Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions

Paper: arXiv 2312.16223

Abstract

Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making do to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters the investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor’s portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders’ confidence and foster transparency in the stock exchange domain.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard ML metrics and SHAP for explainability, but lacks deep mathematical derivations or advanced statistical theory. Empirically, it includes a case study with portfolio value enhancement on a specific exchange, suggesting data-driven implementation, though detailed backtest specifics or code are not provided in the excerpt.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Enhance Profitability & Confidence<br>through Interpretable AI for Investment"] --> B["Data/Inputs:<br>Equity Market Data &<br>Historical Investment Patterns"]
  B --> C["Methodology:<br>SHAP-based<br>Explainability Technique"]
  C --> D["Computation:<br>Train Interpretable<br>Decision Model &<br>Generate Feature Importance"]
  D --> E["Implementation:<br>Apply Model to<br>Trading Strategies"]
  E --> F{"Evaluation"}
  F -->|Case Study| G["Key Findings/Outcomes:<br>1. Enhanced Portfolio Value<br>2. Increased Stakeholder Confidence<br>3. Transparent Forecasting"]
  F -->|Feedback| C

Cited References (30)

1. The psychology and neuroscience of financial decision making (2016)
2. Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions (2021)
3. An interpretable decision-support systems for daily cryptocurrency trading (2022)
4. Automated trading systems statistical and machine learning methods and hardware implementation: a survey (2019)
5. Explainable AI in fintech risk management (2020)
6. Explainable AI in credit risk management (2021) — link
7. The benefits of using artificial intelligence in payment fraud detection: A case study (2018)
8. Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong (2020)

0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.