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In this video we demonstrate how easy-to-use and powerful the ThemeX tool is on the Unstructured Data Terminal. We use ThemeX to search filings, transcripts and social media for macro insights on the impact of a recession.
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Earnings performance and indicators are one of the most looked at metrics within the finance community when choosing to invest in a security. Paired with earnings announcements are a conference calls where management comments on the company’s performance in a prepared statement. Afterwards investors can ask questions about specific components of the company and its outlook. This section is where you can gauge the finance community’s thoughts on a security based on the tone of conversation and the language used.
SMA converts Earnings Call Transcripts to JSON format and applies proprietary sentiment metrics. We calculate sentiment, word count, and section metrics from the Q&A section of the Earnings Call Transcripts to see relationships with subsequent price returns.
Since Earnings Calls typically occur 4 times in a calendar year for each company, this analysis uses quarterly holding periods. Portfolios are determined by the value of sentiment and word count metrics associated with the company’s Earnings Call Q&A section. At the end of each month, we look at any new Earnings Call Transcripts submitted in the past month and rebalance the portfolios. Sentiment and word count values can be held for a company for up to 4 months or until a new Earnings Call occurs. Portfolios are quintiles with Quintile 5 being the highest 20% of the metric and Quintile 1 being the lowest 20%. Each portfolio is rebalanced at market close of the last trading day of each month. Securities must have a Price > $5 to be included in the portfolio to remove volatility in returns from penny stocks.
The first metric is Sum Sentiment. SMA looks at every word and phrase for sentiment and has a score when identified. We take the total sum of all the sentiment from the words and phrases in the Q&A section. We expect companies that have a higher Sum Sentiment to outperform the market while companies with lower Sum Sentiment underperform.
Sum Sentiment quintiles performed as expected. There is a monotonic signal showing companies with high Sum Sentiment in the Q&A section of their Earnings Call are more likely to exceed market expectations. On the other side, companies with low Sum Sentiment underperform. The tone, or sentiment, of investors questions as well as the response from management, projects how people feel about the future of the company which affects the security’s future price returns.
A theoretical Long/Short between Quintile 5 and Quintile 1 has a steady increase over time with not much drawdown. This theoretical Long/Short portfolio passes a significance test at a 5% level. Each Quintile has on average 484 securities, which comes to an average of 2,420 securities rebalanced each month.
Next, we look at Hits per Word. Hit Count is the number of words or phrases tagged with a sentiment score and word count is the total number of words in the Q&A section. We take Hit Count divided by Word Count to get hits per word. This metric is the percentage of the document scored with sentiment. This is helpful in identifying which companies discuss relevant components and crucial to the firm’s outlook, rather than blathering about unrelated or unnecessary topics.
The monotonic nature of this graph demonstrates the predictive power of this factor. Companies that have a higher percentage of words/phrases with sentiment in their document tend to outperform the market. These firms are letting investors know insightful information rather than boilerplate responses. Investors respond positively to truthful management insights and respond negatively to Earnings Calls where management doesn’t convey the company’s vision.
The Questions and Answers section of an Earnings Call contains crucial information that typically isn’t being quantified. With SMA’s new Earnings Call Transcripts product, these calls now have quantitative metrics to its qualitative insights. This information can now be tapped to project companies price returns with an investment horizon of multiple months. The analysis above doesn’t even account for the alpha immediately after the earnings release because trades are executed at market close of the last trading day of each month. That means there could be 20+ days of alpha from the new Earnings Call to when the strategy acts on that information. This conservative approach still exemplifies the importance of these factors to the finance community.
If you are interested in learning more about how SMA’s Earnings data can help your trading strategies, please email us at contactus@socialmarketanalytics.com or schedule a demo using this link.
One of the most closely followed events on the corporate calendar are earnings calls. This gives executives the opportunity to comment on earnings and answer questions from those outside of the company. Using our patented Natural Language Processing, Social Market Analytics scores Earnings Call Transcripts in real time and creates metrics based on sentiment, word count, and section count. For this research, we look specifically at the question-and-answer section of call transcripts. The theory is that isolating the section of the call where executives aren’t controlling the topic of conversation will give a more accurate assessment of the sentiment surrounding earnings results. We use Sum of Sentiment to quantify the positivity of the call. Sum of Sentiment adds all the words and phrases tagged in the section with sentiment. The following histogram shows the distribution of the Sum of Sentiment variable.
