Machine Learning stock prediction: PSI Index Stock Prediction

PSI Index Research Report

Summary

The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We evaluate PSI Index prediction models with Transfer Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the PSI Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold PSI Index stock.

Key Points

  1. Trading Interaction
  2. What are buy sell or hold recommendations?
  3. Trading Interaction

PSI Index Target Price Prediction Modeling Methodology

We consider PSI Index Stock Decision Process with Transfer Learning (ML) where A is the set of discrete actions of PSI 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(Wilcoxon Rank-Sum Test)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(Transfer Learning (ML)) X S(n):→ (n+3 month) r s rs

n:Time series to forecast

p:Price signals of PSI Index 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?

PSI Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: PSI Index PSI Index
Time series to forecast n: 25 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold PSI 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%

Adjusted IFRS* Prediction Methods for PSI Index

  1. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.
  2. In accordance with paragraph 4.1.3(a), principal is the fair value of the financial asset at initial recognition. However that principal amount may change over the life of the financial asset (for example, if there are repayments of principal).
  3. In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
  4. Changes in market conditions that give rise to market risk include changes in a benchmark interest rate, the price of another entity's financial instrument, a commodity price, a foreign exchange rate or an index of prices or rates.

*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

PSI Index assigned short-term B1 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the PSI Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold PSI Index stock.

Financial State Forecast for PSI Index PSI Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba2
Operational Risk 5385
Market Risk4644
Technical Analysis6588
Fundamental Analysis5454
Risk Unsystematic8271

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 667 signals.

References

  1. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
  2. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
  3. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
  4. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
  5. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
  6. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
  7. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
Frequently Asked QuestionsQ: What is the prediction methodology for PSI Index stock?
A: PSI Index stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is PSI Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold PSI Index Stock.
Q: Is PSI Index stock a good investment?
A: The consensus rating for PSI Index is Hold and assigned short-term B1 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of PSI Index stock?
A: The consensus rating for PSI Index is Hold.
Q: What is the prediction period for PSI Index stock?
A: The prediction period for PSI Index is (n+3 month)

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