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lalit-ponnala

Lalit Ponnala

Director, Global Research & Design, S&P Dow Jones Indices

Lalit Ponnala is Director, Global Research & Design at S&P Dow Jones Indices (S&P DJI). In this role, he is responsible for the conceptualization, research, and design of factor-based indices and strategic solutions across multiple asset classes.
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Jan 13, 2022

S&P 500 Twitter Sentiment Indices: Factor Analysis

We previously introduced the S&P 500 Twitter Sentiment Indices and analyzed their performance characteristics. We noted how the indices seek to capture the Tweet-based sentiment premium, with their differentiated performance being driven by differences in index construction and sector weighting. In this blog, we will examine their exposure to standard risk factors and analyze their…

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Jan 10, 2022

S&P 500 Twitter Sentiment Indices: Performance Characteristics

In a previous blog, we introduced the S&P 500® Twitter Sentiment Index and the S&P 500 Twitter Sentiment Select Equal Weight (EW) Index, highlighting their objective and construction. At the core of these indices is a model-based sentiment score that captures the direction and magnitude of the sentiment associated with a given company. S&P DJI…

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Sep 9, 2021

Introducing the S&P Multi-Asset Dynamic Inflation Strategy Index – Part 2

In a previous blog, we highlighted the objective of the S&P Multi-Asset Dynamic Inflation Strategy Index and summarized its methodology. We justified our strategy choice for each inflation regime (high, medium, low) and illustrated how the index dynamically switches between these strategies based on a monthly inflation forecast. In this blog, we will take a…

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May 13, 2020

Comparing Bottom-Up versus Top-Down Multi-Factor Construction

The S&P 500® Quality, Value & Momentum Multi-Factor Index is designed to measure the performance of stocks having the highest combination of quality, value, and momentum (QVM). It takes a “bottom-up” approach of scoring each stock on its individual factor attributes and selecting stocks that jointly score highest across all the factors [1]. In light…

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