How accurate is machine learning in stock market? (NSE AFFLE Stock Forecast)

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We evaluate Affle (India) Limited prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Sign Test^1,2,3,4 and conclude that the NSE AFFLE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell NSE AFFLE stock.

Keywords: NSE AFFLE, Affle (India) Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Nash Equilibria
2. What is neural prediction?
3. Should I buy stocks now or wait amid such uncertainty?

NSE AFFLE Target Price Prediction Modeling Methodology

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We consider Affle (India) Limited Stock Decision Process with Sign Test where A is the set of discrete actions of NSE AFFLE 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(Sign 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of NSE AFFLE 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?

NSE AFFLE Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: NSE AFFLE Affle (India) Limited
Time series to forecast n: 30 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell NSE AFFLE 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

Affle (India) Limited assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Sign Test^1,2,3,4 and conclude that the NSE AFFLE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell NSE AFFLE stock.

Financial State Forecast for NSE AFFLE Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Operational Risk	35	65
Market Risk	42	49
Technical Analysis	86	38
Fundamental Analysis	63	85
Risk Unsystematic	57	69

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 510 signals.

References

1. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
2. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
3. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
4. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
7. 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

Frequently Asked Questions

Q: What is the prediction methodology for NSE AFFLE stock?
A: NSE AFFLE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Sign Test
Q: Is NSE AFFLE stock a buy or sell?
A: The dominant strategy among neural network is to Sell NSE AFFLE Stock.
Q: Is Affle (India) Limited stock a good investment?
A: The consensus rating for Affle (India) Limited is Sell and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE AFFLE stock?
A: The consensus rating for NSE AFFLE is Sell.
Q: What is the prediction period for NSE AFFLE stock?
A: The prediction period for NSE AFFLE is (n+6 month)

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