: The consensus rating for CSX Corporation is Sell and assigned short-term B1 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of CSX stock?

A: The consensus rating for CSX is Sell.

Q: What is the prediction period for CSX stock?

A: The prediction period for CSX is (n+3 month)