Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. We evaluate Nikkei 225 Index prediction models with Modular Neural Network (DNN Layer) and Beta1,2,3,4 and conclude that the Nikkei 225 Index 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 Nikkei 225 Index stock.

Keywords: Nikkei 225 Index, Nikkei 225 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Fundemental Analysis with Algorithmic Trading
2. What are buy sell or hold recommendations?

Nikkei 225 Index Target Price Prediction Modeling Methodology

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 consider Nikkei 225 Index Stock Decision Process with Beta where A is the set of discrete actions of Nikkei 225 Index 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(Beta)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 (DNN Layer)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of Nikkei 225 Index 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?

Nikkei 225 Index Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: Nikkei 225 Index Nikkei 225 Index
Time series to forecast n: 20 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold Nikkei 225 Index 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

Nikkei 225 Index assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Beta1,2,3,4 and conclude that the Nikkei 225 Index 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 Nikkei 225 Index stock.

Financial State Forecast for Nikkei 225 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B3
Operational Risk 5830
Market Risk4466
Technical Analysis3854
Fundamental Analysis6558
Risk Unsystematic5030

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 514 signals.

References

1. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
2. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
3. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
4. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
5. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
6. 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.
7. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
Frequently Asked QuestionsQ: What is the prediction methodology for Nikkei 225 Index stock?
A: Nikkei 225 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Beta
Q: Is Nikkei 225 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold Nikkei 225 Index Stock.
Q: Is Nikkei 225 Index stock a good investment?
A: The consensus rating for Nikkei 225 Index is Hold and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of Nikkei 225 Index stock?
A: The consensus rating for Nikkei 225 Index is Hold.
Q: What is the prediction period for Nikkei 225 Index stock?
A: The prediction period for Nikkei 225 Index is (n+6 month)