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 evaluate Euro Stoxx 50 Index prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression1,2,3,4 and conclude that the Euro Stoxx 50 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 Sell Euro Stoxx 50 Index stock.

Keywords: Euro Stoxx 50 Index, Euro Stoxx 50 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is the use of Markov decision process?
2. Can we predict stock market using machine learning?
3. Which neural network is best for prediction?

## Euro Stoxx 50 Index Target Price Prediction Modeling Methodology

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We consider Euro Stoxx 50 Index Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of Euro Stoxx 50 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(ElasticNet Regression)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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of Euro Stoxx 50 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?

## Euro Stoxx 50 Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: Euro Stoxx 50 Index Euro Stoxx 50 Index
Time series to forecast n: 08 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell Euro Stoxx 50 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 Euro Stoxx 50 Index

1. However, depending on the nature of the financial instruments and the credit risk information available for particular groups of financial instruments, an entity may not be able to identify significant changes in credit risk for individual financial instruments before the financial instrument becomes past due. This may be the case for financial instruments such as retail loans for which there is little or no updated credit risk information that is routinely obtained and monitored on an individual instrument until a customer breaches the contractual terms. If changes in the credit risk for individual financial instruments are not captured before they become past due, a loss allowance based only on credit information at an individual financial instrument level would not faithfully represent the changes in credit risk since initial recognition.
2. The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.
3. For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
4. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).

*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

Euro Stoxx 50 Index assigned short-term Ba3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with ElasticNet Regression1,2,3,4 and conclude that the Euro Stoxx 50 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 Sell Euro Stoxx 50 Index stock.

### Financial State Forecast for Euro Stoxx 50 Index Euro Stoxx 50 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba1
Operational Risk 5282
Market Risk8787
Technical Analysis7573
Fundamental Analysis7367
Risk Unsystematic4648

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 738 signals.

## References

1. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
2. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
3. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
4. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
5. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
6. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
7. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
Frequently Asked QuestionsQ: What is the prediction methodology for Euro Stoxx 50 Index stock?
A: Euro Stoxx 50 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression
Q: Is Euro Stoxx 50 Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell Euro Stoxx 50 Index Stock.
Q: Is Euro Stoxx 50 Index stock a good investment?
A: The consensus rating for Euro Stoxx 50 Index is Sell and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of Euro Stoxx 50 Index stock?
A: The consensus rating for Euro Stoxx 50 Index is Sell.
Q: What is the prediction period for Euro Stoxx 50 Index stock?
A: The prediction period for Euro Stoxx 50 Index is (n+3 month)