Dow Inc. Stock Forecast, Price & Rating (DOW)

In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. We evaluate Dow Inc. prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Beta^1,2,3,4 and conclude that the DOW 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 Hold DOW stock.

Keywords: DOW, Dow Inc., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What statistical methods are used to analyze data?
2. What is prediction model?
3. Buy, Sell and Hold Signals

DOW Target Price Prediction Modeling Methodology

One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We consider Dow Inc. Stock Decision Process with Beta where A is the set of discrete actions of DOW 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(Beta)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of DOW 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 Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: DOW Dow Inc.
Time series to forecast n: 22 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold DOW 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 Inc. assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Beta^1,2,3,4 and conclude that the DOW 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 Hold DOW stock.

Financial State Forecast for DOW Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Operational Risk	52	32
Market Risk	46	76
Technical Analysis	37	83
Fundamental Analysis	50	54
Risk Unsystematic	84	80

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 784 signals.

References

1. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
2. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
3. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
4. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
5. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
6. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
7. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]

Frequently Asked Questions

Q: What is the prediction methodology for DOW stock?
A: DOW stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Beta
Q: Is DOW stock a buy or sell?
A: The dominant strategy among neural network is to Hold DOW Stock.
Q: Is Dow Inc. stock a good investment?
A: The consensus rating for Dow Inc. is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of DOW stock?
A: The consensus rating for DOW is Hold.
Q: What is the prediction period for DOW stock?
A: The prediction period for DOW is (n+1 year)

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