How accurate is machine learning in stock market? (Dow Jones Shanghai Index Stock Forecast)

Finance is one of the pioneering industries that started using Machine Learning (ML), a subset of Artificial Intelligence (AI) in the early 80s for market prediction. Since then, major firms and hedge funds have adopted machine learning for stock prediction, portfolio optimization, credit lending, stock betting, etc. In this paper, we survey all the different approaches of machine learning that can be incorporated in applied finance. We evaluate Dow Jones Shanghai Index prediction models with Ensemble Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the Dow Jones Shanghai 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 Dow Jones Shanghai Index stock.

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

Key Points

1. Decision Making
2. How can neural networks improve predictions?
3. Stock Rating

Dow Jones Shanghai Index Target Price Prediction Modeling Methodology

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. We consider Dow Jones Shanghai Index Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of Dow Jones Shanghai 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 Sign-Rank 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(Ensemble Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of Dow Jones Shanghai 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?

Dow Jones Shanghai Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: Dow Jones Shanghai Index Dow Jones Shanghai Index
Time series to forecast n: 10 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell Dow Jones Shanghai 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 Dow Jones Shanghai Index

1. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
2. When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.
3. In the reporting period that includes the date of initial application of these amendments, an entity is not required to present the quantitative information required by paragraph 28(f) of IAS 8.
4. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.

*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

Dow Jones Shanghai Index assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the Dow Jones Shanghai 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 Dow Jones Shanghai Index stock.

Financial State Forecast for Dow Jones Shanghai Index Dow Jones Shanghai Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B3
Operational Risk	31	52
Market Risk	40	36
Technical Analysis	85	53
Fundamental Analysis	70	42
Risk Unsystematic	87	55

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 802 signals.

References

1. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
2. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
3. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
4. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
5. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
6. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
7. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for Dow Jones Shanghai Index stock?
A: Dow Jones Shanghai Index stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is Dow Jones Shanghai Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell Dow Jones Shanghai Index Stock.
Q: Is Dow Jones Shanghai Index stock a good investment?
A: The consensus rating for Dow Jones Shanghai Index is Sell and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of Dow Jones Shanghai Index stock?
A: The consensus rating for Dow Jones Shanghai Index is Sell.
Q: What is the prediction period for Dow Jones Shanghai Index stock?
A: The prediction period for Dow Jones Shanghai Index is (n+3 month)

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