Company Evaluation based on Clinical Trial Volume
Information through BEAM Biotech & Pharma R&D Data

Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd. (MTEC)1

1. Introduction
The aim of this paper is to analyze clinical trial data, which is fed by Ozmosi as BEAM
BIOTECH & PHARM COMPANY R&D DATA, and their relationships to corporate
values, specifically focusing on the impact of clinical trials on sales forecasts and stock
prices. According to Open:FactSet Marketplace, BEAM Data contains over 130,000
industry-sponsored clinical trials, covering more than 1,00,000 point-in-time data
versions, across 6,000 companies with over 1,800 mapped to ticker symbols [Ozmosi].
The robust point-in-time data includes cleaned company names, drug names, Mechanismof Action, drug target information, FDA approved or novel status, disease area tags,

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1oncology biomarkers and various text rich data elements for NLP development ofcategories and indicators.
In light of the abundant information contained within the data, it is essential to first
conduct a foundational analysis using basic metrics such as the number of data points andother fundamental data characteristics. This initial approach will enable us to betterunderstand the underlying structure and patterns within the clinical trial data, which canthen be utilized to perform more complex and in-depth analyses in the subsequent stagesof our research. By focusing on these fundamental aspects, we aim to establish a solid
groundwork for the investigation of the relationships between clinical trials and corporate
values, ultimately contributing to a more comprehensive understanding of the impact ofclinical trials on sales forecasts and stock prices.
This paper is organized as follows: In Section 2, we conducted an analysis of theunderlying data properties, with particular emphasis on the number of trail test data bysome features. Section 3 describes the construction of features using test data counts andthe relationship between the constructed features and sales growth rates. Section 4
examines whether the factors created in the previous section have potential for use in
obtaining market returns. The conclusion, Section 5, summarizes the paper and offers
suggestions for improving the factor with additional data.

2. Data at a Glance
In this section, we will present the key data points used for our analysis. Figure 1-1
presents the annual distribution of data submitted up until 2022, focusing on thoserecords where the stock ticker for the primary sponsor is not null. After 2005, data
involves over 40k data until 2019. It is unclear why the amount of data for 2022 appears to
be undercounted. It is possible that the number of tests is actually low, but the possibilityof vendor data collection lag remains.
A notable trend becomes apparent when comparing the recent years to around 2010.
As shown in Figure 1-2, the proportion of data submitted by large corporations has been
decreasing over time. This could suggest a shift in the business landscape with potentiallymore room for smaller companies and start-ups to make an impact.

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Figure 1-1: Distribution of Data per Year (total count)

Figure 2-2: Distribution of Data per Year (percentage per company)

Figure 2 offers a compilation of the number of companies with ticker symbols
registered in the dataset after 2005. The figure offers a unique perspective on thechanging corporate landscape in the medical technology industry.
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Despite an overall decrease in the number of data records in the 2010s (as shown in
Figure 1), there has been an increase in the number of companies, with a peak observedin 2018. This seeming discrepancy suggests that while the total data submissions havegone down, the number of different companies represented in these submissions hasincreased.
This rise in company representation hints at a broadening of the medical technology
sector, with an influx of diverse businesses entering the field. It could suggest that while
each company might be contributing fewer data records, there are more companies
contributing to the data overall.

Figure 2: The Number of Companies

Clinical trials play a crucial role in medical technology development, providing
rigorous testing and validation stages for new therapies, procedures, or devices. Typically,
these trials are divided into several phases, each designed to answer different researchquestions.
Phase 1 trials focus on evaluating safety, identifying side effects, and determining
appropriate dosage. Phase 2 trials primarily aim to evaluate the effectiveness of thetreatment or procedure and further assess its safety. The trials in Phase 3 compare the4new treatment to current standard treatments, often involving larger participant groups. Ifsuccessful, Phase 3 trials may lead to the treatment or procedure gaining market approval.
Phase 4 trials occur post-market and monitor long-term effects. Phase 1 /Phase 2 andPhase 2 /Phase 3 are combined trials consisting of two phases [FDA]. Data marked as"Not applicable" and data with an empty phase field were aggregated as "N/A or blank".
In Figure 3, we count the number of trials organized by their respective phases after2013. We observe that the highest number of data records correspond to Phase 3 clinical
trials. This indicates that many medical technologies are already at the point where they
have undergone initial safety and efficacy evaluations when conducting clinical trial
recruitment, and their effectiveness is being assessed in a broader patient population,rather than being tested in clinical trials sequentially from Phase 1.

