Can neural networks predict stock market? (DH Stock Forecast)

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We evaluate Definitive Healthcare prediction models with Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the DH 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 Hold DH stock.


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

Key Points

  1. Trust metric by Neural Network
  2. What is prediction model?
  3. How can neural networks improve predictions?

DH Target Price Prediction Modeling Methodology

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 consider Definitive Healthcare Stock Decision Process with Pearson Correlation where A is the set of discrete actions of DH 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(Pearson Correlation)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) i = 1 n a i

n:Time series to forecast

p:Price signals of DH 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?

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

Sample Set: Neural Network
Stock/Index: DH Definitive Healthcare
Time series to forecast n: 12 Oct 2022 for (n+6 month)

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

Definitive Healthcare assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the DH 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 Hold DH stock.

Financial State Forecast for DH Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B3
Operational Risk 8280
Market Risk8131
Technical Analysis8141
Fundamental Analysis3650
Risk Unsystematic3033

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 621 signals.

References

  1. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
  2. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
  3. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
  4. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
  5. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
  6. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
  7. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked QuestionsQ: What is the prediction methodology for DH stock?
A: DH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation
Q: Is DH stock a buy or sell?
A: The dominant strategy among neural network is to Hold DH Stock.
Q: Is Definitive Healthcare stock a good investment?
A: The consensus rating for Definitive Healthcare is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of DH stock?
A: The consensus rating for DH is Hold.
Q: What is the prediction period for DH stock?
A: The prediction period for DH is (n+6 month)

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