Outlook: DecisionPoint Systems Inc. Common Stock assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 10 Dec 2022 for (n+16 weeks)
Methodology : Modular Neural Network (Market Volatility Analysis)

## Abstract

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. (Nelson, D.M., Pereira, A.C. and De Oliveira, R.A., 2017, May. Stock market's price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). Ieee.) We evaluate DecisionPoint Systems Inc. Common Stock prediction models with Modular Neural Network (Market Volatility Analysis) and Stepwise Regression1,2,3,4 and conclude that the DPSI stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

## Key Points

1. Nash Equilibria
2. Can statistics predict the future?
3. How accurate is machine learning in stock market?

## DPSI Target Price Prediction Modeling Methodology

We consider DecisionPoint Systems Inc. Common Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of DPSI 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(Stepwise 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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+16 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of DPSI stock

j:Nash equilibria (Neural Network)

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?

## DPSI Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: DPSI DecisionPoint Systems Inc. Common Stock
Time series to forecast n: 10 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

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 (Grey to Black): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for DecisionPoint Systems Inc. Common Stock

1. Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.
2. For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
3. In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
4. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.

*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

DecisionPoint Systems Inc. Common Stock assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Stepwise Regression1,2,3,4 and conclude that the DPSI stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

### Financial State Forecast for DPSI DecisionPoint Systems Inc. Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 5330
Market Risk6874
Technical Analysis5856
Fundamental Analysis5878
Risk Unsystematic4341

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 873 signals.

## References

1. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
2. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
3. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
4. 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
5. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
6. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
7. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked QuestionsQ: What is the prediction methodology for DPSI stock?
A: DPSI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Stepwise Regression
Q: Is DPSI stock a buy or sell?
A: The dominant strategy among neural network is to Buy DPSI Stock.
Q: Is DecisionPoint Systems Inc. Common Stock stock a good investment?
A: The consensus rating for DecisionPoint Systems Inc. Common Stock is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of DPSI stock?
A: The consensus rating for DPSI is Buy.
Q: What is the prediction period for DPSI stock?
A: The prediction period for DPSI is (n+16 weeks)