The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We evaluate BSE Sensex 30 Index prediction models with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and conclude that the BSE Sensex 30 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 BSE Sensex 30 Index stock.

Keywords: BSE Sensex 30 Index, BSE Sensex 30 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What statistical methods are used to analyze data?
2. What is a prediction confidence?
3. Trust metric by Neural Network

BSE Sensex 30 Index Target Price Prediction Modeling Methodology

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We consider BSE Sensex 30 Index Stock Decision Process with Logistic Regression where A is the set of discrete actions of BSE Sensex 30 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(Logistic 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(Multi-Task Learning (ML)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of BSE Sensex 30 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?

BSE Sensex 30 Index Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: BSE Sensex 30 Index BSE Sensex 30 Index
Time series to forecast n: 20 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold BSE Sensex 30 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

BSE Sensex 30 Index assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Logistic Regression1,2,3,4 and conclude that the BSE Sensex 30 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 BSE Sensex 30 Index stock.

Financial State Forecast for BSE Sensex 30 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 3148
Market Risk7237
Technical Analysis6254
Fundamental Analysis7174
Risk Unsystematic6460

Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 574 signals.

References

1. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
2. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
3. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
4. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
5. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
6. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
7. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
Frequently Asked QuestionsQ: What is the prediction methodology for BSE Sensex 30 Index stock?
A: BSE Sensex 30 Index stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Logistic Regression
Q: Is BSE Sensex 30 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold BSE Sensex 30 Index Stock.
Q: Is BSE Sensex 30 Index stock a good investment?
A: The consensus rating for BSE Sensex 30 Index is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of BSE Sensex 30 Index stock?
A: The consensus rating for BSE Sensex 30 Index is Hold.
Q: What is the prediction period for BSE Sensex 30 Index stock?
A: The prediction period for BSE Sensex 30 Index is (n+1 year)