The Sum of Sentiment is centered around 3.5 and is roughly normal with a heavy tail skewing right. As executives of companies want to express good things to come, it makes sense that the sum is predominantly positive. Still some earnings calls are more positive than others. Based on the distribution of sentiment, we defined an extremely positive earning call as having a sum greater than 5 and a negative earning call as having a sum less than 1.5. These thresholds give a roughly equal number of instances over the past 14 years. We took these thresholds and compared returns for different time periods following the Earnings Call. Time periods were subsequent Open-to-Close; subsequent Close-to-Close, subsequent week return, subsequent month return, and subsequent quarter return. Since earnings calls are spaced throughout the year, it is difficult to compound the subsequent returns. Instead, we will be looking at average excess returns for each threshold. The excess return for each security is calculated by subtracting the SPY return of the same time frame from the securities return. Our hypothesis is that the average excess returns for the extremely positive earnings calls will be higher than those for negative earnings calls. We calculated returns of all instances since the end of 2009.
For long-term holdings, the average excess return for high sentiment earnings call companies was strongly positive. On the contrary, negative sentiment earning calls company returns were negative for every time frame. Quarterly returns highlight the importance of a positive earnings call as the average excess return is close to .8% higher than negative. The biggest takeaway from different time periods was the large difference in returns between the next Open-to-Close and the next Close-to-Close, especially those with a high sentiment. Entering on the subsequent close rather than open dropped the excess returns by .8% and made them negative. The next close to close returns were negative regardless of the sentiment threshold. Waiting to enter removed the benefit of positivity from high sentiment. We looked at the returns of these two-time frames with high sentiment over the past 13 years.
Looking at the past 14-year performance: The Open-to-Close excess returns were positive 12/14 years and Close-to-Close excess returns were positive 4/14 years. The two negative years for the Open-to-Close also came during an abnormal period of the COVID-19 pandemic. Immediate open to close return benefits from the high sentiment far more than the close to close. Therefore, there is a premium on knowing the sentiment of an Earnings Call in real time and entering the next open to maximize short term returns. Instead of manually reading earnings calls to gain insights, traders can use the sentiment summarized by Social Market Analytics to select positions. Waiting to enter on positive earnings calls generally hurts the short-term returns. Social Market Analytics’ scoring on Earning Calls can give traders the advantage of entering the position as quickly as possible for immediate returns, while also providing a holding option for quarterly returns.
If you are interested in learning more about how SMA’s Earnings data can help your trading strategies, please email us at contactus@socialmarketanalytics.com or schedule a demo using this link.
Explore sentiment on earnings calls and all corporate filings on the SMA Unstructured Data Terminal below.
The target of this research was to find an indicator that helps predict the direction of the overall US Equity market for the next week using sentiment data from the previous week. The hypothesis is when there is high volatility in sentiment over the previous week, which means investors have differing opinions, the subsequent week overall market performance will underperform. When volatility on sentiment is low or neutral, the crowd has reached a consensus and the general market will outperform over the next week. The sentiment metric used to represent volatility is Raw-Volatility in SMA’s S-Factor data feed, which captures the volatility of the sentiment from Twitter conversations. All Raw-Volatility data points were taken from the 3:40 pm ET timestamp (20 minutes before the market close). We calculated the summation of Raw-Volatility for each date as a proxy to represent the volatility of Twitter social sentiment on the entire market. The exact calculation is as follows, where “N” is the number of companies with sentiment on that date and “D” is the date:
We then created a 7-day standardized volatility using a 91-day benchmark:
This Z_Volatility score follows a roughly normal distribution.
Using the S&P 500 ETF Trust (SPY) as a proxy of general market performance, we then look at the relationship between Z_Volatility and SPY’s return series. The daily close-to-close return is calculated as:
Hypothesis: When Z_Volatility for the previous closing Date is high, the subsequent market performance will be lower. When Z_Volatility is low or neutral, the next day’s market performance will be higher.
To test this, our strategy is to open short position of SPY when Z_Volatility > 1. When Z_Volatiltiy is =< 1, the portfolio treats SPY as a long position. This hypothetical portfolio is then compared to SPY over the past 10 years:
Prior to the COVID-19 pandemic, which began in early 2020, SPY outperformed the modified portfolio. However, since then the behavior of this factor changed drastically. Here is the same graph as above starting in 2020:
Taking a closer look, the separation since the beginning of 2020 is quite significant. Adding a short position to SPY when volatility on sentiment is high, has enhanced the portfolio’s return. Even though many of the days will maintain a long position, the Z-Volatility is predictive of downturns in the market since 2020. Traders could use this metric as an indicator to stay out of the market, or at the very least trade with more caution. The COVID-19 Pandemic led to a large amount of uncertainty surrounding the stock market and the direction its heading. A high Z_Volatility score indicates the public’s opinion is more uncertain about the direction of various stocks. This research shows the value of sentiment from Social Market Analytics in predicting macro-level events and price movements.
If you are interested in learning more about how SMA’s S-Factor data can help your trading strategies, please email us at contactus@socialmarketanalytics.com or schedule a demo using this link.