This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate Donaldson prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the DCI 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 DCI stock.

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

## Key Points

1. Can we predict stock market using machine learning?
2. How do you know when a stock will go up or down?
3. How do you pick a stock?

## DCI Target Price Prediction Modeling Methodology

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. We consider Donaldson Stock Decision Process with Polynomial Regression where A is the set of discrete actions of DCI 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(Polynomial Regression)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 (Social Media Sentiment Analysis)) X S(n):→ (n+16 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of DCI 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?

## DCI Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: DCI Donaldson
Time series to forecast n: 18 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold DCI 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

Donaldson assigned short-term Caa2 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the DCI 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 DCI stock.

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

Rating Short-Term Long-Term Senior
Outlook*Caa2B3
Operational Risk 4062
Market Risk4934
Technical Analysis4839
Fundamental Analysis4740
Risk Unsystematic3562

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 481 signals.

## References

1. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
2. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
3. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
4. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
Frequently Asked QuestionsQ: What is the prediction methodology for DCI stock?
A: DCI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Polynomial Regression
Q: Is DCI stock a buy or sell?
A: The dominant strategy among neural network is to Hold DCI Stock.
Q: Is Donaldson stock a good investment?
A: The consensus rating for Donaldson is Hold and assigned short-term Caa2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of DCI stock?
A: The consensus rating for DCI is Hold.
Q: What is the prediction period for DCI stock?
A: The prediction period for DCI is (n+16 weeks)

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