Machine Learning stock prediction: DXC Stock Prediction

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We evaluate DXC Technology prediction models with Modular Neural Network (DNN Layer) and Sign Test^1,2,3,4 and conclude that the DXC 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 DXC stock.

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

Key Points

1. Market Signals
2. Fundemental Analysis with Algorithmic Trading
3. Can we predict stock market using machine learning?

DXC Target Price Prediction Modeling Methodology

In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. We consider DXC Technology Stock Decision Process with Sign Test where A is the set of discrete actions of DXC 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of DXC 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?

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

Sample Set: Neural Network
Stock/Index: DXC DXC Technology
Time series to forecast n: 10 Oct 2022 for (n+16 weeks)

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

DXC Technology assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Sign Test^1,2,3,4 and conclude that the DXC 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 DXC stock.

Financial State Forecast for DXC Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Operational Risk	62	64
Market Risk	46	44
Technical Analysis	42	86
Fundamental Analysis	68	51
Risk Unsystematic	52	81

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 700 signals.

References

1. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
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. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
4. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
5. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
6. 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
7. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999

Frequently Asked Questions

Q: What is the prediction methodology for DXC stock?
A: DXC stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Sign Test
Q: Is DXC stock a buy or sell?
A: The dominant strategy among neural network is to Buy DXC Stock.
Q: Is DXC Technology stock a good investment?
A: The consensus rating for DXC Technology is Buy and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of DXC stock?
A: The consensus rating for DXC is Buy.
Q: What is the prediction period for DXC stock?
A: The prediction period for DXC is (n+16 weeks)

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