Figure 3: The Number of Trials by Each Phases

3. Feature Creation and Its Relationship with Future Sales
One of the important aspects to explore in the financial data research is the developmentof meaningful features and the identification of their potential relationships with key
financial metrics. In this chapter, we make an investigation into the creation of a5

particular feature – an index related to the yearly increase rate of trial numbers for eachcompany - and its correlation with future sales growth rate.
First, we tried to examine the relationship between the simple increase or decrease intrial numbers (without considering the rate of change) as a feature and the sales growthrate. Interestingly, this analysis did not reveal a significant correlation between these twovariables. This suggests that simply increasing the number of trials does not directlytranslate into higher future sales.
However, upon refining our approach, some intriguing findings came to light. When
we excluded companies with relatively low trial numbers (below the median for each year
in the case presented here) and considered the change in the percentile rank of the trial
numbers, a correlation with future sales growth emerged. Table 1 shows the correlation
between values calculated from population data, including some limited and all stages (asshown in first column), in December of each year from 2014 to 2021 and future sales
growth rates several years later. In these calculations, sales growth was determined basedon the last-twelve-months-data for the quarterly fiscal year ending in October, November
or December; note that correlations to growth rates after 2-year and 3-year are limited totrial data through 2020 and 2019, respectively.

Table 1: Correlations between Clinical Trial Data Count and Sales Growthcorrelation to sales growthAll
Before phase3
Phase3
No phase dataafter 1y6.17%
4.20%10.06%-2.43%

after 2y11.31%
19.00%
17.98%
6.90%

after 3y7.44%
-21.96%14.80%
7.01%

When focusing solely on Phase 3 trials, the correlation between the rate of increase intrial numbers and future sales growth was more stable and consistently higher comparedto other cases. This finding corroborates the significance of Phase 3 trials in the productdevelopment cycle and their impact on a company's sales growth. This outcome isintuitive as successful Phase 3 trials often directly pave the way for the product's launch
and market approval. Hence, an increase in the number of successful Phase 3 trials islikely to lead to a greater number of new products being launched, which in turn drives
sales growth.
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Figure 4 illustrates a scatter plot of the feature we created based on Phase 3 data
against the sales growth rate two years later. While the explanatory power of thiscorrelation is not overwhelmingly strong, it is statistically significant, as indicated by thep-value of 0.02.
It is essential to note that predicting growth rates in the healthcare technology sector
can often be challenging due to factors such as R&D investment amounts, regulatory
uncertainties, and the long timelines typically associated with bringing new medical
technologies to market.
Despite these complexities, our analysis was able to extract a statistically significant
correlation using clinical trial data, highlighting its utility in such predictive tasks. The
fact that this significant result emerged even in the complex landscape of the healthcaretechnology sector underscores the potential of clinical trial data as a valuable tool in
financial forecasting and decision-making processes.

Figure 4: Scatter Plot of the Feature and Sales Growth

It's important to acknowledge that the data set used for this analysis also includes
data that could predict the success rate of trials. If this prediction data is accurate, it could
be used to construct a feature with an even stronger correlation with future sales growth.
However, we did not use success rate data in this report. As such, the incorporationand evaluation of trial success rate data present an intriguing avenue for future research.

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By adding a success rate prediction component, it may be possible to further enhance thepredictive power of our sales forecasting.

4. Factor Effects on Stock Returns
The next step of our analysis seeks to ascertain whether the factor we have constructedhave an impact on the market. Although we have already shown the usefulness of thefactor in predicting a part of fundamental information, earning returns is still challenging
because market participants might have already factored in the corresponding
information.
To investigate this, we focused on U.S. corporations that rank within the top 3000 inglobal market capitalization. We segmented this population into three groups based onfactor quartiles (quartile 1 is the group with the smallest number of features, and quartile
3 is the group with the largest). Subsequently, we compared the median of future returns
over two periods—6 months and 1 year—taken at the end of June and December eachyear, for each group. Firms with no trial data are excluded. The results are in Figure 5.
This comparison shows that firms with higher factor values tend to have higher returns,and the relationship is particularly pronounced for one-year returns. However, thecorrelation observed for 6-month returns is weaker by comparison. This indicates that
information from clinical trial data is primarily factored into the price over a period of
about one year, which is consistent with the period of Phase 3 trials and suggests that themarket is factoring in the company's expected growth rate along with the increased
probability of product commercialization.

