With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. We evaluate DuPont prediction models with Modular Neural Network (Market Direction Analysis) and Chi-Square1,2,3,4 and conclude that the DD 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 Sell DD stock.

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

## Key Points

1. Prediction Modeling
2. Decision Making
3. What is prediction model? ## DD Target Price Prediction Modeling Methodology

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We consider DuPont Stock Decision Process with Chi-Square where A is the set of discrete actions of DD 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+6 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of DD stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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How do AC Investment Research machine learning (predictive) algorithms actually work?

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

Sample Set: Neural Network
Stock/Index: DD DuPont
Time series to forecast n: 13 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell DD 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

DuPont assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Chi-Square1,2,3,4 and conclude that the DD 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 Sell DD stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 3270
Market Risk5574
Technical Analysis8158
Fundamental Analysis8040
Risk Unsystematic4836

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 808 signals.

## References

1. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
2. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
3. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
4. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
5. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
6. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
7. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
Frequently Asked QuestionsQ: What is the prediction methodology for DD stock?
A: DD stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Chi-Square
Q: Is DD stock a buy or sell?
A: The dominant strategy among neural network is to Sell DD Stock.
Q: Is DuPont stock a good investment?
A: The consensus rating for DuPont is Sell and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of DD stock?
A: The consensus rating for DD is Sell.
Q: What is the prediction period for DD stock?
A: The prediction period for DD is (n+6 month)

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