Levi Strauss Write Up & Analysis
By: David Barone, Tameem General, Jackson Rehder, Ben Trainer, & Teymour Younes
1. Regression Analysis: The regression analysis shows a strong and statistically significant
relationship between Levi Strauss’s projected unit sales and gross sales, with an R² of
0.9631. This indicates that roughly 96% of the variation in gross revenue can be
explained by the unit sales projections. For Levi’s as a company, this finding reinforces
the reliability of their bottom-up forecasting process, which starts with unit-level
projections and adjusts according to average unit retail (AUR). The estimated slope of
23.56 suggests that each additional unit sold contributes approximately $23.56 to gross
sales, which is constant with the firm’s use of AUR to estimate their revenue. The
intercept of about $10.2 million may reflect a baseline revenue from things like fixed
contracts or other sources that aren’t directly tied to unit sales. This analysis also supports
the structure of Levi’s AI-based forecasting models, which incorporate projected unit
sales as a key input.
Regression Analysis of Levi Sales/Projections
Data
Gross Sales (numbers in thousands)
350 000
300 000
y = 23,565x + 1E+07
R² = 0,9631
250 000
200 000
150 000
100 000
50 000
0
0
2 000
4 000
6 000
8 000
10 000
12 000
14 000
Sales Projections (numbers in thousands)
2. Beta is a measure of a stock's sensitivity to market movements and is a key component in
the Capital Asset Pricing Model (CAPM). In this report, we estimate the beta of Tesla
Inc. (TSLA) using regression analysis of monthly returns over two-time horizons: the
past 5 years and the past 3 years. The benchmark index used for market returns is the
S&P 500 (SPX)
Data collection was using Monthly Adjusted Closing Prices Tesla (TSLA) and S&P 500
(SPX) for the periods:
 5-Year Period: April 2020 to April 2025
 3-Year Period: April 2022 to April 2025
Regression Analysis for 3 Year Period Data:
Figure 1: Line chart with TSLA and SPX prices for last three years
Figure 2: Scatter Plot of TSLA vs SPX Monthly Returns (3 Year Period)
Graph shows Tesla, compared to the overall market, has Beta (Slope) at 1.65 in the last three
years.
Figure 3: Line chart with TSLA and SPX prices for last five years
Figure 4: Scatter Plot of TSLA vs SPX Monthly Returns (5 Year Period)
Graph shows Tesla, compared to the overall market, has Beta (Slope) at 2.37 in the last five
years.
The difference between the two beta estimates—1.65 over the past 3 years versus 2.37
over the 5-year period—indicates a material shift in Tesla’s systematic risk profile. A
higher 5-year beta suggests that Tesla's stock was significantly more volatile and
responsive to market movements earlier in the period, likely driven by heightened
speculation, growth uncertainty, and earnings volatility during its rapid scale-up phase. In
contrast, the lower 3-year beta reflects a potential stabilization in Tesla’s business
fundamentals and a dampening of market overreaction to company-specific news. From a
regression standpoint, this implies a reduction in the slope coefficient of the linear
relationship between Tesla’s excess returns and the market’s excess returns, suggesting
that Tesla's returns have become less levered to market-wide shocks. This could be
attributed to Tesla’s growing maturity, improved operational performance, and broader
investor base, which together contribute to less erratic return behavior. In CAPM terms,
the downward shift in beta directly reduces the expected return demanded by investors
for holding Tesla's stock, assuming a constant risk-free rate and market risk premium.
3. The COVID-19 crisis changed the environment dramatically which the algorithm could
not correctly respond to due to the data it was trained on during stable economic periods.
The forecasting model relies on patterns in past data sets to identify new patterns from
fresh data. While the data points used are not mentioned, it is highly likely that the
model had an “internal lens”, focusing on business KPIs while ignoring macro-economic
factors such as unemployment or GDP growth. The model likely lacked the contextual
awareness or ability to predict and adapt to the sudden drop in consumer demand and
supply chain interruptions. Nevertheless, the shift the COVID-19 pandemic had on the
world created a ripple effect which no data set had trained itself and disrupted all
historical patterns.
4. Forecasts from algorithms are built upon historical data and become accurate once they
pinpoint patterns of that historical data and use incoming data to predict the next quarter.
Reliability comes down to how similar the current environment is to when the historical
data was recorded. Given the new administration’s pace in implementing and repealing
tariffs, there is reason for caution when relying on algorithmic forecasts for the 3rd and 4th
quarter of 2025. Significant shifts are likely, and these changes can create a volatile
environment that departs from market trends. As seen in Levi’s case, algorithms cannot
adapt to rapidly changing factors in volatile times due to the contrast in stability from
when the historical data was recorded. Thus, if the economic environment does not
change significantly, the algorithm will provide accurate forecasts. If not, the computer
driven forecast will likely be inaccurate.