{"id":91928,"date":"2024-09-26T15:07:44","date_gmt":"2024-09-26T19:07:44","guid":{"rendered":"https:\/\/www.bloomberg.com\/professional\/insights\/data\/economic-nowcasting-managing-multiple-complex-datasets-for-better-investment-decisions\/"},"modified":"2024-09-27T10:00:13","modified_gmt":"2024-09-27T14:00:13","slug":"economic-nowcasting-managing-multiple-complex-datasets-for-better-investment-decisions","status":"publish","type":[3762],"link":"https:\/\/www.bloomberg.com\/professional\/insights\/data\/economic-nowcasting-managing-multiple-complex-datasets-for-better-investment-decisions\/","title":{"rendered":"Economic nowcasting: Managing multiple complex datasets for better investment decisions"},"content":{"rendered":"<div  class=\"bbg-row-container\">\n    <style>section[data-anchor=row-6a09249f97ae7]::before {\n\t\t\t\tbackground-color: #eeeeee;\n\t\t\t}<\/style>\n    <section class=\"bbg-row bg--custom-color  bg--eeeeee text--black bbg-row--full-bg-bleed\" data-anchor='row-6a09249f97ae7'>\n        \n        \n        <div\n            class=\"bbg-row--content\"\n                    >\n            \n            <p><div\n    class=\"bbg-column bbg-column--width-2\"\n    style=\"\"\n    >\n    \n<\/div><div\n    class=\"bbg-column bbg-column--width-8 bbg-column--valign-bottom\"\n    style=\"\"\n    >\n    <p>    <ul class=\"bbg-categories_list\">\n                    <li>\n                <a href=\"https:\/\/www.bloomberg.com\/professional\/insights\/category\/data\/\" rel=\"category tag\">\n                    Data\n                <\/a>\n            <\/li>\n                    <li>\n                <a href=\"https:\/\/www.bloomberg.com\/professional\/insights\/category\/financial-services\/\" rel=\"category tag\">\n                    Financial Services\n                <\/a>\n            <\/li>\n                    <li>\n                <a href=\"https:\/\/www.bloomberg.com\/professional\/insights\/category\/markets\/\" rel=\"category tag\">\n                    Markets\n                <\/a>\n            <\/li>\n                    <li>\n                <a href=\"https:\/\/www.bloomberg.com\/professional\/insights\/category\/trading\/\" rel=\"category tag\">\n                    Trading\n                <\/a>\n            <\/li>\n            <\/ul>\n<div\n    class=\"bbg-spacer\"\n        style=\"height: 24px !important\"\n    >\n<\/div>    <h1 class=\"bbg-metadata bbg-metadata--title\">Economic nowcasting: Managing multiple complex datasets for better investment decisions<\/h1>\n<\/p>\n\n<\/div>\n\n\n                    <\/div>\n    <\/section>\n<\/div>\n\n<div  class=\"bbg-row-container\">\n    <section class=\"bbg-row  text--black row-padding--top-compact row-padding--bottom-none bbg-row--full-bg-bleed\" data-anchor='row-6a09249f9ed0d'>\n        \n        \n        <div\n            class=\"bbg-row--content\"\n                    >\n            \n            <p><div\n    class=\"bbg-column bbg-column--width-2\"\n    style=\"\"\n    >\n    \n<\/div><div\n    class=\"bbg-column bbg-column--width-8\"\n    style=\"\"\n    >\n    <p><div\n    class=\"bbg-spacer\"\n        style=\"height: 40px !important\"\n    >\n<\/div><div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<p><strong>Bloomberg Professional Services<\/strong><\/p>\n\n<\/div>\n    <p class=\"bbg-metadata bbg-metadata--date\">September 26, 2024<\/p>\n<\/p>\n\n<\/div>\n\n\n                    <\/div>\n    <\/section>\n<\/div>\n\n<div  class=\"bbg-row-container\">\n    <section class=\"bbg-row  text--black row-padding--top-none row-padding--bottom-none bbg-row--full-bg-bleed\" data-anchor='row-6a09249fa1a9a'>\n        \n        \n        <div\n            class=\"bbg-row--content\"\n                    >\n            \n            <p><div\n    class=\"bbg-column bbg-column--width-2\"\n    style=\"\"\n    >\n    \n<\/div><div\n    class=\"bbg-column bbg-column--width-8\"\n    style=\"\"\n    >\n    <p><div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<p>Buy-side company research analysts have unprecedented access to large, complex datasets. And they also face challenges in managing all that data to evaluate investment strategies and understand potential returns.