Does algo trading work? (NASDAQ Composite Index Stock Forecast)

Abstract

We evaluate NASDAQ Composite Index prediction models with Ensemble Learning (ML) and Ridge Regression^1,2,3,4 and conclude that the NASDAQ Composite Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy 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

1. Can statistics predict the future?
2. What is the best way to predict stock prices?
3. Fundemental Analysis with Algorithmic Trading

NASDAQ Composite Index Target Price Prediction Modeling Methodology

We consider NASDAQ Composite Index Stock Decision Process with Ridge Regression 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(Ridge Regression)^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+16 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

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

 

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?

NASDAQ Composite Index Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: NASDAQ Composite Index NASDAQ Composite Index
Time series to forecast n: 03 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy 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 Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Ridge Regression^1,2,3,4 and conclude that the NASDAQ Composite Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy NASDAQ Composite Index stock.

Financial State Forecast for NASDAQ Composite Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	80	48
Market Risk	84	65
Technical Analysis	56	39
Fundamental Analysis	53	32
Risk Unsystematic	61	78

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 638 signals.

References

1. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
2. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
3. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
4. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
5. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
6. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
7. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.

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 Ensemble Learning (ML) and Ridge Regression
Q: Is NASDAQ Composite Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy NASDAQ Composite Index Stock.
Q: Is NASDAQ Composite Index stock a good investment?
A: The consensus rating for NASDAQ Composite Index is Buy and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NASDAQ Composite Index stock?
A: The consensus rating for NASDAQ Composite Index is Buy.
Q: What is the prediction period for NASDAQ Composite Index stock?
A: The prediction period for NASDAQ Composite Index is (n+16 weeks)

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