How to Review Literature of Finance?

Analyzing previous finance literature is crucial for grasping existing knowledge, identifying research gaps, and refining your research direction. This study note guides you through key considerations when critically evaluating financial research papers, complemented with numerical examples for clarity.

1. Understanding the Nature of Study:

Identify whether the study is a Theoretical Model, Data Application, or Data Mining Exercise. When reviewing a finance paper, identify whether it:

• Develops a theoretical model: Establishes new frameworks or relationships.

• Applies existing techniques: Uses known methods to analyze new data.

• Engages in data mining: Searches data for patterns without theoretical backing.

Example:

• Theoretical Model: A paper proposes a new model linking interest rates and stock prices:

• Data Application: Uses CAPM to estimate stock returns for Indian companies.

• Data Mining: Analyzes 1,000 variables without a hypothesis, hoping to find any significant correlation.

Note:👉 A sound paper should ideally be theory-driven rather than a random search for significance.

2. Quality of Data Used in the Literature

Is the Data, used in the study, of “Good Quality”?

High-quality data ensures credible results. Assess the following:

• Source Reliability: Is the data from verified databases (e.g., Bloomberg, RBI, NSE)?

• Sample Size: Is the sample large enough to invoke asymptotic theory?

• Data Period: Is it long enough to capture economic cycles?

Example:

• Good Data: Daily stock prices of 500 companies from NSE over 10 years (ample sample for robust inference).

• Poor Data: Monthly returns of 5 stocks over 6 months (too small to generalize results).

  
  

Note:👉 Reliable sources and sufficiently large samples improve result credibility.

3. Technical Validity

Have the Techniques Been Validly Applied? Check if the methodologies align with the research question and data type. Ensure diagnostic tests validate the model.

Techniques and Diagnostics:

• OLS Regression: Check for heteroskedasticity (Breusch-Pagan test) and autocorrelation (Durbin-Watson test).

• Time Series Models: Verify stationarity (ADF test) and multicollinearity (VIF scores).

Example:

A study regresses stock returns on macroeconomic variables:

• Valid Application: Conducts VIF test (all < 5), no multicollinearity found.

• Invalid Application: Omits diagnostic tests, risking biased estimates.

  
  

Note: 👉 Diagnostics ensure the results are reliable and not artefacts of data issues.

4. Data Interpretation

Have the Results Been Sensibly Interpreted? Evaluate if the authors interpret their results logically and without exaggeration.

Example Interpretation:

• Sensible: “A 1% increase in interest rates leads to a 0.5% decline in stock returns, consistent with economic theory.”

• Exaggerated: “Interest rate changes fully explain stock market behaviour,” ignoring other factors.

Note:👉 Accurate interpretations should align with the data's strengths and limitations.

5. Link between Results and Conclusion

Authors should not overstate findings or generalize beyond their data scope.

Example:

• Appropriate Conclusion: “Our results indicate a significant but moderate relationship between inflation and stock returns for the Indian market.”

• Overstated Conclusion: “Inflation is the sole determinant of stock returns globally,” despite only Indian data being used.

  
  

Note: 👉 Conclusions must reflect the results' scope and robustness.

Final Thoughts

Understanding previous finance literature involves critically evaluating the research’s theoretical foundation, data quality, applied techniques, result interpretation, and conclusions. A careful review ensures that you build your research on solid ground, avoiding common pitfalls of data misuse or overstated claims.