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 Dow Jones Industrial Average Index prediction models with Modular Neural Network (Market Direction Analysis) and Independent T-Test ^{1,2,3,4} and conclude that the Dow Jones Industrial Average Index 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 Hold Dow Jones Industrial Average Index stock.**

**Dow Jones Industrial Average Index, Dow Jones Industrial Average Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Why do we need predictive models?
- What is statistical models in machine learning?
- Can we predict stock market using machine learning?

## Dow Jones Industrial Average Index Target Price Prediction Modeling Methodology

In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. We consider Dow Jones Industrial Average Index Stock Decision Process with Independent T-Test where A is the set of discrete actions of Dow Jones Industrial Average Index 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(Independent T-Test)

^{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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of Dow Jones Industrial Average Index 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?

## Dow Jones Industrial Average Index Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**Dow Jones Industrial Average Index Dow Jones Industrial Average Index

**Time series to forecast n: 11 Sep 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold Dow Jones Industrial Average Index 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

Dow Jones Industrial Average Index assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Independent T-Test ^{1,2,3,4} and conclude that the Dow Jones Industrial Average Index 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 Hold Dow Jones Industrial Average Index stock.**

### Financial State Forecast for Dow Jones Industrial Average Index Stock Options & Futures

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 61 | 68 |

Market Risk | 85 | 57 |

Technical Analysis | 44 | 81 |

Fundamental Analysis | 74 | 60 |

Risk Unsystematic | 70 | 60 |

### Prediction Confidence Score

## References

- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.

## Frequently Asked Questions

Q: What is the prediction methodology for Dow Jones Industrial Average Index stock?A: Dow Jones Industrial Average Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Independent T-Test

Q: Is Dow Jones Industrial Average Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold Dow Jones Industrial Average Index Stock.

Q: Is Dow Jones Industrial Average Index stock a good investment?

A: The consensus rating for Dow Jones Industrial Average Index is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of Dow Jones Industrial Average Index stock?

A: The consensus rating for Dow Jones Industrial Average Index is Hold.

Q: What is the prediction period for Dow Jones Industrial Average Index stock?

A: The prediction period for Dow Jones Industrial Average Index is (n+16 weeks)