How Is Machine Learning Used in Trading? (NSE PAGEIND Stock Forecast)

Page Industries Limited Research Report

Summary

The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. We evaluate Page Industries Limited prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test^1,2,3,4 and conclude that the NSE PAGEIND stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE PAGEIND stock.

Key Points

1. Fundemental Analysis with Algorithmic Trading
2. Is Target price a good indicator?
3. Investment Risk

NSE PAGEIND Target Price Prediction Modeling Methodology

We consider Page Industries Limited Stock Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of NSE PAGEIND 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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of NSE PAGEIND stock

j:Nash equilibria (Neural Network)

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 PAGEIND Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NSE PAGEIND Page Industries Limited
Time series to forecast n: 18 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE PAGEIND 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%

Adjusted IFRS* Prediction Methods for Page Industries Limited

1. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
2. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
3. To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).
4. To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Page Industries Limited assigned short-term Ba3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Sign Test^1,2,3,4 and conclude that the NSE PAGEIND stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE PAGEIND stock.

Financial State Forecast for NSE PAGEIND Page Industries Limited Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B3
Operational Risk	41	66
Market Risk	88	42
Technical Analysis	64	40
Fundamental Analysis	64	46
Risk Unsystematic	73	31

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 842 signals.

References

1. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
2. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
3. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
4. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
5. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
6. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
7. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]

Frequently Asked Questions

Q: What is the prediction methodology for NSE PAGEIND stock?
A: NSE PAGEIND stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test
Q: Is NSE PAGEIND stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE PAGEIND Stock.
Q: Is Page Industries Limited stock a good investment?
A: The consensus rating for Page Industries Limited is Hold and assigned short-term Ba3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of NSE PAGEIND stock?
A: The consensus rating for NSE PAGEIND is Hold.
Q: What is the prediction period for NSE PAGEIND stock?
A: The prediction period for NSE PAGEIND is (n+1 year)

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