In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We evaluate NASDAQ Composite Index prediction models with Reinforcement Machine Learning (ML) and Independent T-Test1,2,3,4 and conclude that the NASDAQ Composite Index 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 NASDAQ Composite Index stock.
Keywords: NASDAQ Composite Index, NASDAQ Composite Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How useful are statistical predictions?
- Nash Equilibria
- What is a prediction confidence?
NASDAQ Composite Index Target Price Prediction Modeling Methodology
The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We consider NASDAQ Composite Index Stock Decision Process with Independent T-Test where A is the set of discrete actions of NASDAQ Composite 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(Independent T-Test)5,6,7= X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of NASDAQ Composite Index stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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NASDAQ Composite Index Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: NASDAQ Composite Index NASDAQ Composite Index
Time series to forecast n: 13 Sep 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NASDAQ Composite 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
NASDAQ Composite Index assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Independent T-Test1,2,3,4 and conclude that the NASDAQ Composite Index 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 NASDAQ Composite Index stock.
Financial State Forecast for NASDAQ Composite Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B1 |
| Operational Risk | 31 | 52 |
| Market Risk | 88 | 74 |
| Technical Analysis | 77 | 38 |
| Fundamental Analysis | 60 | 52 |
| Risk Unsystematic | 38 | 62 |
Prediction Confidence Score
References
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
- Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
Frequently Asked Questions
Q: What is the prediction methodology for NASDAQ Composite Index stock?A: NASDAQ Composite Index stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Independent T-Test
Q: Is NASDAQ Composite Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold NASDAQ Composite Index Stock.
Q: Is NASDAQ Composite Index stock a good investment?
A: The consensus rating for NASDAQ Composite Index is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NASDAQ Composite Index stock?
A: The consensus rating for NASDAQ Composite Index is Hold.
Q: What is the prediction period for NASDAQ Composite Index stock?
A: The prediction period for NASDAQ Composite Index is (n+1 year)
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