Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed.** We evaluate Twitter Inc. prediction models with Statistical Inference (ML) and Linear Regression ^{1,2,3,4} and conclude that the TWTR 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 Hold TWTR stock.**

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

*Keywords:*## Key Points

- Can statistics predict the future?
- What is the use of Markov decision process?
- Can stock prices be predicted?

## TWTR Target Price Prediction Modeling Methodology

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We consider Twitter Inc. Stock Decision Process with Linear Regression where A is the set of discrete actions of TWTR 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(Linear 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+8 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of TWTR 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?

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

**Sample Set:**Neural Network

**Stock/Index:**TWTR Twitter Inc.

**Time series to forecast n: 05 Oct 2022**for (n+8 weeks)

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

Twitter Inc. assigned short-term B1 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Linear Regression ^{1,2,3,4} and conclude that the TWTR 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 Hold TWTR stock.**

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

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

Outlook* | B1 | Ba1 |

Operational Risk | 37 | 49 |

Market Risk | 41 | 58 |

Technical Analysis | 74 | 67 |

Fundamental Analysis | 85 | 89 |

Risk Unsystematic | 57 | 86 |

### Prediction Confidence Score

## References

- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86

## Frequently Asked Questions

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

Q: Is TWTR stock a buy or sell?

A: The dominant strategy among neural network is to Hold TWTR Stock.

Q: Is Twitter Inc. stock a good investment?

A: The consensus rating for Twitter Inc. is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of TWTR stock?

A: The consensus rating for TWTR is Hold.

Q: What is the prediction period for TWTR stock?

A: The prediction period for TWTR is (n+8 weeks)