Insights from Luxury Watches on Stock Market Trends
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Chapter 1: Understanding the Watch Price Index
Can luxury timepieces serve as indicators for stock market trends? Within the complex sphere of financial markets, unconventional data sources can uncover distinctive patterns.
The concept of the Watch Price Index (WPI) investigates whether the prices of high-end watches are linked with stock market fluctuations, acting as indicators of economic vitality and consumer confidence. Luxury watches, known for their resilience in value, may reflect changes in economic sentiment. For example, an increase in watch prices could suggest consumer optimism, potentially coinciding with bullish stock market trends. In contrast, declining prices may signal financial caution, aligning with bearish market conditions.
This article delves into the ChronoPulse Watch Price Index as a viable predictor of stock market trends. The ChronoPulse index aggregates data from over 600,000 luxury watch transactions, focusing on 14 prominent brands and 140 models that have a significant presence in the secondary market. With two decades of data sourced from Chrono24, this index provides valuable insights into market dynamics and current pricing trends.
As of the latest data, the index has experienced a downturn of 8.53% over the past year. To kick off our analysis, we will extract data from this chart and construct a simple 7-day moving average to smooth out the data points and visualize the trends.
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From the chart, it's evident that the index has been on a steady decline since the year's outset, prompting curiosity about its correlation with popular benchmarks such as SPY and QQQ.
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Section 1.1: Correlating ChronoPulse with Benchmark Indexes
To visualize the correlation, we will normalize the benchmark indexes' values alongside their daily closing prices.
ChronoPulse vs SPY Normalized Correlation
ChronoPulse vs QQQ Normalized Correlation
Interestingly, the watch index displays a relatively strong negative correlation with both benchmark indexes. Next, we will compare it against various stocks to examine the relationships further, ensuring a diverse selection to minimize bias.
While correlation illustrates the relationship between two variables, we will also conduct a statistical hypothesis test to assess whether one time series can predict another. This analysis won't prove causality but will examine predictability.
Subsection 1.1.1: Example with NIO Stock
Using the stock symbol NIO, we will perform a Granger Causality Test. Given that stock market data typically spans five days a week, we will analyze five lags to determine if there's statistical evidence that the watch index can predict the closing price of the asset.
The results of the Granger Causality Test indicate that the watch index holds predictive power over the asset's closing price, especially at specific lag values. Here’s a summary of the findings:
- Lag 1: The p-values are significantly below the 0.05 threshold (ranging from 0.0077 to 0.0083), suggesting a statistically significant relationship at this lag.
- Lag 2: P-values hover around 0.0442 to 0.0473, indicating a significant relationship, albeit weaker than at lag 1.
- Lag 3: P-values exceed 0.05, showing no significant relationship at this lag.
- Lag 4: P-values fall below 0.05 again, indicating a significant relationship.
- Lag 5: P-values are near or slightly above the threshold (ranging from 0.0497 to 0.0593), suggesting a borderline significant relationship.
In summary, these results imply that the watch index exhibits a Granger-causal relationship with NIO stock prices, particularly at lags 1, 2, and 4. This indicates that past values of the watch index provide valuable insights for predicting future closing prices.
The findings may also support the economic rationale connecting the Watch Price Index and NIO’s stock prices. NIO, positioned within the luxury, technology, and sustainability sectors, may reflect broader economic trends that affect luxury consumption and technological innovation.
Consumer confidence, often mirrored in luxury goods spending (like high-end watches), could also serve as a predictor for the demand for luxury electric vehicles.
Chapter 2: Validating Predictive Power
To further validate the robustness of the Granger causality relationship, we can perform Out-Of-Sample Testing by dividing the data into a training and testing set. Here, we implement a 20% train/test split and optimize the code to identify the optimal moving average window that maximizes correlation between the assets.
The combined results from the Granger Causality Test and out-of-sample correlation suggest that past values of the Simple Moving Average (SMA) may hold useful information for anticipating future stock prices. It’s important to remember that Granger Causality tests for predictive power in a statistical context, rather than implying direct causation.
We can devise a straightforward mean reversion trading strategy between the normalized asset and the optimal SMA, leading to significant findings.
In conclusion, our investigation into whether the Watch Price Index, represented by ChronoPulse, can forecast stock prices reveals compelling insights. The Granger Causality Test results indicate that the watch index possesses predictive power over stock prices, particularly for NIO, at specific lag values.
This aligns with the economic rationale that trends in luxury goods, like high-end watches, can reflect broader market sentiments, potentially influencing sectors such as luxury electric vehicles.
Additionally, we have illustrated how to leverage such signals to construct a simple trading strategy. For those interested in exploring various trading strategies or alternative signals, further resources are available.
Happy trading!
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