Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We evaluate NASDAQ Composite Index prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Sign Test1,2,3,4 and conclude that the NASDAQ Composite Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy NASDAQ Composite Index stock.

Keywords: NASDAQ Composite Index, NASDAQ Composite Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Can statistics predict the future?
2. Operational Risk
3. Technical Analysis with Algorithmic Trading

## NASDAQ Composite Index Target Price Prediction Modeling Methodology

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. We consider NASDAQ Composite Index Stock Decision Process with Sign Test where A is the set of discrete actions of NASDAQ Composite 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(Sign Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of NASDAQ Composite 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?

## NASDAQ Composite Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: NASDAQ Composite Index NASDAQ Composite Index
Time series to forecast n: 26 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy NASDAQ Composite 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

NASDAQ Composite Index assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Sign Test1,2,3,4 and conclude that the NASDAQ Composite Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy NASDAQ Composite Index stock.

### Financial State Forecast for NASDAQ Composite Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B1
Operational Risk 4865
Market Risk7964
Technical Analysis8850
Fundamental Analysis8955
Risk Unsystematic6152

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 756 signals.

## References

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2. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
3. C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
4. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
5. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
6. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
7. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
Frequently Asked QuestionsQ: What is the prediction methodology for NASDAQ Composite Index stock?
A: NASDAQ Composite Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Sign Test
Q: Is NASDAQ Composite Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy NASDAQ Composite Index Stock.
Q: Is NASDAQ Composite Index stock a good investment?
A: The consensus rating for NASDAQ Composite Index is Buy and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NASDAQ Composite Index stock?
A: The consensus rating for NASDAQ Composite Index is Buy.
Q: What is the prediction period for NASDAQ Composite Index stock?
A: The prediction period for NASDAQ Composite Index is (n+3 month)