In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We evaluate D.B.Corp Limited prediction models with Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NSE DBCORP 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 Buy NSE DBCORP stock.
Keywords: NSE DBCORP, D.B.Corp Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can machine learning predict?
- Investment Risk
- Understanding Buy, Sell, and Hold Ratings
NSE DBCORP 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 D.B.Corp Limited Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of NSE DBCORP 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(Wilcoxon Rank-Sum Test)5,6,7= X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of NSE DBCORP 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?
NSE DBCORP Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: NSE DBCORP D.B.Corp Limited
Time series to forecast n: 01 Oct 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy NSE DBCORP 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
D.B.Corp Limited assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NSE DBCORP 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 Buy NSE DBCORP stock.
Financial State Forecast for NSE DBCORP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B2 |
| Operational Risk | 56 | 33 |
| Market Risk | 37 | 64 |
| Technical Analysis | 54 | 42 |
| Fundamental Analysis | 78 | 68 |
| Risk Unsystematic | 51 | 63 |
Prediction Confidence Score
References
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
- Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
Frequently Asked Questions
Q: What is the prediction methodology for NSE DBCORP stock?A: NSE DBCORP stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test
Q: Is NSE DBCORP stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSE DBCORP Stock.
Q: Is D.B.Corp Limited stock a good investment?
A: The consensus rating for D.B.Corp Limited is Buy and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE DBCORP stock?
A: The consensus rating for NSE DBCORP is Buy.
Q: What is the prediction period for NSE DBCORP stock?
A: The prediction period for NSE DBCORP is (n+16 weeks)
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