This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models.** We evaluate Williams Companies prediction models with Inductive Learning (ML) and Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the WMB stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell WMB stock.**

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

*Keywords:*## Key Points

- What is statistical models in machine learning?
- Should I buy stocks now or wait amid such uncertainty?
- What is the use of Markov decision process?

## WMB Target Price Prediction Modeling Methodology

The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. We consider Williams Companies Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of WMB 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(Statistical Hypothesis Testing)

^{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(Inductive Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of WMB 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?

## WMB Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**WMB Williams Companies

**Time series to forecast n: 05 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell WMB 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

Williams Companies assigned short-term B2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Inductive Learning (ML) with Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the WMB stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell WMB stock.**

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

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

Outlook* | B2 | Ba3 |

Operational Risk | 65 | 41 |

Market Risk | 60 | 53 |

Technical Analysis | 78 | 90 |

Fundamental Analysis | 32 | 48 |

Risk Unsystematic | 47 | 79 |

### Prediction Confidence Score

## References

- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99

## Frequently Asked Questions

Q: What is the prediction methodology for WMB stock?A: WMB stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Statistical Hypothesis Testing

Q: Is WMB stock a buy or sell?

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

Q: Is Williams Companies stock a good investment?

A: The consensus rating for Williams Companies is Sell and assigned short-term B2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of WMB stock?

A: The consensus rating for WMB is Sell.

Q: What is the prediction period for WMB stock?

A: The prediction period for WMB is (n+1 year)

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