Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. ** We evaluate Fortis Inc. prediction models with Transfer Learning (ML) and Chi-Square ^{1,2,3,4} and conclude that the FTS stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy FTS stock.**

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

*Keywords:*## Key Points

- Market Risk
- Can neural networks predict stock market?
- Buy, Sell and Hold Signals

## FTS Target Price Prediction Modeling Methodology

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We consider Fortis Inc. Stock Decision Process with Chi-Square where A is the set of discrete actions of FTS 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(Chi-Square)

^{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(Transfer Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of FTS 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?

## FTS Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**FTS Fortis Inc.

**Time series to forecast n: 19 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy FTS 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

Fortis Inc. assigned short-term Ba3 & long-term Caa1 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Chi-Square ^{1,2,3,4} and conclude that the FTS stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy FTS stock.**

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

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

Outlook* | Ba3 | Caa1 |

Operational Risk | 42 | 45 |

Market Risk | 74 | 36 |

Technical Analysis | 71 | 41 |

Fundamental Analysis | 66 | 35 |

Risk Unsystematic | 71 | 46 |

### Prediction Confidence Score

## References

- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
- M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016

## Frequently Asked Questions

Q: What is the prediction methodology for FTS stock?A: FTS stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Chi-Square

Q: Is FTS stock a buy or sell?

A: The dominant strategy among neural network is to Buy FTS Stock.

Q: Is Fortis Inc. stock a good investment?

A: The consensus rating for Fortis Inc. is Buy and assigned short-term Ba3 & long-term Caa1 forecasted stock rating.

Q: What is the consensus rating of FTS stock?

A: The consensus rating for FTS is Buy.

Q: What is the prediction period for FTS stock?

A: The prediction period for FTS is (n+6 month)

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