Using Python for Text Analytics in Accounting (Essay Sample)

Using Python for Text Analytics in Accounting
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Using Python for Text Analytics in Accounting
Research Question
Can stock returns be explained by sentiments extracted from 10-k reports?
Type of Research to Answer the Question
A Linear Regressions Engagement to Share Price Forecasting
Framing and Contribution of the Research
Take a look at the New York Times business section. When you read, you are expressing views on the different companies in the daily news. Your cerebral cortex allocates the text a rating of "sentiment" based on a beneficial, adverse, or neutral text rubric (Anand et al., 2020). Analysis of feelings is the comparable computer science of reading news. It is the systematic processing of attributes extracted from text mining words.
As can be seen from a glance at a newspaper page, text significantly outnumbers numerical information. Anecdotes, recollections, and quotations trump charts and graphs. Previously limited to price ratios and margins, financial analysis is undergoing a sentiment revolution (Anand et al., 2020).
Within Google Scholar, thanks to seminal publications, the phrase "sentiment analysis in finances" now produces 661,000 results. As shown, the text is tokenized, processed, probably stems, and classified (translated into words) (Anand et al., 2020). Texts from several publications, including (a) forums, blogs, and wikis, have now been incorporated into the literature; (b) headlines and studies reports.
The Imperative
The existing academic literature is primarily concerned with using sentiment to forecast stock market returns. Therefore, the literature has not evaluated if a textual analysis can predict future revenue, working capital, or influence for a company (Azhar et al., 2019).
Strategies and Tactics 
We examine whether the feeling expressed in the Management Discussion and Analysis (MD&A) Section of SEC Form 10-K provides a predictor of the foundations of a company during the subsequent filing period. Management's assessment of the company's current financial condition and prospects for growth is provided in the managerial conversation and debate section (Azhar et al., 2019).
The Worth
Our findings will aid in determining the accuracy of management's tone and portrayal of a firm's trajectory. From a practical point of view, we (1) offer microprocessor models for quantifiable financial firms to anticipate the economic situation of their investments, (2) direct insurance businesses to critical 10-K statements which help to assess the health of the balance sheet of a business, and (3) increase market volatile awareness among CEOs and CFOs. In selecting verbiage, corporate management responsible for accurate representations of the value of enterprise can now be more careful.
Data Analysis
Using edgarWebR, we scrape the 10-K annual reports of all S&P 500 companies in 2016. Then we carry out an NRC sentiment classification in Syuzhet using Saif Mohammad's Emotion Lexicon. According to Mohammad, "a combination of letters and their organizations with eight impulses is a vernacular of the National Research Council: anguish, fear, anticipation, confidence, surprise, despair, pleasure and revulsion" and two feelings (negative and positive)." NRC sentiment attributions were initially collected manually via Mechanical Turk and have since gained widespread adoption in computational intelligence and finance. We combined our analysis with the company's net income, operational cash flows, investments in retained earnings, fund flow financing, dividends, and stock-based remuneration from 2016 to 2017 (Azhar et al., 2019).
Overview of Public Opinion
We immediately notice a firm's inherent positivism, as evidenced by the fact that positive words outnumber negative words by a factor of two in the average 10-K. The information is expected to be an allusion to CEOs and the inordinate trust of CFOs in their predicting skills. Additionally, we keep in mind that 2016 saw a robust economy and bull market, which bolstered optimistic forecasts (Brink & Stoel, 2019).
The most frequently occurring words are those associated with Trust. The example is primarily attributed to the emphasis placed on financial regulation and moral principles. It is also unsurprising that Anticipation has the second-highest word count, given the MD&A section's forward-looking nature. Surprisingly, sadness and fear also occur frequently, most likely due to the plight of distressed energy and retail companies in 2016.
Plots & Visualizations of Correlation
The correlation plot between the various NRC emotions is now shown. We have no significant correlation between forecast and fear, joy and anger, surprise, and disgust, contrary to expectations. The more words related to fear, the more they relate to joy (a testament to the polarity of management). With the above-mentioned optimistic inclinations, excessive positivism appears to have "masked out" significant differences in emotional content underlying it. Furthermore, differences should be considered in the overall lengths of 10 ks, since longer 10 ks may contain more words of joy and fear (Azhar et al., 2019).
The plot of financial correlation is full of intriguing relationships compared to the emotional correlation plot. (1) Investment cash flows and operational cash flows, for example, are negatively correlated with net income. This year's lower bonuses lead to higher revenue figures for CEOs in the following year, unfortunately. (2) The correlation –0.16 between net revenue and the operating cash flow is also worthy of note. The negative correlation indicates that the S&P 500 companies use accruals and income easing extensively. (3) All other logical relations are relatively well established; increasing operating cash flows entail greater capacity for reinvestment, which leads to a strong correlation with increasing cash flows (Brink & Stoel, 2019).
Design Choices in the Research and Limitations
We immediately notice that various emotional categories are highly predictive of changes in the fundamentals of a business. These are the first findings of their kind, as all previous academic research has limited sentiment analysis to relationships with equity returns without regard for underlying fundamentals. Five significant observations summarize our findings (Brink & Stoel, 2019).
Each financial variable is associated with a unique set of sentiment categories. For example, Disgust exhibits a statistically significant negative correlation with the logged change in Net Income. It becomes insignificant, however, when the Operating Cash Flow of business changes. This finding is relevant to analysts who predicted specific parts of a company's balance sheet or remuneration policies; case-specific research requires an appropriate 10-K analysis approach and strategic planning conversations (Azhar et al., 2019).
Emotional changes are logically related to changes in fundamental firm fundamentals. A 1% rise in joy leads, for example, to a statistically meaningful increase in capital flow from investment and finance. CEOs who are optimistic about the future naturally increase capital expenditures and issue additional debt to fund speculative plant expansions. Investment banks can use this finding to establish relationships with clients expected to have higher future growth CAPEX (i.e., through M & M&A).
It is worth noting a few niche relationships. In terms of dividends, an increase in Surprise results in increased dividend distributions, whereas an increase in Disgust results in a reduction in dividend distributions of the same magnitude. Typically, 10-Ks that include Surprise does not include a great deal of Disgust. Increases in Anticipation result in decreased stock-based compensation. When CEOs express anxiety about the near future, they appear to be reducing existing incentive plans. Additionally, CEOs appear to be preceding near-term compensation in order to pursue long-term investment opportunities. This finding can help firms to determine future compensation plans (Brink & Stoel, 2019).
Positive and negative sentiment changes do not statistically significantly predict changes in corporate fundamentals. A data analyst should degrade such feelings into the more nuanced emotions previously discussed.
The above regressions have an average statistical R-squared of 3%. Although this is important, we now use a financial sentiment analysis machine learning approach to maximize the percentage of variance explained.
Major potential issues and "Plan B."
In this case, abnormal trends of the data listed would be a significant potential problem. Our plan B would then improve the efficiency of the AI.
Literature Review
Textual data have risen significantly in recent years in accounting research. This monograph defines and describes the standard text information and then shows the storage and analysis of textual data using the Python programming tongue to help researchers understand and use textual data. The memoir contains a sample code that replicates tasks of textual analysis in recent papers.
In the first section of the monograph, we discuss how to get started with Python. We begin by describing Anaconda, a Python distribution that includes the necessary libraries for textual analysis and how to install it. The Jupyter notebook is then introduced, a programming environment that facilitates researc...