How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently.** We evaluate Church & Dwight prediction models with Modular Neural Network (CNN Layer) and Sign Test ^{1,2,3,4} and conclude that the CHD stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold CHD stock.**

**CHD, Church & Dwight, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Market Outlook
- Can we predict stock market using machine learning?
- How do you decide buy or sell a stock?

## CHD Target Price Prediction Modeling Methodology

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. We consider Church & Dwight Stock Decision Process with Sign Test where A is the set of discrete actions of CHD 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of CHD 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?

## CHD Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**CHD Church & Dwight

**Time series to forecast n: 23 Sep 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold CHD 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

Church & Dwight assigned short-term Ba2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Sign Test ^{1,2,3,4} and conclude that the CHD stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold CHD stock.**

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

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

Outlook* | Ba2 | B2 |

Operational Risk | 55 | 50 |

Market Risk | 84 | 55 |

Technical Analysis | 84 | 75 |

Fundamental Analysis | 52 | 36 |

Risk Unsystematic | 67 | 35 |

### Prediction Confidence Score

## References

- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55

## Frequently Asked Questions

Q: What is the prediction methodology for CHD stock?A: CHD stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Sign Test

Q: Is CHD stock a buy or sell?

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

Q: Is Church & Dwight stock a good investment?

A: The consensus rating for Church & Dwight is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of CHD stock?

A: The consensus rating for CHD is Hold.

Q: What is the prediction period for CHD stock?

A: The prediction period for CHD is (n+4 weeks)

- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)