Published Sep 17, 2020

How Traders Used Google Searches To See The Economic Recovery In Real Time

Ben Breitholtz delves into the cutting-edge world of alternative data, highlighting the transformative role of real-time Google search trends in understanding economic recovery, and how mobility data and unstructured datasets are reshaping market predictions for savvy investors.
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Episode Highlights

  • Search Data

    explains how Google search data is accessed and processed to provide valuable insights for investors. Using Google Trends, he accesses data through an API, avoiding specific search terms to capture a broader range of related queries 1. Google categorizes searches into over 140 categories, such as urban transportation, and topics like inflation, allowing for comprehensive analysis across different regions 1. Breitholtz highlights the importance of decomposing search activity into trend, seasonality, and shock components to understand consumer behavior and economic shifts 2.

       

    Consumer Insights

    Understanding consumer intentions through search activity offers a more reliable alternative to traditional surveys. notes that surveys often fail to capture honest consumer intentions due to respondents' reluctance to disclose financial hardships 3. In contrast, search data provides a candid view of consumer interests, as individuals are more truthful in their online queries 3. This method has gained traction as internet access has expanded, offering a more accurate reflection of consumer behavior and intentions 4.

       

    Real-Time Demand

    The rapid pace of economic changes has heightened the demand for real-time alternative data. and discuss how traditional economic indicators are insufficient in capturing swift market shifts, leading to increased reliance on real-time data like Google searches and restaurant bookings 5. This data provides immediate insights into economic recovery and consumer behavior, becoming essential in investment processes 6. As Breitholtz points out, understanding and adjusting for data quirks, such as seasonality, is crucial for accurate analysis 6.

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