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median return(%)
20%
15%
10%
5%
0%
1

2
6M_RETURN

31Y_RETURN

Figure 5: Medium-term Returns by Quartile

The time series graph in Figure 6 shows the cumulative sum of 6-month returns forlong the companies in the third quintile and short the companies in the first quintile.
Returns have been increasing steadily except for the COVID-19 pandemic period. This
result indicates that the factor derived from the rate of increase in Phase 3 trial numbers
provides meaningful predictive power also in terms of stock returns. This suggest that themarket has not fully priced in this information.
Indeed, real-world financial markets are affected by a variety of causes, and there arenumerous hurdles to implementing a portfolio strategy based on it. While the factor
presents promising potential, it should be used in conjunction with other factors andwithin the broader context of a comprehensive, well-thought-out investment strategy.
Further in-depth analysis is needed, including incorporation into existing strategies.

9cumulative sum return(%)
160%
140%
120%
100%
80%
60%
40%
20%
Dec-22Jun-22

Dec-21

Jun-21

Dec-20Jun-20Dec-19Jun-19Dec-18Jun-18Dec-17

Jun-17

Dec-16Jun-16

Dec-15Jun-15

Dec-14Jun-14

Dec-13Jun-130%

Figure 6: Cumulative Sum of Spread Return for 3rd - 1st Quantile Portfolio

5. Conclusion
In conclusion, this analysis reveals the potential value of incorporating insights fromclinical trial data into financial forecasting and investment strategies within the healthcaretechnology sector. A key finding is the statistically significant correlation between the rateof increase in Phase 3 trials and future sales growth, suggesting that this metric can serveas an effective indicator of a company's future performance.
Importantly, our factor built around this metric demonstrates not only predictive
power over company fundamentals but also the ability to generate stable returns overtime. This implies that the market might not have fully priced in the information, offering
potential investment opportunities.
For further studies, we can use some related data as follows:I.

Success rates

II. Disease type
III. FDA approval data
IV. Patent information
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BEAM Data also provides success rates estimated by machine learning and FDA
approved or novel status etc. This information may improve the accuracy of the factor wemade, especially for predicting sales growth. On the other hand, too much confidence inthe commercialization of a product will cause the market to factor it in, so greater care will
be needed in its application to market returns.
In January 2023, FactSet also published a dataset named “FACTSET
STREETACCOUNT DRUG EVENTS & PIPELINE” on the FactSet Marketplace to help
evaluate companies in the healthcare sector [FACTSET]. According to Open:FactSet
Marketplace, the dataset provides various drug events by scanning all possible news
sources for FDA or EU drug approvals, litigation, high-impact events, newly marketedgenerics, and newly approved form/dosage. Combining these data such as patent andexclusivity expiration dates, with clinical trial data is expected to provide further insight.
With the increasing prominence of the biotechnology and pharmaceutical sectors,
understanding the connections between clinical trial outcomes and corporate performanceis vital for investors and stakeholders alike. Our findings highlight the utility of clinical trial
data as a new source of information for financial analysis in the healthcare technology
sector and provide clues to strategies that may yield more accurate forecasts and better
investment results.

References
FACTSET. FACTSET STREETACCOUNT DRUG EVENTS & PIPELINE.
OPEN:FACTSET MARKETPLACE. [Online]
https://go.factset.com/marketplace/catalog/product/factset-streetaccount-drug-eventsand-pipeline.
FDA. U. S. Food & Drug Administration. [Online] https://www.fda.gov/patients/drugdevelopment-process/step-3-clinical-research.
Ozmosi. BEAM BIOTECH & PHARM COMPANY R&D DATA. OPEN:FACTSET
MARKETPLACE. [Online] https://go.factset.com/marketplace/catalog/product/beam.

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