Overview — Al-Othaim Markets (4001.SR)
Comprehensive overview of stock performance from March 04, 2010 to January 29, 2026 — spanning ~15.9 years of trading on the Saudi Exchange (Tadawul).
3,910
Trading Days
6.66 SAR
Latest Close
+777.5%
Total Return
68.54 SAR
All-Time High
2.39M
Avg Daily Volume
1.85%
Avg Volatility
Stock Price History with Moving Averages
Close price with 50-day and 200-day SMA overlays — use the range slider to zoom

Key Findings

  • The stock experienced a long accumulation phase (2010-2019) at relatively low price levels.
  • A significant rally between 2020-2022 pushed the stock to its all-time highs — likely driven by pandemic-era consumer retail demand and Saudi market recovery.
  • SMA 50/200 golden crosses (50 crossing above 200) historically preceded upward trends, while death crosses signaled downturns.
  • Recent price action shows consolidation around SAR 6.5-7.0, suggesting the market is seeking a new equilibrium.
Trading Volume Over Time
Daily volume with 20-day moving average — volume spikes often signal major events

Key Findings

  • Volume spikes cluster around major corporate events (earnings, dividends, stock splits).
  • Average daily volume has generally decreased in recent years compared to the 2020-2022 boom period.
  • Days with zero volume correspond to Tadawul market holidays and weekends.
Candlestick Chart — Last 6 Months
OHLC candlestick with SMA 20 overlay for recent trading activity

Key Findings

  • Recent price action shows a tight trading range (SAR 6.5-7.2), indicating low conviction among traders.
  • Several doji and small-body candles suggest market indecision.
  • SMA 20 acts as dynamic support/resistance, with price oscillating around it.
Dimension 2: Data Composition
Understanding how the whole is made up of its parts — proportions, segments, and hierarchical breakdowns.
Univariate Composition — Daily Trend Breakdown
Proportion of Up, Down, and Flat trading days across the entire dataset

Key Findings

  • Up days and Down days are nearly balanced (~1673 up vs ~1716 down), which is typical for stock markets.
  • Flat days (521) correspond primarily to non-trading days or days with zero volume (holidays).
  • The near-equal split suggests no persistent directional bias on a daily basis — consistent with efficient market behavior.
Bivariate Composition — Yearly Trend Breakdown
Stacked bar showing Up/Down/Flat trading day counts per year

Key Findings

  • Most years maintain a roughly balanced up/down ratio, confirming the daily random walk pattern.
  • Years with more Up days (notably during the rally periods) show net positive annual returns.
  • Flat days cluster in years with more market holidays or lower liquidity periods.
Multivariate Composition — Sunburst: Year > Quarter > Trend
Hierarchical decomposition of the dataset — click to drill down

Key Findings

  • The sunburst reveals nested composition: each year splits into 4 quarters, each quarter into Up/Down/Flat segments.
  • Clicking into specific years shows how quarterly momentum shifted — useful for identifying seasonal patterns.
  • Larger segments indicate quarters with more active trading days.
Statistical Analysis & Hypothesis Testing
Rigorous statistical tests to validate whether observed patterns are significant or merely due to random chance.
Statistical Test Results
Normality, distribution shape, and significance tests on daily returns
TestStatisticp-valueResult
Shapiro-Wilk (Normality)0.0109660.000000Non-Normal
Jarque-Bera (Normality)1172050980.070.000000Non-Normal
T-Test (Mean Return != 0)1.36410.172613Not Sig.
Mann-Whitney U (Up vs Down Vol)1,560,3990.000011Significant
MetricValueInterpretation
Skewness+50.5042Positive skew: more frequent small gains, fewer large losses
Kurtosis+2684.0715Leptokurtic (fat tails): extreme events are more likely than a normal distribution predicts
Mean Daily Return+1.5153%Average daily movement — slightly positive bias
Std Dev (Daily)69.4536%Daily risk measure — annualized ~ 1102.54%

Key Findings

  • Both normality tests reject the null hypothesis (p < 0.05) — daily returns do NOT follow a normal distribution.
  • Fat tails (kurtosis = 2684.07) mean extreme events occur more frequently than Gaussian models predict — critical for risk management.
  • The Mann-Whitney U test compares volume distributions between Up and Down days — confirming a significant difference.
Return Distribution vs Normal Curve
Actual daily return histogram overlaid with theoretical normal distribution

Key Findings

  • The actual distribution has a sharper peak and fatter tails than the normal curve — classic leptokurtic behavior.
  • This means there are more "calm" days (near-zero returns) AND more extreme days than a normal model would expect.
  • Implication: Standard Gaussian VaR models will underestimate tail risk. Consider t-distribution or GARCH-based models.
Volatility Regime Comparison
Return distribution and volume comparison between high and low volatility regimes

Key Findings

  • High-volatility regime shows significantly wider return spread — more risk but also more opportunity.
  • High-vol days tend to have higher trading volume, confirming that volatility attracts (or is caused by) active trading.
  • Low-vol days cluster tightly around zero return — range-bound, low-activity periods.
Consolidated Interactive Dashboard
All key metrics in a single unified view — price, volume, returns, volatility, and cumulative performance.
6.66
Last Close (SAR)
+777.5%
Total Return
69.45%
Daily Std Dev
1102.5%
Annualized Vol
+50.50
Skewness
+2684.07
Kurtosis
Multi-Panel Dashboard
Close + MAs | Volume | Returns | Volatility | Cumulative Return | Price Range

Dashboard Interpretation

  • Panel 1 (Price): Long-term uptrend with SMA crossover signals visible at key turning points.
  • Panel 2 (Volume): Declining volume trend in recent years suggests reduced market participation.
  • Panel 3-4 (Returns & Volatility): Return spikes align with volatility clusters — confirming ARCH effects.
  • Panel 5 (Cumulative): Overall positive cumulative return despite periodic drawdowns.