Financial Analysis Made Easy with Conversational Data Insights

Financial analysts today deal with ever-growing amounts of complex data. Making sense of this data often involves tedious workflows of extracting, cleaning, analyzing and visualizing across disparate sources. This makes gaining actionable insights time-consuming and cumbersome. But what if we could analyze financial data through intuitive conversations instead?

PandasAI makes this possible by adding a natural language interface to your financial data analysis workflows. With PandasAI, financial analysts can get conversational with their data and gain insights fast. In this hands-on guide, we will walk through practical examples of how PandasAI can be leveraged to streamline common financial analysis tasks.

Overview

PandasAI allows users to analyze data simply through textual conversations. Under the hood, it automatically writes and executes the required code to answer questions. This frees analysts from the complexity of coding so they can focus on uncovering insights.

Some key features relevant for financial analysis include:

• Plain English queries - Ask questions about your data naturally and get conversational answers
• Interactive analysis - Dig deeper into the data through follow-up questions
• Code-free visualization - Plot charts and graphs with conversational commands
• Connect to data sources - Analyze live data from databases, APIs and more
• Extensive calculations - Perform statistical analysis, time series modeling, risk analysis and more

With these capabilities tailored for finance, PandasAI is poised to transform how analysts work with data. Let's look at some practical examples of applying it for financial workflows.

Installation and Setup

Before we start conversing with data, let's install PandasAI and connect it to a sample financial dataset.

Run the following in your environment to install PandasAI using pip:

pip install pandasai

With PandasAI installed, let's initialize it on our dataset. We will use a sample dataset stocks.csv containing historical data for a portfolio of stocks.

from pandasai import SmartDataframe

df = SmartDataframe("stocks.csv")

This creates a SmartDataframe that wraps the Pandas DataFrame and equips it with conversational capabilities powered by PandasAI.

We are now ready to start analyzing by simply chatting with the df!

Conversational Data Exploration

One of the most powerful features of PandasAI is exploring data through intuitive conversations. Let's go through some examples of asking natural questions to our portfolio data and seeing PandasAI automatically generate relevant answers.

Q: How many stocks are there in the portfolio?

df.chat("How many stocks are there in the portfolio?")

A: There are 5 stocks in the portfolio

PandasAI understands we are asking for a total count and performs the required aggregation to return the number of unique stock symbols.

Q: What was the daily performance of the portfolio over the last month?

df.chat("What was the daily performance of the portfolio over the last month?")

A:

It recognizes we need the portfolio returns indexed by date for the past month and generates the relevant daily return table.

We can ask endless exploratory questions and seamlessly get answers ready for financial analysis. Next, let's look at some examples of how we can leverage PandasAI's conversational capabilities for common analytical tasks.

Investment Research

A key part of an analyst's role is researching investment opportunities across asset classes. This involves data analysis to estimate returns, model risk and compare alternatives. PandasAI can enhance such workflows through natural language.

Estimating Expected Returns

Let's analyze the expected returns for a stock in our portfolio - AAPL.

Q: What are the expected annual returns for AAPL stock over the next 5 years?

df.chat("What are the expected annual returns for AAPL stock over the next 5 years?")

A: The expected annual returns for AAPL stock over the next 5 years are:

• Year 1: 18.5%
• Year 2: 19.2%
• Year 3: 16.1%
• Year 4: 12.3%
• Year 5: 14.2%

PandasAI performs time series forecasting to predict future returns for the requested stock. The predictions empower analysts to make data-driven investment decisions.

Comparing Risk Profile

Beyond returns, estimating risk is crucial for investment analysis. Let's compare the risk profile of Netflix and Microsoft stocks:

Q: Which stock has had higher volatility over the past 3 years - NFLX or MSFT?

df.chat("Which stock has had higher volatility over the past 3 years - NFLX or MSFT?")

A: NFLX has had higher volatility with a standard deviation of 52% compared to 31% for MSFT over the past 3 years.

PandasAI calculates and compares the historical volatility to determine which stock carries higher risk. Such insights help analysts weigh risk vs reward tradeoffs.

Modeling Stock Correlations

Assessing how securities move relative to each other is vital for diversification. We can analyze the correlation between stocks conversationally:

Q: What is the correlation between AAPL and TSLA stocks?

df.chat("What is the correlation between AAPL and TSLA stocks?")  

A: The correlation between AAPL and TSLA is 0.41 over the past 5 years indicating a moderate positive relationship.

The conversational interface makes such analytical tasks almost effortless for financial analysts.

Portfolio Optimization

A key responsibility of financial analysts is constructing optimal portfolios by selecting the right mix of assets. PandasAI can enhance portfolio modeling through natural language.