<\/p>\n<p>Quantitative research is transforming the investment landscape. Accessing large and complex datasets \u2013 along with advanced compute infrastructure and tooling \u2013 is becoming a necessity for any data-driven investment professional. Bloomberg has developed <a href=\"https:\/\/www.bloomberg.com\/professional\/products\/bloomberg-terminal\/research\/bquant\/\">BQuant Enterprise<\/a>, a cloud-based analytics platform specifically designed for quantitative analysts and data scientists in the financial markets.<\/p>\n<p>The platform provides broad access to Bloomberg\u2019s financial datasets and services within a sandbox environment, scalable compute resources, and a modern developer ecosystem, enabling efficient execution of data and time-intensive projects.<\/p>\n\n<\/div>\n<div class=\"bbg-interstitial\" aria-label=\"interstitial\" tabindex=\"0\">\n\t<style>\n\t\t.bbg-interstitial #card_1.bbg-card_hasCta{\n\t\t\tbackground:rgba(0,0,0,0);\n\t\t\tpadding: 104px;\n\t\t\t\n\t\t\t\n\t\t}\n\t\t.bbg-interstitial #card_1.bbg-card_hasCta .bbg-card__content, .bbg-interstitial #card_1.bbg-card_hasCta .bbg-card__content p{\n\t\t\tcolor:inherit;\n\t\t}\n\t\t@media (max-width: 768px) {\n\t\t\t.bbg-interstitial #card_1.bbg-card_hasCta{\n\t\t\t\tpadding: 80px 32px;\n\t\t\t}\n\t\t}\n\t\t@media (max-width: 480px) {\n\t\t\t.bbg-interstitial #card_1.bbg-card_hasCta{\n\t\t\t\tpadding: 80px 18px;\n\t\t\t}\n\t\t}\n\t<\/style>\n\t<div class=\"wpb_content_element bbg-card  bbg-card-dark bbg-card_hasCta has_interstitial\" id=\"card_1\" data-card_type=\"no_image\">\n  \n  \n  <div class=\"bbg-card__innerwrapper\">\n    <div class=\"bbg-card__content\">\n      \n      \n                      <h3 class=\"bbg-card__title\">Discover more with Bloomberg newsletters<\/h3>\n      \n              <div class=\"bbg-card__wysiwyg bb-wysiwyg\"><p>Subscribe now<\/p>\n<\/div>\n          <\/div>\n\n          \n<div\n  id=\"cta_1090262589002163737\"\n  class=\"wpb_content_element bbg-cta icon icon-arrow\">\n  <style>\n    \n    \n    \n  <\/style>\n  <div\n    class=\"bbg-cta-link link-holder\"\n    data-links-type=\"cta-links\">\n    <p class=\"bbg-cta-p right\">\n      <a\n        class=\"bbg-cta-link link interstitial_cta\"\n        href=\"https:\/\/www.bloomberg.com\/professional\/insights\/newsletter\/\"\n        target=\"_blank\"\n        rel=\"\"\n        data-section-name=\"\"\n                role=\"button\"\n        aria-label=\"Learn more\"\n        >\n                <\/a>\n    <\/p>\n  <\/div>\n<\/div>\n\n      <\/div>\n\n  <\/div>\n\n<\/div>\n\n<\/div>\n\n\n                    <\/div>\n    <\/section>\n<\/div>\n\n<div  class=\"bbg-row-container\">\n    <section class=\"bbg-row  text--black row-padding--top-none bbg-row--full-bg-bleed\" data-anchor='row-6a09249fa73eb'>\n        \n        \n        <div\n            class=\"bbg-row--content\"\n                    >\n            \n            <p><div\n    class=\"bbg-column bbg-column--width-2\"\n    style=\"\"\n    >\n    \n<\/div><div\n    class=\"bbg-column bbg-column--width-8\"\n    style=\"\"\n    >\n    <div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<h2>How much data are we talking about?<\/h2>\n<p>Financial market players need to monitor real-time economic conditions to understand how they impact the market environment, fund flows, and investment opportunities. Economic nowcasting, the process of predicting the very recent past, present, and very near-future state of the economy, can help with this. But how can we nowcast when key economic indicators are published with significant lags?