Please find my research paper on Intelligent Portfolio Management via NLP Analysis of Financial 10-K Statements, published in the November issue of International Journal of Artificial Intelligence and Applications
The project attempts to analyze if the sentiment stability of financial 10-K reports over time can determine the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis. Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the 10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at predicting future returns, the framework uses the alphalens library to perform factor return analysis, turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its future mean returns.
Quandl end of day US Stock Prices database, Accessed: 2020-10
!pip install quandl
import quandl
quandl.ApiConfig.api_key = "YOURAPIKEY"
data = quandl.get(['EOD/AMZN', 'EOD/NKE'])
data.head()We test our hypothesis: Sentiment stability of financial 10-K report can be a potential trading signal, on a diverse portfolio of 7 stocks as below:
In order to extract financial 10-K reports of the stocks in our universe, we leverage a pre-defined SEC API and the CIK number of the stock. Details on how to extract the 10-K report from SEC EDGAR database and pre-process it can be found in this notebook.
You can find the PyTorch implementation of the framework here
Factor returns are a way to directly measure the returns of our portfolio if their weights were
determined purely by the alpha factor. Alphalens requires two mandatory arguments to
predict future mean returns: factors and prices. In this project, we consider cosine
similarity between two consecutive 10-K reports as factor data and year-end adjusted closing prices of the stocks in our portfolio as
pricing data to run against our factor data.
After generating the factor data frame and setting the pricing data, we pass both the arguments
in the alphalens’ method called get_clean_factor_and_forward_returns, which accepts factor
data, pricing data, quantiles, bins, and periods. This function generates a multi-indexed
merged data frame that is indexed by date at level 0 and followed by stock/asset at level 1. This
data frame contains the values for a single alpha factor, forward returns for each period, and
quantile/bin in which the signal belongs.
Figure 1 shows a plot between factor returns and time. As we can see from the graph, 10-K financial reports expressing the sentiment interesting and positive, yield the maximum returns. On the other hand, the forms that convey constraining , negative , and litigious resulted in the lowest returns. The following observation aligns with our hypothesis that performing NLP analysis on financial 10-K statements could predict future mean returns.
Since liquidity and transaction costs are dependent upon market conditions at the time of the
trade, it is challenging to simulate actual transaction costs when evaluating an Alpha factor. So a
useful proxy for these real-world constraints is to measure the turnover. The turnover
analysis estimates the fraction of the portfolio's total value getting traded in a period. One of
the ways to measure turnover is factor rank autocorrelation . Factor rank autocorrelation is a
way to measure how stable are the ranked alpha factors. In this case, stability refers to the fact that alpha ranks do not change much from period to period. Since trading is costly, we would
always prefer other factors to be the same, i.e., the alpha factor’s ranks do not change
significantly per period. A high factor rank autocorrelation is an indication that the turnover
is lower. A low or even a negative autocorrelation is a proxy to indicate a higher turnover. If
two alpha factors have similar quintile performance and similar factor returns, we would prefer
the one with lower turnover.
The reason for choosing alpha factor with lower turnover is that it makes it possible for us to execute trades if we have liquid stocks and reduce transaction costs. Excessive turnover could imply that our Alpha factor is only catching noise.
The Sharpe Ratio or risk-adjusted return is a critical metric in evaluating alpha factors. It is the
measure of excess portfolio return over the risk-free rate relative to its standard deviation.
Sharpe ratio helps us to compare the relative performance of alpha factors. One important thing
to note is that the Sharpe ratio is the key and not the magnitude of factor returns. Table 1 shows the Sharpe ratio of our alpha factors.
Usually, a ratio under 1.0 is considered sub-optimal. Sharpe ratio greater than 1.0 is acceptable to good by investors. A Sharpe ratio
higher than 2.0 is good, and investors deem a 3.0 or higher Sharpe ratio excellent.
Looking at the Sharpe ratio of our Alpha factor, we can see that the 10-K filing reports that
convey the sentiment interesting have the highest Sharpe ratio of 4.10, followed by the 10-K
documents that express a positive view with a Sharpe ratio of 1.02.
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