OXLCZ Oxford Lane Capital Corp. 5.00% Notes due 2027

Outlook: Oxford Lane Capital Corp. 5.00% Notes due 2027 assigned short-term Ba3 & long-term B2 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 07 Dec 2022 for (n+1 year)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized.(Qiu, M. and Song, Y., 2016. Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), p.e0155133.) We evaluate Oxford Lane Capital Corp. 5.00% Notes due 2027 prediction models with Modular Neural Network (Financial Sentiment Analysis) and Spearman Correlation^1,2,3,4 and conclude that the OXLCZ 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 OXLCZ stock.

Key Points

1. Operational Risk
2. Trust metric by Neural Network
3. What is a prediction confidence?

OXLCZ Target Price Prediction Modeling Methodology

We consider Oxford Lane Capital Corp. 5.00% Notes due 2027 Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of OXLCZ 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(Spearman Correlation)^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 (Financial Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of OXLCZ 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?

OXLCZ Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: OXLCZ Oxford Lane Capital Corp. 5.00% Notes due 2027
Time series to forecast n: 07 Dec 2022 for (n+1 year)

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

Adjusted IFRS* Prediction Methods for Oxford Lane Capital Corp. 5.00% Notes due 2027

1. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
2. One of the defining characteristics of a derivative is that it has an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. An option contract meets that definition because the premium is less than the investment that would be required to obtain the underlying financial instrument to which the option is linked. A currency swap that requires an initial exchange of different currencies of equal fair values meets the definition because it has a zero initial net investment.
3. The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
4. 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.

*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

Oxford Lane Capital Corp. 5.00% Notes due 2027 assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Spearman Correlation^1,2,3,4 and conclude that the OXLCZ 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 OXLCZ stock.

Financial State Forecast for OXLCZ Oxford Lane Capital Corp. 5.00% Notes due 2027 Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	87	86
Market Risk	72	30
Technical Analysis	49	34
Fundamental Analysis	34	65
Risk Unsystematic	88	34

Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 831 signals.

References

1. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
2. 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.
3. 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.
4. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
5. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
6. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
7. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58

Frequently Asked Questions

Q: What is the prediction methodology for OXLCZ stock?
A: OXLCZ stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Spearman Correlation
Q: Is OXLCZ stock a buy or sell?
A: The dominant strategy among neural network is to Hold OXLCZ Stock.
Q: Is Oxford Lane Capital Corp. 5.00% Notes due 2027 stock a good investment?
A: The consensus rating for Oxford Lane Capital Corp. 5.00% Notes due 2027 is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of OXLCZ stock?
A: The consensus rating for OXLCZ is Hold.
Q: What is the prediction period for OXLCZ stock?
A: The prediction period for OXLCZ is (n+1 year)

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