<\/p>\n<p>One way is to use real-time financial market data as a gauge for assessing underlying economic activity. In a 2020 paper titled <a href=\"https:\/\/www.ecb.europa.eu\/pub\/pdf\/scpwps\/ecb.wp2494~7eb5392c0e.en.pdf\">&#8220;Nowcasting business cycle turning points with stock networks and machine learning,&#8221; <\/a>\u00a0the European Central Bank showed how this can be done.<\/p>\n<p>The paper highlights the importance of granularity and the impact of economy-wide return interconnections. For example, when consumer and business spending weaken in some parts of the economy, corporate earnings start to decline, prompting cost-cutting measures.<\/p>\n<p>The authors analyzed \u201cGranger causal\u201d relationships among S&amp;P 500 members to identify which firms influence each other\u2019s returns. They then trained multiple machine learning models using these findings to assess the current state of the US economy by predicting connections between US GDP growth and daily S&amp;P 500 member stock prices.<\/p>\n\n<\/div>\n<div id=\"\" class=\"wpb_content_element bbg-single-image align-center\">\n    <figure class=\"bbg-single-image__figure\" style=\"max-width:1024px\">\n                <img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"292\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcast.jpg\" class=\"bbg-single-image__image attachment-large\" alt=\"S&amp;P 500\" title=\"nowcast\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcast.jpg 800w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcast.jpg 552w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcast.jpg 768w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcast.jpg 1513w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/>\n        \n            <\/figure>\n<\/div>\n\n<div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<p>The pink shade in the above chart means the recession cycle or \u201cbad period.\u201d In this case, the 2007-2008 financial crisis.<\/p>\n<h2>Nowcasting in a BQuant workflow<\/h2>\n\n<\/div>\n<div id=\"\" class=\"wpb_content_element bbg-single-image align-center\">\n    <figure class=\"bbg-single-image__figure\" style=\"max-width:1024px\">\n                <img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"312\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting2.png\" class=\"bbg-single-image__image attachment-large\" alt=\"BQNT environment\" title=\"nowcasting2\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting2.png 800w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting2.png 552w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting2.png 768w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting2.png 1191w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/>\n        \n            <\/figure>\n<\/div>\n\n<div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<p>A typical BQuant workflow starts by querying data from BQL services to pandas dataframe, and then converts pandas dataframe into Spark dataframe for Spark speed-up calculation usage.<\/p>\n<p>In BQuant Enterprise, we have the (i) data, (ii) the compute resources (CPU\/ GPU\/ Memory), (iii) Jupyter Notebook as the IDE.<\/p>\n<p>To use stock market data to identify economic regime changes, we first pull quarterly GDP data and daily stock returns data for the past 40 years. This is a lot of data, but the BQuant Enterprise environment can easily handle it.<\/p>\n<p>Then, to efficiently process the \u201cGranger causal\u201d relationship calculation for each ticker pair at each time point, we can use a native Spark service, leveraging parallel processing. Once \u201cGranger causal\u201d relationship is computed, the results can then be saved in the dedicated S3 bucket provided to each BQuant Enterprise clients. These pre-calculated and cached results can then be called in future requests.<\/p>\n<p>We then perform a large set of \u201cGranger causal\u201d tests to the dataframe to examine the inner causalities, and save the resulting files into an AWS S3 bucket. Then visualizing everything into network graphs and apply machine learning models to do the final analysis.