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1. Stock price prediction using sentiment Based LSTM: S&P500 vs Reddit posts

   Richard-Ojo, O; Wimmer, H. (April 2024, The Proceedings of IEMTRONICS 2024)

   Phillip Bradford, S. Andrew (Ed.)

   The stock market is as volatile as it is unpredictable, the unstable nature of the stock market results in fluctuations in stock prices and invariably, the market performance of stocks. Understanding the underlying factors that contribute to the volatility of the stock market, which has its consequences on stock prices, has become important to researchers and investors alike. Some of the methods that researchers have used in the past as a gauge for understanding market performance include analyzing economic conditions, understanding company performance, following geopolitical events and market trends. To contribute to the vast research field of stock price predictions and the challenge of understanding stock price fluctuations, this study will aim to find a relationship between human sentiments on the social media platform, Reddit, and the S&P 500 stock index. In this study, we will analyze posts from five subreddits that typically discuss the stock market and stock price fluctuations. This will form the first part of our dataset. Historical stock prices for the S&P 500 index will be obtained from Yahoo Finance. This will form our final dataset. Using VADER (Valence aware dictionary and sentiment reasoner), we will extract the sentiments within the five subreddits and categorize them into positive and negative sentiments. The historical stock prices from Yahoo finance will be matched with the aggregate sentiments for each day and this data passed through the LSTM model for training. Our findings provide strong evidence of social media’s impact on stock price predictions. 

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2. Quantifying dimensional change in stochastic portfolio theory

   https://doi.org/10.1111/mafi.12425  

   Bayraktar, Erhan; Kim, Donghan; Tilva, Abhishek (July 2024, Mathematical Finance)

   In this paper, we develop the theory of functional generation of portfolios in an equity market with changing dimension. By introducing dimensional jumps in the market, as well as jumps in stock capitalization between the dimensional jumps, we construct different types of self‐financing stock portfolios (additive, multiplicative, and rank‐based) in a very general setting. Our study explains how a dimensional change caused by a listing or delisting event of a stock, and unexpected shocks in the market, affect portfolio return. We also provide empirical analyses of some classical portfolios, quantifying the impact of dimensional change in portfolio performance relative to the market. 

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3. The Unprecedented Stock Market Reaction to COVID-19

   https://doi.org/10.1093/rapstu/raaa008  

   Baker, Scott R; Bloom, Nicholas; Davis, Steven J; Kost, Kyle; Sammon, Marco; Viratyosin, Tasaneeya (July 2020, The Review of Asset Pricing Studies)

   Pontiff, Jeffrey (Ed.)

   Abstract No previous infectious disease outbreak, including the Spanish Flu, has affected the stock market as forcefully as the COVID-19 pandemic. In fact, previous pandemics left only mild traces on the U.S. stock market. We use text-based methods to develop these points with respect to large daily stock market moves back to 1900 and with respect to overall stock market volatility back to 1985. We also evaluate potential explanations for the unprecedented stock market reaction to the COVID-19 pandemic. The evidence we amass suggests that government restrictions on commercial activity and voluntary social distancing, operating with powerful effects in a service-oriented economy, are the main reasons the U.S. stock market reacted so much more forcefully to COVID-19 than to previous pandemics in 1918–1919, 1957–1958, and 1968. 

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4. Retrieving Top-k Hyperedge Triplets: Models and Applications

   https://doi.org/10.1109/BigData62323.2024.10825860  

   Niu, Jason; Amburg, Ilya D; Aksoy, Sinan G; Sarıyüce, Ahmet Erdem (December 2024, Proceedings)

   Complex systems frequently exhibit multi-way, rather than pairwise, interactions. These group interactions cannot be faithfully modeled as collections of pairwise interactions using graphs and instead require hypergraphs. However, methods that analyze hypergraphs directly, rather than via lossy graph reductions, remain limited. Hypergraph motifs hold promise in this regard, as motif patterns serve as building blocks for larger group interactions which are inexpressible by graphs. Recent work has focused on categorizing and counting hypergraph motifs based on the existence of nodes in hyperedge intersection regions. Here, we argue that the relative sizes of hyperedge intersections within motifs contain varied and valuable information. We propose a suite of efficient algorithms for finding top-k triplets of hyperedges based on optimizing the sizes of these intersection patterns. This formulation uncovers interesting local patterns of interaction, finding hyperedge triplets that either (1) are the least similar with each other, (2) have the highest pairwise but not groupwise correlation, or (3) are the most similar with each other. We formalize this as a combinatorial optimization problem and design efficient algorithms based on filtering hyperedges. Our comprehensive experimental evaluation shows that the resulting hyperedge triplets yield insightful information on real-world hypergraphs. Our approach is also orders of magnitude faster than a naive baseline implementation. 

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5. Stock market bubbles and the forecastability of gold returns and volatility

   https://doi.org/10.1002/asmb.2887  

   Gabauer, David; Gupta, Rangan; Karmakar, Sayar; Nielsen, Joshua (August 2024, Applied Stochastic Models in Business and Industry)

   Abstract In this article, multi‐scale LPPLS confidence indicator approach is used to detect both positive and negative bubbles at short‐, medium‐, and long‐term horizons for the stock markets of the G7 and the BRICS countries. This enables detecting major crashes and rallies in the 12 stock markets over the period of the 1st week of January, 1973 to the 2nd week of September, 2020. Similar timing of strong (positive and negative) LPPLS indicator values across both G7 and BRICS countries was also observed, suggesting interconnectedness of the extreme movements in these stock markets. Next, these indicators were utilized to forecast gold returns and its volatility, using a method involving block means of residuals obtained from the popular LASSO routine, given that the number of covariates ranged between 42 and 72, and gold returns demonstrated a heavy upper tail. The finding was, these bubbles indicators, particularly when both positive and negative bubbles are considered simultaneously, can accurately forecast gold returns at short‐ to medium‐term, and also time‐varying estimates of gold returns volatility to a lesser extent. The results of this paper have important implications for the portfolio decisions of investors who seek a safe haven during boom‐bust cycles of major global stock markets. 

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