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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68564
Volume:
10 |
10 |
Oct 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

AIML
Zeal Polytechnic pune

AIML
Zeal Polytechnic pune

AIML
Zeal Polytechnic pune

AIML
Zeal Polytechnic pune

AIML
Zeal Polytechnic pune

AIML
Zeal Polytechnic pune
Financial market prices change rapidly due to economic events, market sentiment and investor activity. Manual analysis of large volumes of market data is difficult, while price forecasts alone do not communicate possible losses. This paper proposes an AI-Based Financial Market Prediction and Risk Analysis App that integrates market visualization, AI-based forecasting and portfolio risk analytics within a single decision-support interface. The proposed system combines historical price and volume data, technical indicators and financial news sentiment with statistical baselines, XGBoost and long short-term memory (LSTM) models. A risk engine estimates volatility, Value at Risk (VaR), Conditional Value at Risk (CVaR) and stress-scenario outcomes. The application is designed to provide forecasts, uncertainty indicators, risk alerts and understandable insights. A chronological evaluation protocol is proposed using MAE, RMSE, directional accuracy and VaR backtesting. The system remains a proposed design; no live-trading or predictive performance claims are made.
KEYWORDS
This paper presents the design of an AI-Based Financial Market Prediction and Risk Analysis App that combines market overview, predictive intelligence and risk analytics in one interface. Its five-stage architecture connects data inputs and feature engineering with AI models, a risk engine and an interactive dashboard. The proposed approach emphasizes measurable risk, transparent insights and chronological validation. Future implementation and empirical testing are required to establish actual accuracy, reliability and usefulness.
[1] E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” Journal of Finance, 1970. [2] S. Gu, B. Kelly, and D. Xiu, “Empirical asset pricing via machine learning,” Review of Financial Studies, 2020. [3] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” ACM SIGKDD, 2016. [4] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, 1997. [5] P. Jorion, Value at Risk: The New Benchmark for Controlling Market Risk. McGraw-Hill, 1997. [6] R. T. Rockafellar and S. Uryasev, “Optimization of conditional value-at-risk,” Journal of Risk, 2000. [7] M. López de Prado, Advances in Financial Machine Learning. Wiley, 2018. [8] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” NeurIPS, 2017.
Publication Date
10/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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