<\/p>\n\n<\/div>\n<div id=\"\" class=\"wpb_content_element bbg-single-image align-center\">\n    <figure class=\"bbg-single-image__figure\" style=\"max-width:1024px\">\n                <img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"528\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting3.png\" class=\"bbg-single-image__image attachment-large\" alt=\"Nowcasting\" title=\"nowcasting3\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting3.png 800w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting3.png 552w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting3.png 768w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting3.png 1536w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting3.png 1620w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/>\n        \n            <\/figure>\n<\/div>\n\n<div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<p>The above network graph depicts the \u201cGranger causal\u201d results as a ball connected to another ball via arrows connecting reason to the outcome. Because the dataset is large, the graph is quite dense. It will be much clearer visually if there are only two or three points.<\/p>\n<p>As the final step, three supervised learning models (Support Vector Machine, Logistic Regression, and Na\u00efve Bayes) are trained and used to create \u201cbinary (positive\/ negative)\u201d, and \u201c3-class (high-growth\/ low growth\/ recession) versions of the forecast.<\/p>\n<p><strong>Real time forecast, negative growth, three methods<\/strong><\/p>\n\n<\/div>\n<div id=\"\" class=\"wpb_content_element bbg-single-image align-center\">\n    <figure class=\"bbg-single-image__figure\" style=\"max-width:1024px\">\n                <img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"348\" src=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting4-1.png\" class=\"bbg-single-image__image attachment-large\" alt=\"Real-timeReal time forecast, negative growth, three methods\" title=\"nowcasting4-1\" srcset=\"https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting4-1.png 800w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting4-1.png 552w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting4-1.png 768w, https:\/\/assets.bbhub.io\/image\/v1\/resize?width=auto&amp;type=webp&amp;url=https:\/\/assets.bbhub.io\/professional\/sites\/41\/nowcasting4-1.png 1296w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/>\n        \n            <\/figure>\n<\/div>\n\n<div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<p>We use 3 different types of classifiers to output the YES\/NO for a negative growth prediction.<\/p>\n<p>It\u2019s like a voting system. If all of the models agree the economy will go well, we can have more confidence in our predictions. Here, in the pink recession seasons, lots of our results output negative growth. For the white areas, the models usually gave \u201cthere would not be negative growth,\u201d which means our models are relatively accurate.<\/p>\n<p>If you\u2019re interested in testing these results for yourself, please <a href=\"https:\/\/www.bloomberg.com\/professional\/products\/bloomberg-terminal\/research\/bquant\/#request-demo\">request a demo<\/a>.<\/p>\n\n<\/div>\n\n<\/div>\n\n\n                    <\/div>\n    <\/section>\n<\/div>\n\n<div  class=\"bbg-row-container\">\n    <style>section[data-anchor=row-6a09249fbb579]::before {\n\t\t\t\tbackground-color: #eeeeee;\n\t\t\t}<\/style>\n    <section class=\"bbg-row bg--custom-color  bg--eeeeee text--black bbg-row--full-bg-bleed\" data-anchor='row-6a09249fbb579'>\n        \n        \n        <div\n            class=\"bbg-row--content\"\n                    >\n            \n            <div\n    class=\"bbg-column\"\n    style=\"\"\n    >\n    <div\n\tclass=\"bb-wysiwyg\"\n\t\t>\n\t<h2>Recommended for you<\/h2>\n\n<\/div>\n<div\n   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