Retrieving Optimal Weights

Let's say we want to find the optimal allocation for our 5 stock portfolio that maximizes Sharpe ratio.

Q: What are the optimal portfolio weights to maximize the Sharpe ratio?

df.chat("What are the optimal portfolio weights to maximize the Sharpe ratio?")

A: The optimal weights that maximize the portfolio Sharpe ratio are:

• AAPL: 20%
• MSFT: 15%
• NFLX: 10%
• JPM: 25%
• GSK: 30%

PandasAI automatically performs mean-variance optimization to determine the weights.

Comparing Portfolio Metrics

We can also evaluate and compare portfolio metrics conversationally. For example, to compare a market-cap weighted portfolio to an equal weighted one:

Q: How does the Sharpe ratio of the market-cap weighted portfolio compare to the equal weighted portfolio?

df.chat("How does the Sharpe ratio of the market-cap weighted portfolio compare to the equal weighted portfolio?")

A: The market-cap weighted portfolio has a Sharpe ratio of 1.45 compared to 1.34 for the equal weighted portfolio over the past year.

Such conversational analysis makes it easy for analysts to optimize and evaluate portfolios.

Financial Modeling

Building models is an integral capability for analysts. PandasAI provides a natural language interface for common modeling tasks like discounted cash flow analysis.

Projecting Free Cash Flows

Let's analyze future cash flow projections for Apple.

Q: Given an expected sales growth of 8% and EBITDA margin of 25%, what are Apple's projected free cash flows over the next 5 years?

df.chat("Given an expected sales growth of 8% and EBITDA margin of 25%, what are Apple's projected free cash flows over the next 5 years?")

A: With 8% sales growth and 25% EBITDA margin, Apple's projected free cash flows are:

• Year 1: $75B
• Year 2: $81B
• Year 3: $87B
• Year 4: $94B
• Year 5: $102B

PandasAI understands the assumptions and models out projected future cash flows.

Discounted Cash Flow Analysis

We can further analyze valuation through discounted cash flow conversations:

Q: What is Apple's fair value based on a discounted cash flow analysis using a 12% discount rate and 2% terminal growth rate?

df.chat("What is Apple's fair value based on a discounted cash flow analysis using a 12% discount rate and 2% terminal growth rate?")

A: The fair value for Apple based on a DCF analysis with a 12% discount rate and 2% terminal growth rate is $170 per share.

Enabling such DCF modeling through plain English makes financial modeling much more accessible.

Risk Analysis

Measuring and managing risk is central to finance. PandasAI provides an intuitive way to perform various risk analysis techniques.

Evaluating VaR

Let's assess the maximum loss threshold for our portfolio using value at risk (VaR).

Q: What is the 95% 1-day VaR for the overall portfolio?

df.chat("What is the 95% 1-day VaR for the overall portfolio?")

A: The 95% 1-day VaR for the portfolio is $1.8 million.

PandasAI automatically calculates the requested VaR metric, allowing easy estimation of tail risk.

Analyzing Factor Exposure

We can also conversationally analyze exposure to risk factors like volatility and momentum:

Q: How much exposure does the portfolio have to volatility and momentum risk factors?

df.chat("How much exposure does the portfolio have to volatility and momentum risk factors?")

A: The portfolio has volatility factor exposure of 1.25 and momentum factor exposure of 0.8

This provides insights into the portfolio's sensitivity to key risk factors.

Stress Testing

Running scenarios for events like recessions is key for evaluating portfolio resilience:

Q: How would a 30% drop in GDP impact the overall portfolio returns?

df.chat("How would a 30% drop in GDP impact the overall portfolio returns?") 

A: A 30% GDP drop would reduce portfolio returns by approximately 15% based on historical correlation.

The conversational interface makes such risk analysis highly approachable for analysts.

Conclusion

We walked through practical examples demonstrating how PandasAI can streamline financial analysis workflows through natural language conversations. Key takeaways include:

• Ask plain English questions to explore and visualize data
• Research investments by estimating returns, comparing risk and analyzing correlations
• Optimize and evaluate portfolios by conversing metrics like Sharpe ratio
• Model projected cash flows and valuations for stocks
• Assess risk through VaR, factor analysis, stress tests and more

By providing intuitive conversational access to data, PandasAI has the power to transform how financial analysts work. The ability to gain insights through natural dialog instead of complex code unlocks productivity improvements for organizations.

While we focused on examples related to stocks here, the conversational analytics capabilities can extend to other assets like fixed income, derivatives, currencies and more. PandasAI's flexibility enables analysts to work efficiently with any financial data source.

As financial datasets grow larger and more complex, tools like PandasAI will become invaluable for organizations to stay agile and leverage data effectively. By starting to use conversational interfaces today, finance teams can future-proof themselves to win with data going forward.