Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history.** We evaluate Nasdaq, Inc. prediction models with Multi-Instance Learning (ML) and Chi-Square ^{1,2,3,4} and conclude that the NDAQ 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 Hold NDAQ stock.**

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

*Keywords:*## Key Points

- Can neural networks predict stock market?
- What is neural prediction?
- Stock Rating

## NDAQ Target Price Prediction Modeling Methodology

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. We consider Nasdaq, Inc. Stock Decision Process with Chi-Square where A is the set of discrete actions of NDAQ 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(Multi-Instance Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of NDAQ 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?

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

**Sample Set:**Neural Network

**Stock/Index:**NDAQ Nasdaq, Inc.

**Time series to forecast n: 31 Oct 2022**for (n+6 month)

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

## Adjusted IFRS* Prediction Methods for Nasdaq, Inc.

- An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
- The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
- Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
- An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

Nasdaq, Inc. assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Multi-Instance Learning (ML) with Chi-Square ^{1,2,3,4} and conclude that the NDAQ 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 Hold NDAQ stock.**

### Financial State Forecast for NDAQ Nasdaq, Inc. Stock Options & Futures

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

Outlook* | B2 | B1 |

Operational Risk | 71 | 63 |

Market Risk | 79 | 54 |

Technical Analysis | 55 | 42 |

Fundamental Analysis | 33 | 64 |

Risk Unsystematic | 39 | 60 |

### Prediction Confidence Score

## References

- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
- 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.
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
- Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
- D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.

## Frequently Asked Questions

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

Q: Is NDAQ stock a buy or sell?

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

Q: Is Nasdaq, Inc. stock a good investment?

A: The consensus rating for Nasdaq, Inc. is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of NDAQ stock?

A: The consensus rating for NDAQ is Hold.

Q: What is the prediction period for NDAQ stock?

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